(04:11) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Are you managing your team the same way you did five years ago? With AI agents now part of the workforce, the old playbook no longer applies. In this episode, Jurgen Appelo, author of “Human Robot Agent” and creator of Management 3.0 and unFIX, challenges conventional thinking about management, organizational design, and the future of work in the AI era. He explains why rigid frameworks like Scrum are becoming bottlenecks to AI speed and why he believes we need to completely rethink how organizations operate. The conversation dives into the concept of creating “fast tracks” for AI agents while maintaining “slow tracks” for human collaboration. Jurgen also breaks down why team sizes are shrinking and why professionals must move beyond T-shaped skills to become M-shaped, multidisciplinary workers to remain relevant. He also shares his controversial take on why Scrum is “done” and why he trusts AI more than the average human when solving complex problems. Key topics discussed: Managing systems vs people in hybrid human-AI teams Why patterns beat frameworks for organization design Why Scrum is done: adapting Agile for the AI era M-shaped workers: the new multidisciplinary skill Fast and slow tracks: redesigning work for AI Why AI outperforms average humans at complex problems Critical thinking as the essential leadership skill The new optimal team size and dynamic reteaming Timestamps: (00:00:00) Trailer & Intro (00:02:20) Career Turning Points: Seven-Year Career Pivots (00:05:29) Origins of Management 3.0 (00:08:31) Managing Systems, Not People (00:12:35) Everlasting Management Principles (00:17:21) unFIX: Patterns Over Frameworks (00:24:27) Core unFIX Patterns (00:31:39) Pipedrive Case Study: unFIX in Action (00:38:16) M3K: Merging Management 3.0 and unFIX (00:41:33) Skeptical Enthusiast: Balanced AI Perspective (00:47:18) Co-Creating with Humans and Machines (00:51:51) From T-Shaped to M-Shaped Workers (00:56:38) Why I Trust AI More Than Humans (01:00:19) Scrum is Done (Not Dead) (01:05:50) Redesigning Organizations for AI: Fast and Slow Tracks (01:09:25) 3 Tech Lead Wisdom _____ Jurgen Appelo’s Bio Jurgen Appelo is an author, speaker, and entrepreneur who helps leaders rewire their organizations for AI-driven leadership and autonomous digital agents. Recognized by Inc.com as a Top 50 Leadership Expert and Top 100 Leadership Speaker, he bridges opposing worldviews: human ingenuity and AI, leadership versus governance, stability with innovation, and individual growth fueling collective success. As founder of The unFIX Company (and previously founder of Management 3.0 and co-founder of Agile Lean Europe), Jurgen pioneers the future of work through stories, games, tools, and practices that challenge conventional thinking. Follow Jurgen: LinkedIn – linkedin.com/in/jurgenappelo Website – jurgenappelo.com Substack – substack.jurgenappelo.com  Human Robot Agent – https://jurgenappelo.com/pages/human-robot-agent Like this episode? Show notes & transcript: techleadjournal.dev/episodes/242. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(04:00) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at ⁠getunleash.io/pricing⁠. Why do so many software projects still fail despite modern tools? The answer often lies in the psychology of the team, not the technology stack. Software development is often viewed purely as a technical challenge, yet many projects fail due to human factors and cognitive bottlenecks. In this episode, Adam Tornhill, CTO and Founder of CodeScene, shares his unique journey combining software engineering with psychology to solve these persistent industry problems. He explains the concept of “Your Code as a Crime Scene,” a method for using behavioral analysis to identify high-risk areas in a codebase that static analysis tools often miss. Adam covers the tangible business impact of code health, specifically how it drives predictability and development speed. He explains why 1-2% of our codebase accounts for up to 70% of our development work, and how focusing on these hotspots can make our team 2x faster and 10x more predictable. Adam also provides a critical reality check on the rise of AI in coding, exploring whether it will help reduce technical debt or accelerate it, and offers strategies for maintaining quality in an AI-assisted future. Key topics discussed: Combining psychology and software engineering Why predictability matters more than speed Treating your codebase as a crime scene Behavioral analysis vs. static analysis The hidden danger of the “Bus Factor” Will AI help or hurt code quality? Why healthy code helps both humans and AI Essential guardrails