Tech Lead Journal

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Great technical leadership requires more than just great coding skills. It requires a variety of other skills that are not well-defined, and they are not something that we can fully learn in any school or book. Hear from experienced technical leaders sharing their journey and philosophy for building great technical teams and achieving technical excellence. Find out what makes them great and how to apply those lessons to your work and team.

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October 26, 2026Episode 27152 min

How to Build a Successful Product: Stop Chasing Pain Points, Start Finding PULL - Rob Snyder

Why do startups with great customer research, design partners and real traction still fail to sell? Rob did everything the playbook says, then spent two years unable to close a sale. He explains what the playbook gets wrong. In this episode, Rob Snyder, author of “The Power of Pull: What You Need to Know About Customer Demand to Build a Successful Startup”, shares what he learned after his own startup failures and a sudden jump to $4M ARR on a different product. He explains why interviews, pain points, and design partners fail to predict buying, and introduces the PULL framework (Project, Unavoidable, List, Limitations) for spotting real demand. Rob describes why founders should set their product aside, observe where buyers are stuck, and treat sales as a research method rather than a persuasion exercise. We also discuss how to recognize the “pain cave”, why free pilots usually signal weak demand, and how to find “hell yes” customers one at a time. Finally, Rob covers how AI and vibe coding change the picture, and how the success story factory and the theory of constraints help you repeat early wins. Key topics discussed: Why customer interviews and design partners miss real demand The PULL framework: Project, Unavoidable, List, Limitations Why sales is the best research method for early-stage founders Spotting the pain cave: enthusiasm without anyone buying Why free pilots signal weak demand, even in enterprise Protecting your product from DIY vibe coding Why building a product for yourself can be an ego project The success story factory and the theory of constraints Timestamps: (00:00) Trailer & Intro (02:42) Why Did Rob Write “The Power of Pull” After His Startup Failures? (04:33) Why Do Customer Interviews and Design Partners Fail to Predict Buying? (06:42) Is “Jobs to Be Done” and the Faster Horse Quote Wrong About Demand? (09:37) What Is the PULL Framework (Project, Unavoidable, List, Limitations)? (11:44) Why Must Founders Forget Their Product to Find PULL? (14:12) How Do You Find Where Buyers Are Stuck: Sales and Go to the Gemba? (16:48) Can You Find Demand for Visionary Products Like SpaceX? (19:47) How Is Demand Different From Wanting a Product? (22:42) Why Should You Start With One “Hell Yes” Customer, Not a Persona? (26:25) How Do You Know If You Are Stuck in the “Pain Cave”? (29:28) Should You Avoid Free Pilots When Selling to Enterprises? (32:13) Does Cheap AI-Powered Building Make Finding Demand Easier or Harder? (33:37) How Can You Protect Your SaaS Product From Being Vibe Coded? (36:40) Will Smarter AI Models Kill Products Like Harvey and Lovable? (38:42) Is Building a Product for Yourself an Ego Project or Real PULL? (40:32) What Is LARPing in Startups and How Do Founders Fall Into It? (42:58) What Is the Success Story Factory and the Theory of Constraints? (46:22) Why Is PULL Upstream of Everything in Business? (47:32) Where Can I Learn More About PULL? (48:34) 3 Tech Lead Wisdom _____ Rob Snyder’s Bio Rob Snyder is a serial startup founder and a fellow at Harvard Innovation Labs. He graduated from Harvard Business School and previously worked at McKinsey & Company. He is also an entrepreneur-in-residence and venture partner for early-stage venture capital funds. Snyder lives with his wife and daughter in New Hampshire. Follow Rob: LinkedIn – linkedin.com/in/rsnyder1 Website – robsnyder.org The Physics of Startups Newsletter – thephysicsofstartups.substack.com The Physics of Startups YouTube – @rob-snyder Like this episode? Show notes & transcript: techleadjournal.dev/episodes/271 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