for AI-generated code Timestamps: (00:00) Trailer & Intro (01:29) Career Turning Point: From Developer to Psychologist (02:36) Combining Psychology and Software Engineering (04:00) Why Engineering Leaders Need Psychology Knowledge (05:46) The Root Cause of Failing Software Projects (07:43) Why Code Abstractness Makes Quality Hard to Measure (09:29) Aligning Code Quality with Business Outcomes (11:37) Code Health: 2x Speed, 10x Predictability (12:58) Why Predictability is Undervalued in Software (19:53) Introducing “Your Code as a Crime Scene” (21:57) Behavioral Code Analysis: Hotspot Analysis vs Static Code Analysis (24:06) Behavioral Code Analysis: Understanding Change Coupling (26:30) Dealing with God Classes (29:40) Behavioral Code Analysis: The Social Side of Code (31:33) Why Developers Aren’t Interchangeable (33:14) Introduction to CodeScene (36:48) Will AI Help or Hurt Code Quality? (39:14) Essential Guardrails for AI-Generated Code (42:06) Using CodeScene to Maintain Quality in the AI Era (43:06) How AI Accelerates Technical Debt at Scale (45:54) Why AI-Friendly Code is Human-Friendly Code (48:32) Documentation: Capturing the “Why” for Humans and AI (50:42) The Reality Check: Future of Software Development with AI (52:41) 3 Tech Lead Wisdom _____ Adam Tornhill’s Bio Adam Tornhill is the founder and CTO of CodeScene and the best-selling author of Your Code as a Crime Scene. Combining degrees in engineering and psychology, Adam helps companies optimize software quality using AI-driven methodologies. He is an international keynote speaker and researcher who enjoys retro computing and martial arts in his spare time. Follow Adam: LinkedIn – linkedin.com/in/adam-tornhill-71759b48 CodeScene – codescene.com  Your Code as a Crime Scene – pragprog.com/titles/atcrime2/your-code-as-a-crime-scene-second-edition Like this episode? Show notes & transcript: techleadjournal.dev/episodes/241. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(06:03) Brought to you by Unleash Unleash is a private, flexible, and scalable feature flag system that lets teams decouple deployments from releases. It reduces the risk of shipping new features and gives organizations real-time control over what reaches production. And as AI accelerates development, Unleash helps engineering teams move fast and stay stable with safe rollouts and instant kill switches. Start a free trial of Unleash at getunleash.io/pricing. Are you making critical decisions without consulting AI? Greg argues it’s now irresponsible for any leader to make high-stakes decisions without talking to AI first. In this episode, Greg Shove, CEO of Section and a multi-time founder with 30 years of entrepreneurial experience, shares how AI is fundamentally different from any previous technology wave. Unlike traditional software that makes us more productive within our existing boundaries, AI allows us to jump capability boundaries – enabling individuals and organizations to do things they simply couldn’t do before. Greg explains why most enterprise AI rollouts are failing (hint: they’re treating AI like software when it’s actually co-intelligence), how to cultivate resilience through multiple startup failures, and the practical strategies for getting teams to adopt AI (from simple hacks like putting a post-it note on your monitor to creating an entire AI-dedicated screen). This conversation goes beyond the hype to explore both the superpowers and limitations of AI, the real organizational outcomes you can expect (spoiler: it’s not just about layoffs), and why moving from efficiency to creation is the key to unlocking AI’s true potential in your organization. Key topics discussed: Why AI breaks capability boundaries unlike any other tech Treating AI as a thought partner, not just a productivity tool Why most large organizations fail at AI deployment Managing workforce anxiety during AI transformation The four possible team outcomes when rolling out AI Moving from efficiency (cut) to growth (create) with AI The Post-it note hack that changed how teams use AI daily Walking the walk: leading authentically in AI adoption Timestamps: (00:00:00) Trailer & Intro (00:02:44) Career Turning Points (00:06:03) Cultivating Entrepreneurial Resilience (00:07:49) Understanding the AI Wave: Scale and Transformation (00:12:29) Pivoting to AI: Section’s Transformation Journey (00:17:57) AI as a Thought Partner (00:22:57) Practical Tips for Leaders Using AI Daily (00:30:49) Rolling Out AI Organization-Wide: Managing Change and Anxiety (00:41:30) AI ROI: Beyond Efficiency to Creation (00:51:01) AI-Powered Education: The ProfAI Approach (00:57:53) 1 Tech Lead Wisdom _____ Greg Shove’s Bio Greg Shove is a seven-time CEO, all in on AI. After first using ChatGPT in February 2023, he pivoted his company Section to be AI-powered. Now he helps enterprise organizations move from AI-anxious to AI-proficient with a proven playbook, delivered through keynote speaking and executive workshops. Greg is also the founder of Machine & Partners, an AI lab building custom enterprise AI applications, and co-author of Personal Math, a weekly newsletter sharing business insights for early-career leaders and founders. Follow Greg: LinkedIn – linkedin.com/in/gregshove Newsletter – personalmath.substack.com Section AI – sectionai.com Prof AI – prof.ai Like this episode? Show notes & transcript: techleadjournal.dev/episodes/240. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

(06:06) Brought to you by Jellyfish AI tools alone won’t transform your engineering org. Jellyfish provides insights into AI tool adoption, cost, and delivery impact – so you can make better investment decisions and build teams that use AI effectively. See for yourself at jellyfish.co/platform/ai-impact. Why do organizations constantly complain about having too much technical debt? Because they’re solving the wrong problem. In this episode, Dr. Andrew Brown, author of “Taming Your Dragon: Addressing Your Technical Debt,” reveals a profound insight: technical debt isn’t fundamentally a technical problem. It’s a trade-off problem rooted in human bias, organizational systems, and economic incentives. Through his innovative “Technical Debt Onion Model,” Andrew shows how decisions about code quality happen across five interconnected layers, from individual cognitive biases to wicked problem dynamics. Andrew explains why the financial debt analogy is dangerously misleading and, more importantly, how others can rack up debt you’ll eventually pay for. Drawing from behavioral economics, systems thinking, and organizational theory, he reveals why our emotions, not logic, drive most technical decisions, and how to work with this reality rather than against it. Key topics discussed: Why technical debt is a trade-off problem, not technical How emotions override logic in critical decisions The Technical Debt Onion Model framework explained Principal-agent problems sabotaging your codebase Externalities: who pays for shortcuts taken today? Why burning down debt is already too late Ulysses contracts for managing future obligations Systems thinking applied to software development Wicked problems: why different teams see different solutions AI’s impact on technical debt creation Timestamps: (00:00:00) Trailer & Intro (00:02:24) Career Turning Points (00:06:06) The Importance of Skilling Up in Tech (00:06:49) The Definition of Technical Debt (00:09:08) The Broken Analogy of Technical Debt as a Financial Debt (00:09:58) The Role of Human Bias and Organization Issues in Technical Debt (00:12:41) Tech Debt is a Trade-off Problem (00:13:07) Building a Healthier Relationship with Technical Debt (00:15:15) The Technical Debt Onion Model (00:18:17) The Onion Model: Trade-Off Layer (00:25:10) The Ulysses Contract for Managing Technical Debt (00:33:03) The Onion Model: Systems Layer (00:36:32) The Onion Model: Economics/Game-Theory Layer (00:41:50) The Onion Model: Wicked Problem Layer (00:48:10) How Organizations Can Start Managing Technical Debt Better (00:52:03) The Al Impact on Technical Debt (00:56:16) 3 Tech Lead Wisdom _____ Andrew Brown’s Bio Andrew Richard Brown has worked in software since 1999, starting as an SAP programmer fixing Y2K bugs. He realized the biggest problems in software development were human, not technical, and has since helped teams improve performance by addressing these issues. Andrew coaches organizations on software development and quality engineering, focusing on technical debt, risk in complex systems, and project underestimation. He investigates how cognitive biases drive software problems and applies behavioral science techniques to solve them. His research has produced counterintuitive insights and fresh approaches. He regularly speaks at international conferences and runs a growing YouTube channel on these topics. Follow Andrew: LinkedIn – linkedin.com/in/andrew-brown-4b38062 YouTube – @behaviouralsoftwareclub705 Email – brownsensei@hotmail.com  Taming Your Dragon – https://www.amazon.com/Taming-Your-Dragon-Addressing-Technical/dp/B0CV4TTP32/ Like this episode? Show notes & transcript: techleadjournal.dev/episodes/239. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