October 12, 2026Episode 2701 hr 2 min

Stop Being the Hero: How Developers Become Great Software Architects - Lee Atchison

Why do the best developers often make the worst software architects? The skills that make someone a hero developer can become the habits that hold their team back once they move into architecture. In this episode, Lee Atchison, software architect and author of “The Software Conductor”, explains what changes when you move from writing code to leading through other people. He shares why he rewrote his book three times before settling on a story format, and how the orchestra conductor analogy captures the architect’s role. Lee walks through the four ways architects fail in hero mode, the five questions a conductor asks that architects can use to spot problems early, and why most system failures are really broken connections between people. He also explains why architects should act as mentors who remove obstacles and offer a test for whether you taught or just decided. Finally, he shares his view on AI, why judgment matters more than any particular role, and why he expects the dark factory trend to fade. Key topics discussed: Why the hero developer mindset fails in architecture roles The four ways architects fail when they stay in hero mode Five conductor questions that expose problems before they break Why most system failures are really broken team connections A simple test: can the decision be made without you? Why architects work with sand, not big rocks Why judgment matters more than role titles in the AI era Why AI-native apps should embrace non-determinism Timestamps: (00:00) Trailer & Intro (02:53) What Is the Story Behind Writing “The Software Conductor”? (06:22) Why Do Promoted Heroes Struggle as Software Architects? (10:52) What Does a Fractional Software Architect Actually Do? (12:29) Is Software Architecture an IC or Management Path? (16:08) Do You Need to Know Everything to Lead with Influence? (18:08) What Are the Four Ways Architects Fail in Hero Mode? (21:06) What Five Questions Help Architects Spot Problems Before They Break? (25:57) Why Is Empathy at Scale a Crucial Skill for Software Architects? (29:34) Why Should Software Architects Act as Mentors Rather Than Dictators? (33:49) How Do Architects Manage Constant Context Switching? (37:37) Why Was AI Excluded from “The Software Conductor” Book? (40:24) Does AI Really Turn Every Software Developer into a Tech Lead? (44:34) Is AI Policy and Governance Part of the Software Architect Role? (48:41) What Are the Dangers of the AI Dark Factory Trend? (53:23) What Are the Most Important Aspects of Being an AI-Native Team? (57:52) 3 Tech Lead Wisdom _____ Lee Atchison’s Bio Lee Atchison is a software architect, author, and speaker focused on cloud computing, scalable system design, and the developer-to-architect transition. He is the author of Architecting for Scale (O’Reilly), Overcoming IT Complexity (O’Reilly), and The Software Conductor, and the creator of courses for O’Reilly Media and LinkedIn Learning. He writes the Software Architecture Insights newsletter and guest hosts on Software Engineering Daily. With three decades in the industry, including seven years at Amazon and AWS and eight at New Relic, Lee has worked with engineering teams at Nike, Disney, Starbucks, MLB, and Bank of America, and presented at AWS re:Invent and similar conferences. He works with organizations through his practice, Atchison Technology, and most recently served as CTO at Product Genius. Follow Lee: LinkedIn – linkedin.com/in/leeatchison The Software Conductor – thesoftwareconductor.com Software Architecture Insights – softwarearchitectureinsights.com Personal Website – leeatchison.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/270 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

September 28, 2026Episode 2691 hr 7 min

The IT/OT Handbook: From Silos to Sustainable Digitalization - David Ariens & Willem van Lammeren