Why does an AI that brilliantly generates code suddenly fail at basic math? The answer explains why your LLM will fail when you least expect it. In this episode, Emmanuel Maggiori, author of “Smart Until It’s Dumb” and “The AI Pocket Book,” cuts through the AI hype to reveal what LLMs actually do and, more importantly, what they can’t. Drawing from his experience building AI systems and witnessing multiple AI booms and busts, Emmanuel explains why machine learning works brilliantly until it makes mistakes no human would ever make. He shares why businesses repeatedly fail at AI adoption, how hallucinations are baked into the technology, and what developers need to know about building reliable AI products. Whether you’re implementing AI at work or concerned about your career, this conversation offers a grounded perspective on navigating the current AI wave without getting swept away by unrealistic promises. Key topics discussed: Why AI projects fail the same way repeatedly How LLMs work and why they brilliantly fail Why hallucinations can’t be fixed with better prompts Why self-driving cars still need human operators Adopting AI without falling into hype traps How engineers stay relevant in the AI era Why AGI predictions are mostly marketing Building valuable products in boring industries Timestamps: (00:00:00) Trailer & Intro (00:02:32) Career Turning Points (00:06:41) Writing “Smart Until It’s Dumb” and “The AI Pocket Book” (00:08:14) The History of AI Booms & Winters (00:11:34) Why Generative AI Hype is Different Than the Past AI Waves (00:13:26) AI is Smart Until It’s Dumb (00:16:45) How LLM and Generative AI Actually Work (00:22:53) What Makes LLMs Smart (00:27:25) Foundational Model (00:30:01) RAG and Agentic AI (00:34:09) Tips on How to Adopt AI Within Companies (00:37:56) How to Reduce & Avoid AI Hallucination Problem (00:45:49) The Important Role of Benchmarks When Building AI Products (00:50:57) Advice for Software Engineers to Deal With AI Concerns (00:56:49) Advice for Junior Developers (00:59:34) Vibe Coders and Prompt Engineers: New Jobs or Just Hype? (01:01:55) The AGI Possibility (01:07:23) Three Tech Lead Wisdom _____ Emmanuel Maggiori’s Bio Emmanuel Maggiori, PhD, is a software engineer and 10-year AI industry insider. He has developed AI for a variety of applications, from processing satellite images to packaging deals for holiday travelers. He is the author of the books Smart Until It’s Dumb, Siliconned, and The AI Pocket Book. Follow Emmanuel: LinkedIn – linkedin.com/in/emaggiori Website – emaggiori.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/238. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

In this episode, Honey Mittal, CEO and co-founder of Locofy.ai, explores one of the most exciting transformations in software development: the convergence of design and engineering through AI-powered automation. Honey shares the fascinating journey of building Locofy, a tool that converts Figma designs into production-ready front-end code. But this isn’t just another AI hype story. It’s a deep dive into why Large Language Models (LLMs) fundamentally can’t solve design-to-code problems, and why his team spent four years building specialized “Large Design Models” from scratch. Key topics discussed: Why 60-70% of engineering time goes to front-end UI code (and how to automate it) The technical limitations of LLMs for visual design understanding How proper design structure is the key to successful code generation The emergence of “design engineers” who bridge design and development Lessons from pivoting from consumer to enterprise SaaS Building global developer tools from Southeast Asia The real challenges of building deep tech startups in Southeast Asia Career advice for staying relevant in the AI era Whether you’re a front-end engineer tired of translating design pixel-by-pixel, a designer curious about coding, or a technical leader evaluating AI development tools, this episode offers practical insights into the future of software development. Timestamps: (00:00:00) Trailer & Intro (00:02:13) Career Turning Points (00:05:28) Transition from Developers to Product Management (00:09:53) The Key Product Lessons from Working at Major Startups (00:14:12) Learnings from Locofy Product Pivot Journey (00:19:36) An Introduction to Locofy (00:22:40) The Story Behind The “Locofy” Name (00:23:27) How Locofy Generates Pixel Perfect & Accurate Codex (00:28:01) Why Locofy Pivoted to Focus on Enterprises (00:29:39) The Locofy’s Code Generation Process (00:32:13) Why Locofy Built Its Own Large Design Model (00:39:25) Locofy Integration with Existing Development Tools (00:42:44) LLM Strengths and Weaknesses (00:48:47) Other Challenges Building Locofy (00:50:59) The Future of Design & Engineering (00:58:35) The Future of AI-Assisted Development Tools (01:02:53) There is No AI Moat (01:04:37) The Potential of SEA