Why can’t you “move fast and break things” in a factory? Because a small software bug doesn’t just crash an app, it can shut down a plant the size of a small city. David and Willem explain the hidden world of operational technology, where safety always comes before speed. In this episode, David Ariens and Willem van Lammeren, co-authors of “The IT/OT Handbook” and hosts of the IT/OT Insider podcast, break down the divide between IT and OT, information technology and operational technology, and why closing it matters more than ever as manufacturing digitalizes. They explain why OT prioritizes safety and integrity over confidentiality and share a war story where a single form change in China left hundreds of trucks stranded outside a factory. David and Willem walk through the seven cooperation patterns from their book, from “good shadow IT” to facilitation, project teams, and full product teams, and explain why breaking down silos too early can backfire. They also discuss why factories still run Windows XP decades past the end of life, why generative AI’s impact on manufacturing will be slower and different from software, and why the real shortage in this industry isn’t AI, it’s automation engineers. Timestamps: (00:00:00) Trailer & Intro (00:02:42) What Is the Story Behind the Book and the Podcast? (00:06:31) What Is the Main Difference Between IT and OT? (00:11:58) Are Software Developer Skill Sets Interchangeable Between IT and Operations? (00:15:23) How Did The Phoenix Project and Team Topologies Inspire IT/OT Cooperation Patterns? (00:22:41) What Are Some Industry War Stories That Highlight the Challenges Between IT and OT? (00:30:57) What Collaboration Patterns Can Help Resolve Tension Between IT and OT? (00:39:43) Is Tesla the Ultimate Reference Model for Modern Product Teams? (00:42:41) How Can OT Teams Adopt CI/CD Through a Data Platform Abstraction Layer? (00:44:37) What Will Be the Real Impact of AI on Operational Technology? (00:51:54) What Is Driving the Shortage of Automation Engineers in Operations? (00:54:51) Are Humanoid Robots Overhyped for Manufacturing? (01:01:52) 3 Tech Lead Wisdom _____ David Ariens’s Bio David Ariens is a mathematical engineer, co-founder of The IT/OT Insider and The IT/OT Handbook. Previously the Industrial Digitalization Director at BASF Antwerp and former manager of Analytics For Industry, David focuses on industrial cybersecurity, data platforms, and bridging IT and OT through human-centric digital transformation. Willem van Lammeren’s Bio Willem van Lammeren is a chemical engineer, co-founder of The IT/OT Insider and The IT/OT Handbook. He serves as Tech Lead for Industrial IT at Syensqo (formerly Solvay) with extensive experience spanning Barry Callebaut and BASF, focusing on industrial digitalization and operational data architectures. Follow: David’s LinkedIn – linkedin.com/in/davidariens Willem’s LinkedIn – linkedin.com/in/willemvanlammeren The IT/OT Handbook – https://itrevolution.com/product/itot-handbook/ The IT/OT Insider – itotinsider.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/269 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

September 7, 2026Episode 26859 min

Don't Outsource Your Thinking: How to Lead in the AI-Native Era - Emilie Schario

What’s the one rule that matters most when AI writes most of your code? Emilie says it comes down to a single line: don’t outsource your thinking. In this episode, Emilie Schario, co-founder of Kilo Code, unpacks what it actually means to lead in a world where AI can write most of your code. She explains why Kilo went all-in on being model agnostic, supporting over 500 models instead of betting on a single lab. Emilie shares her core rule for using AI responsibly: never outsource your thinking, especially when it comes to auth, billing, or security. She introduces a metric more companies should be tracking, spend per merged pull request, as a better signal of value than raw AI cost. Emilie also talks about the “killing problem” that comes with software becoming nearly free to ship, and why teams now discover product-market fit after shipping rather than before. She closes with how engineers are shifting from producers to reviewers, and what that means for anyone now managing a team of AI agents instead of just their own code. Key topics discussed: Why Kilo Code refuses to bet on a single AI model The one rule for not losing control to AI agents A better way to measure AI spend: cost per merged PR Why shipping software is nearly free, and why that’s risky How AI-native teams find product-market fit after shipping Why senior engineers adapt to AI agents the fastest Building a council of reviewers for high-risk code changes What the Anaconda acquisition means for Kilo Code Timestamps: (00:00) Trailer & Intro (02:27) What Is Kilo Code All About? (03:31) How Do You Avoid Getting Overwhelmed by 500 Plus AI Models? (04:34) How Does a Multi-Platform Approach Benefit Developers? (07:19) How Does Kilo’s Agent Harness Differ From Other Tools? (09:51) Why Will Model Choice Matter Less Over Time? (11:20) What Tools Help You Pick the Right Model for Each Task? (14:16) What Does the Anaconda Acquisition Mean for Kilo Code? (15:43) Why Should You Measure AI Spend Per Merged Pull Request? (18:54) How Do You Balance Speed and Control in AI Development? (20:28) Why Should Companies Treat AI as Operational Infrastructure? (23:31) How Do AI-Native Organizations Run Their Day-to-Day Workflows? (27:58) How Will High-Performing AI Organizations Differentiate Themselves in the Future? (30:11) What Is the “Killing Problem” Now That Shipping Software Is Free? (32:40) How Do You Shift to a Metrics-First Mindset? (34:24) How Is the Product Manager’s Role Changing in AI-Native Teams? (37:06) How Do You Balance Rapid Feature Delivery With Product Quality? (41:48) How Is the Software Engineer’s Role Evolving in the Age of AI? (43:57) What Is the Best Advice for Junior Engineers Entering an AI-Native World? (45:51) How Can Developers Handle the Cognitive Load of Code Reviews? (49:11) How Should Docs and Code Attribution Evolve With AI Agents? (53:06) How Can AI Improve Your Life Outside of Work? (55:04) 3 Tech Lead Wisdom _____ Emilie Schario’s Bio Emilie is VP, Engineering at Anaconda and the former cofounder of Kilo (acquired by Anaconda), where she focuses on turning AI investment into real engineering output. She operates at the intersection of AI tooling, engineering org design, and the day-to-day realities of running high-performing teams, helping organizations translate innovation into measurable impact. Follow Emilie: LinkedIn – linkedin.com/in/emilieschario Playbooks and Priority newsletter – emilie.substack.com Kilo Code – kilo.ai/ Like this episode? Show notes & transcript: techleadjournal.dev/episodes/268 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

August 24, 2026Episode 26755 min

The Father of AI Assistant on Why Joy Comes Before Success - Kevin Surace

If you’re not using AI five times an hour, you’re already behind, says the man who built the first AI assistant back in 1998. Kevin explains why that pace is now the baseline, not the exception, for developers who want to stay employed. In this episode, Kevin Surace, the inventor of the first AI assistant and CEO of Appvance, joins to unpack six decades of AI evolution, from hidden Markov models to the LLM breakthroughs now powering coding agents. Kevin shares how his work at General Magic became the licensed technology behind Siri and Alexa, and how he ended up inventing post-training before anyone had a name for it. He explains why the only developers losing their jobs are the ones refusing to adopt AI, and how the role is shifting from writing code to specifying, coordinating, and testing it across a fleet of agents. Kevin also introduces AI Script Generation Bug Hunter, the testing technology uncovering thousands of bugs humans never catch. The conversation closes with his new book, Joy-Success Cycle, and why he believes joy has to come before success, not after. Timestamps: (00:00) Trailer & Intro (02:27) How Did Kevin Become the Father of AI Assistant? (07:45) How Did AI Evolve From Hidden Markov Models to the LLM Breakthrough? (11:51) How Were Siri and Alexa Built on Kevin’s Licensed AI Technology? (14:25) How Should Software Developers Evolve in the Age of AI? (20:39) Should Developers Fear the Rise of Agentic AI? (25:25) How Does Appvance Use AI to Find Bugs Humans Miss? (32:18) Why Did Kevin Write the Book Joy-Success Cycle? (38:20) How Can We Reclaim Positive Emotions Amid Global Crises and AI Anxiety? (46:44) How Do Purpose and Curiosity Work Together to Guide Your Life? (48:49) How Can a Simple To-Do List Become a Powerful Source of Joy? (50:20) How Did the Joy-Success Method Transform a Sales Team’s Attitude? (52:52) 3 Tech Lead Wisdom _____ Kevin Surace’s Bio Kevin Surace is an AI pioneer and human effectiveness authority helping people thrive while AI reshapes how we work and live. Widely recognized as the father of the virtual assistant, he holds 95 worldwide patents and has been named INC Magazine’s Entrepreneur of the Year, a CNBC Top Innovator of the Decade, and a World Economic Forum Tech Pioneer. He is the author of The Joy-Success Cycle, a practical framework for sustained human effectiveness during rapid change. Follow Kevin: LinkedIn – linkedin.com/in/ksurace Personal Website - kevinsurace.com The Joy-Success Cycle – joysuccesscycle.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/267 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