Talents Solving Global Problems (01:08:14) The Challenges of Building Dev Tools in SEA (01:10:39) The Challenges of Being a Fully Remote Company in SEA (01:14:36) Locofy Traction and ARR (01:18:09) 3 Tech Lead Wisdom _____ Honey Mittal’s Bio Honey Mittal is the CEO and co-founder of Locofy.ai, a platform that automates front-end development by converting designs into production-ready code. Originally an engineer who built some of the first mobile apps in Singapore, Honey transitioned into product leadership after realizing his natural strength lay in identifying high-impact problems. He set a goal to become a CPO by 30 and achieved it, leading product transformations at major Southeast Asian scale-ups like Wego, FinAccel, and Homage. Driven by a decade of experience and the “grunt work” he and his co-founder faced, he started Locofy to solve the costly friction between design and engineering. Honey is passionate about the future of AI in development, the rise of the “Design Engineer”, and proving that globally competitive, deep-tech companies can be built from Southeast Asia. Follow Honey: LinkedIn – linkedin.com/in/honeymittal Twitter – x.com/HoneyMittal07 Website – locofy.ai Like this episode? Show notes & transcript: techleadjournal.dev/episodes/236. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

Can you navigate AI disruption without understanding your landscape? Discover how to gain true situational awareness. The rise of AI has exposed a fundamental problem in how organizations make decisions. Most leaders operate using stories and graphs, not actual maps of their landscape. This leaves them vulnerable to disruption and unable to make informed choices about where to apply new technologies. The result is chaos, waste, and strategic mistakes that could have been avoided. In this episode, Simon Wardley, creator of Wardley Mapping, explains how to build true situational awareness in your organization. He shares why most business “maps” aren’t really maps at all, how to understand the landscape before making decisions, and what leaders need to know about AI adoption beyond the current hype. Key topics discussed: Why leading with stories instead of maps creates fake CEOs The critical difference between graphs and maps in business strategy What Wardley mapping is and the three pattern types leaders must understand How to identify where human decision-making adds value in your AI adoption Why vibe coding is powerful but dangerous without proper code reviews Why software development is still a craft, not engineering How Jevons Paradox means AI won’t eliminate jobs but expand codebases The hidden dangers of AI hallucinations and the need for critical thinking Timestamps: (00:00:00) Trailer & Intro (00:02:59) Career Turning Points (00:06:45) Importance of Understanding Landscape for Leaders (00:10:42) The Problem of Leading with Stories (00:12:49) Wardley Maps vs Other Types of Business Maps/Analysis (00:17:32) Wardley Map Overview (00:23:54) Why Mapping is Not a Common Industry Practice (00:26:23) Climatic Patterns, Doctrines, and Gameplay (00:30:51) Understanding Disruption by Using a Map (00:33:17) Navigating the Recent AI Disruption (00:39:37) A Leader’s Guide to Adopting AI (00:42:49) Turning Coding From a Craft Into Engineering (00:48:05) Simon’s AI & Vibe Coding Experiments (00:55:28) The Importance of Critical Thinking for Software Engineers (01:03:49) Navigating Career Anxiety Due to AI Fear (01:08:56) Tech Lead Wisdom _____ Simon Wardley’s Bio Simon Wardley is a researcher, former CEO, and the creator of Wardley Mapping, a powerful method for visualizing and developing business strategy. His journey began accidentally after a bookseller recommended Sun Tzu’s The Art of War, which sparked a fascination with understanding the competitive “landscape.” As the former CEO of an online photo service acquired by Canon, he felt like a “fake CEO,” leading with stories while lacking true situational awareness. This led him to discover that almost all business “maps” were merely graphs, prompting him to develop his own mapping technique. Today, his work is used by organizations like NASA and taught at multiple MBA programs, helping leaders to “look before they leap” and navigate complex technological and market shifts, including the current disruption caused by AI. Follow Simon: LinkedIn – linkedin.com/in/simonwardley Twitter – x.com/swardley Website – www.swardleymaps.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/235. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