August 10, 2026Episode 2661 hr 1 min

Software Engineering Laws Every Developer Must Know in the AI Era - Milan Milanovic

Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard. In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall’s Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway’s Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart’s Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter’s Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field, his top five must-read books, and how he personally uses AI for research and planning rather than implementation. Key topics discussed: Gall’s Law: the trap of generating complex systems with AI Conway’s Law now runs twice, on humans and on AI agent topology Why software architects should help design your org chart Goodhart’s Law and the danger of “tokenmaxxing” as a metric The 90-90 rule: why AI’s last 10% still costs half the project Dunning-Kruger effect on vibe coders and experts Why less code beats more, even with AI writing it for free Timestamps: (00:00) Trailer & Intro (03:02) What Inspired Milan to Write Laws of Software Engineering? (05:32) How Did a Book on Software Laws Reach Such a Global Audience? (06:49) How Should You Read the Laws of Software Engineering Book? (08:51) Why Does the Book Cover People and Planning, Not Just Technical Laws? (11:07) What Is Gall’s Law in Software Engineering? (14:46) How Can You Apply Gall’s Law When Using AI? (17:34) What Is Conway’s Law in Software Engineering? (19:34) How Should Big Corporations Build New Products Without Structural Reorganization? (21:26) How Does Conway’s Law Apply to Small AI-Powered Product Teams? (23:25) What Is Goodhart’s Law in Software Engineering? (25:52) What Are the Best Examples of Counterbalance Metrics for Leaders Today? (28:06) What Kind of Outcomes Should You Actually Measure? (31:01) What Is Hofstadter’s Law in Software Engineering? (33:35) How Does Hofstadter’s Law Apply in the Age of AI? (36:41) What Is the Dunning-Kruger Effect? (40:10) How Does the Dunning-Kruger Effect Apply to Experienced Engineers Learning AI? (42:46) Are Any of These Laws Becoming Less Relevant Because of AI? (44:32) What Does the Lindy Effect Say About the Fate of Software Developers? (47:49) What Is the Best Career Advice for Junior Developers Entering the AI Landscape? (50:34) What Are the Top Five Must-Read Books for Software Engineers? (55:46) How Can Software Engineers Apply These Laws in Their Daily Work? (57:26) 3 Tech Lead Wisdom _____ Milan Milanovic’s Bio Milan Milanović is the CTO and the author of Laws of Software Engineering. He holds a PhD in Computer Science, has more than 20 years of experience across .NET, Azure, and mobile development, and is a Microsoft MVP. He runs Tech World With Milan, a software engineering newsletter and community followed by more than 400,000 engineers. He writes about architecture, engineering leadership, and how AI is changing the way software gets built. Follow Milan: LinkedIn – linkedin.com/in/milanmilanovic Twitter / X – @milan_milanovic Personal website – milan.milanovic.org Book’s website - lawsofsoftwareengineering.com Newsletter - newsletter.techworld-with-milan.com Like this episode? Show notes & transcript: techleadjournal.dev/episodes/266 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

August 3, 2026Episode 2651 hr 17 min

Signals & Levers: Systems Thinking to Navigate Software Delivery Illusions - Elisabeth Hendrickson & Joel Tosi

(04:48) Brought to you by SpeechifyAI Tired of text-to-speech that sounds robotic or costs too much at scale? SpeechifyAI's new Simba 3.2 model ranks #1 on Artificial Analysis for realness, priced under $10 per million characters. Building something conversational? Check out the Simba Voice Agents API for low-latency, natural back-and-forth interaction. Try it free at speechify.ai . Why do the same software delivery illusions keep fooling smart engineering teams? Elisabeth and Joel show how systems thinking, through signals and levers, helps you spot the illusions of progress, predictability, and control before they cost you. In this episode, Elisabeth Hendrickson and Joel Tosi, co-authors of Signals & Levers, share the story behind the book and trace the “software crisis” back to a 1968 NATO conference, arguing it never actually went away. They explain why software delivery is an adaptive sociotechnical system, and why treating it as a simple linear process leads leaders to pull the wrong levers. Elisabeth breaks down why proxy metrics like velocity are made-up numbers dressed up as science, and why cycle time tells a truer story. Joel walks through the CREATE framework (capacity, risk, execution, adaptability, trust, and economics), and how it can help leaders spot unintended consequences before they happen. They also dig into the three illusions leaders live under: illusion of progress, predictability, and control. The conversation closes with a candid look at where AI fits into all of this, when it amplifies good systems, when it makes bad ones worse, and why optimizing for learning matters more than ever. Timestamps: (00:00:00) Trailer & Intro (00:02:34) The Backstory Behind “Signals & Levers” (00:05:55) Why Has the ‘Software Crisis’ Never Actually Gone Away? (00:08:41) Why Do the Same Software Development Problems Keep Appearing? (00:10:55) What is an Adaptive Sociotechnical System? (00:15:09) Why Do Business Executives Fail to Understand Software Development? (00:20:03) What Are Signals and Levers in Engineering Leadership? (00:24:31) What Makes Proxy Metrics Like Velocity and Lines of Code Dangerous? (00:28:23) Are DORA Metrics a Good Proxy for Software Development Productivity? (00:32:50) What is the CREATE Framework for Avoiding Unintended Consequences? (00:38:26) How Do You Quantify and Apply the CREATE Framework? (00:44:09) What Are the Three Illusions That Leaders Face in Software Delivery? (00:50:27) Will AI Truly Speed Up Software Development? (00:55:55) How Should Leaders Integrate AI Into Systems Thinking? (01:00:53) Can AI Be a Thinking Partner for Systems Thinking? (01:04:26) Why Is the U-Curve a Powerful Tool for Modern Leadership? (01:09:17) 3 Tech Lead Wisdom _____ Elisabeth Hendrickson & Joel Tosi’s Bio Elisabeth Hendrickson is a technology leader with 30+ years of experience, having served as VP R&D at a public company and VP Engineering at a Series B startup. She’s the author of Explore It! and There’s Always a Duck, and now helps tech leaders improve collaboration, decision-making, and execution. Joel Tosi has spent over 25 years delivering software products. For the past decade, he’s helped teams see the systemic issues holding them back and stop “change theater,” using the techniques from Signals & Levers to give everyone a shared view of reality. He’s presented these ideas internationally for over five years. Follow Elisabeth & Joel: LinkedIn (Elisabeth) – linkedin.com/in/testobsessed LinkedIn (Joel) – linkedin.com/in/joel-tosi-531a3b Website – signalsandlevers.com Signals & Levers – itrevolution.com/product/signals-and-levers Signals & Levers workshops – maven.com/signalsandlevers Like this episode? Show notes & transcript: techleadjournal.dev/episodes/265 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