How much of your code exists only to prevent failures? Discover a new paradigm for building reliable applications. In this episode, Preeti Somal, SVP at Temporal, explores a paradigm shift that can dramatically boost productivity and give developers peace of mind. Drawing on her experience leading massive infrastructure at Yahoo and HashiCorp, she explains Temporal’s concept of durable execution that helps developers focus on business logic and remove reliability concerns. Preeti also discusses key findings from Temporal’s first State of Development Report. In this episode, you will learn about: Lessons from operating large-scale systems at Yahoo and HashiCorp Why reliability ranks higher than cost for most engineering teams How durable execution removes reliability complexity from developer concerns Why unlearning old patterns proves harder than learning Temporal’s model Creating a strong incident response culture through blameless post-mortem Nurturing psychological safety in infrastructure teams and on-call engineers Building security and compliance from day one versus retrofitting later Timestamps: (00:00) Trailer & Intro (02:20) Career Turning Points (04:43) Key Learnings from Operating Large Scale Infrastructure (07:56) Key Learnings on Platform Engineering (09:59) Key Learnings on Maintaining High Reliability (12:02) Key Highlights Working at HashiCorp (13:52) Running Infra as Code using Temporal (15:28) Key Principles for Managing a Strong Incident Response (18:37) The Importance of Nurturing Psychological Safety within Infra Team (21:13) The Temporal’s State of Development Report (22:39) The State of AI Usage & Adoption (23:54) Using Temporal for Building AI Applications (26:06) The Complexities Involved in Building AI Applications (28:51) Key Learnings from Temporal’s State of Development Report (31:03) The Choice of Developer Tooling Misalignment (33:12) Integrating Security, Compliance, and Cost into Your Engineering Mindset (33:39) Building with Security and Compliance-First Mindset (36:57) Temporal Paradigm Shift (39:14) How Temporal Hides Away The Complexities of Building Reliable Applications (42:47) Unlearning Required for Using Temporal Programming Model (46:33) Getting Started Building with Temporal (48:34) Temporal’s Durable Execution Guarantee (51:23) The Concern About Temporal Lock-In (54:09) Temporal’s Strong Developer Focus (56:16) The Compliance and Security Aspect of Temporal Cloud (58:41) 3 Tech Lead Wisdom _____ Preeti Somal’s Bio Preeti is Senior Vice President of Engineering at Temporal. Preeti is passionate about building great products, growing world class organizations and solving complex problems. Prior to Temporal, Preeti led the Platform, Security and IT engineering organizations at HashiCorp. Her extensive career includes engineering leadership roles at Yahoo!, VMware and Oracle. While at Yahoo! Preeti was VP of Cloud Services in the Platform organization delivering highly scalable services used by engineers across Yahoo to build and operate applications with improved agility, reliability and security. These services power Yahoo!’s consumer and advertising business. Follow Preeti: LinkedIn – linkedin.com/in/preeti-somal-131890 Twitter – x.com/psomal 📖 Temporal’s State of Development Report 2025 – temporal.io/pages/state-of-development-2025 Like this episode? Show notes & transcript: techleadjournal.dev/episodes/234. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

“Engineering leaders are stuck between the expectations put out by sensational headlines and the reality of what they’re seeing in their organization. There’s a big disappointment gap.” Is your AI investment paying off? Many leaders struggle to see real ROI beyond the hype. In this episode, Laura Tacho, CTO of DX, shares DX’s new research on measuring AI adoption success across 38,000+ engineers. Our conversation reveals why acceptance rates are misleading metrics and introduces DX’s new AI Measurement Framework™ with its three critical dimensions: utilization, impact, and cost. Learn why treating AI as an organizational problem closes the “disappointment gap” between hype and reality. Note: This episode was recorded in July 2025. The AI adoption rate mentioned has since risen to nearly 80%. In this episode, you will learn about: The “Disappointment Gap” between AI hype and reality Why the popular “acceptance rate” metric is misleading The DX AI Measurement Framework™ and its three dimensions The top time-saving AI use case (it’s not code generation!) How AI impacts long-term software quality and maintainability Why organizational readiness matters for successful AI adoption The bigger bottlenecks beyond coding that AI has not yet solved Treating AI agents as team extensions, not digital employees Timestamps: (00:00:00) Trailer & Intro (00:02:32) Latest DX Research on AI Adoption (00:03:54) AI Role on Developer Experience (00:05:43) The Current AI Adoption Rate in the Industry (00:09:27) The Leader’s Challenges Against Al Hype (00:13:22) Measuring AI Adoption ROI Using Acceptance Rate (00:17:39) The DX AI Measurement Framework™ (00:23:05) AI Measurement Framework: Utility Dimension (00:27:51) DX AI Code Metrics (00:30:31) AI Measurement Framework: Impact Dimension (00:32:57) The Importance of Measuring Productivity Holistically (00:35:54) AI Measurement Framework: Cost Dimension (00:38:34) AI Second Order Impact on Software Quality and Maintainability (00:42:38) The Danger of Vibe Coding (00:46:31) Treating AI as Extensions of Teams (00:52:31) The Bigger Bottlenecks to Solve Outside of AI Adoption (00:55:47) DX Guide to AI-Assisted Engineering (01:00:38) Being Deliberate for a Successful AI Rollout (01:02:32) 3 Tech Lead Wisdom _____ Laura Tacho’s Bio Laura Tacho is CTO at DX, a developer intelligence platform, co-author of the Core 4 developer productivity metrics framework, and an executive coach. She’s an experienced technology leader and engineering leadership coach with a strong background in developer tools and distributed systems. Her career includes leadership roles at organizations such as CloudBees, Aula Education, and Nova Credit, where she specialized in building high-performing engineering teams and delivering impactful products. Laura has worked with thousands of engineering leaders as they work to improve their engineering practices with data. Follow Laura: LinkedIn – linkedin.com/in/lauratacho Twitter – x.com/rhein_wein Website – lauratacho.com  AI Measurement Framework – getdx.com/whitepaper/ai-measurement-framework/?utm_source=techleadjournal  Guide to AI-Assisted Engineering – getdx.com/guide/ai-assisted-engineering/?utm_source=techleadjournal AI code metrics – getdx.com/ai-code-metrics Like this episode? Show notes & transcript: techleadjournal.dev/episodes/233. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.

“Architecture is something that has to emerge naturally from the code. If it doesn’t make the code better, more elegant, and more flexible, then you should not be doing it.” Why do so many developers have a love-hate relationship with ORM? The creator of Hibernate reveals the real reasons behind the controversy and what’s being done to fix the fundamental issues. In this episode, Gavin King, the creator of Hibernate, shares the story behind its creation, from a debate with his boss to its rise as a popular open-source. He dives deep into why developers often dislike ORM, pinpointing the “magic” of the stateful persistence context as a major pain point. Gavin explains how modern specifications are fixing these historical issues with an emphasis on type safety and more explicit, stateless operations, giving developers greater control. Key topics discussed: The origin story of Hibernate and the early frustrations with Java EE The single biggest mistake that led some developers to hate ORM Why type safety matters and how the new Jakarta specifications enable type-safe queries Why architecture should emerge from code, not from whiteboard diagrams A critique on industry dogmas and architecture best practices, including DDD aggregates Why disagreement is essential for healthy engineering teams Timestamps: (00:00:00) Trailer & Intro (00:02:24) Career Turning Points (00:16:11) The Problems That Led to Hibernate Creation (00:24:22) Key Things That Make Hibernate Successful (00:31:57) Behind the Scene of Java EE Specifications (00:37:42) The Renaming of Java EE to Jakarta EE (00:40:15) Jakarta Persistence, Jakarta Data, Jakarta Query Language (00:47:20) The Importance of Type Safety (00:54:08) Why Some People Dislike ORM (01:00:47) The Fundamental of Data Fetching and Association (01:08:52) The Upcoming Jakarta Data and QL Updates (01:16:06) Gavin’s View on Software Architecture (01:26:08) The DDD from Gavin’s Perspective (01:30:55) Tech Lead Wisdom _____ Gavin King’s Bio Gavin King is the creator of Hibernate, the revolutionary framework that redefined data persistence for millions of Java developers. A key figure in the evolution of enterprise Java, he has led the development of major industry standards like the Java Persistence API (JPA) and CDI. After a decade designing the Ceylon programming language, he has returned to his roots to advance the next generation of data persistence with Jakarta EE. Follow Gavin: LinkedIn – linkedin.com/in/gavinking Twitter – x.com/1ovthafew Website – hibernate.org Like this episode? Show notes & transcript: techleadjournal.dev/episodes/232. Follow @techleadjournal on LinkedIn, Twitter, and Instagram. Buy me a coffee or become a patron.