July 27, 2026Episode 2641 hr 10 min

From Technical Debt to Triple Debt: The Hidden Cost of AI-Generated Code

Is vibe coding quietly draining the thing that makes teams effective: their shared understanding? Margaret breaks down her new “triple debt” model, and why cognitive debt might be the one nobody’s tracking. In this episode, Margaret-Anne Storey, co-author of the SPACE framework and a leading developer experience researcher, returns three years after her first appearance to unpack her new “triple debt” model. She explains how technical debt is now joined by cognitive debt, the erosion of shared understanding across a team, and intent debt, the loss of the “why” behind decisions that agents also lack. Drawing on her Startup Studio course, where student teams built MVPs in minutes only to lose track of their own architecture weeks later, she walks through what pushed her to name these problems. The conversation covers cognitive surrender, developer fatigue from managing swarms of agents, and the warning signs leaders should watch for on their teams. Margaret also introduces Cognitive Tours, a tool inspired by sailing waypoints and log books, and the idea of strategic friction: deliberately slowing down to preserve understanding. She closes by revisiting the SPACE framework and why its five dimensions still hold up even as AI changes every question we ask about them. Key topics discussed: Why cognitive debt has always existed, just never named The triple debt model: technical, cognitive, and intent Cognitive surrender: accepting AI output without understanding Warning signs leaders should watch for on their teams Why the SPACE framework still holds up in the AI era Timestamps: (00:00:00) Trailer & Intro (00:02:42) How Has AI Transformed Software Development Over the Past Three Years? (00:05:14) What Inspired the Research on Cognitive Debt and AI-Assisted Development? (00:11:28) Why Is Cognitive Debt Accelerating Even Though It Isn’t a New Problem? (00:14:24) What Exactly Is the Triple Debt Model in Software Development? (00:17:36) How Does Intent Debt Relate to Context Engineering and Intent Drift? (00:21:57) Can AI Be Used to Solve the Cognitive Debt It Created? (00:26:55) Why Does Using AI Make Developers Feel Fatigued and Stressed? (00:31:33) How Are AI Agents Affecting Human Relationships in the Workplace? (00:34:37) What Exactly Is Cognitive Surrender and What Are Its Risks? (00:39:38) How Can Leaders Spot Growing Cognitive and Intent Debt Within Their Teams? (00:41:02) Why Is ‘Tokenmaxxing’ a Dangerous Productivity Metric? (00:42:49) What Is the Cognitive Tours Tool and How Does It Address Cognitive and Intent Debt? (00:48:19) How Do You Envision the Daily Workflow of Using This New Tool? (00:50:16) What Is Strategic Friction and How Can It Help Developers? (00:55:10) What Is the Danger of Relying More on AI Agents and Less on Humans? (00:58:26) Does the SPACE Framework Still Hold Up in the Age of AI? (01:04:22) 3 Tech Lead Wisdom _____ Margaret-Anne Storey’s Bio Margaret-Anne Storey is a professor of computer science at the University of Victoria and a Canada research chair in human and social aspects of software engineering. She is coauthor of the SPACE framework and a leading researcher in developer experience (DevEx). Her research focuses on how developers and teams understand complex software systems and how tools, AI, and collaborative practices shape that understanding. Her recent work examines how generative AI is transforming software engineering by changing how understanding is created, shared, and maintained. She collaborates with industry partners including Microsoft and DX. She holds an honorary doctorate from Lund University. Follow Margaret: LinkedIn – linkedin.com/in/margaret-anne-storey-8419462/ Website – margaretstorey.com The Triple Debt Model: From Technical Debt to Cognitive and Intent Debt – queue.acm.org/detail.cfm?id=3807966 Like this episode? Show notes & transcript: techleadjournal.dev/episodes/264 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

July 13, 2026Episode 2631 hr 22 min

Ex-Gojek CTO: Why the Age of AI Rewards Builders as Judgment Becomes Expensive

If expertise is becoming almost free, why is judgment becoming the most expensive skill in tech? Ajey Gore, former Gojek CTO, explains why the age of AI agents rewards builders over typists. In this episode, Ajey Gore, former Group CTO of Gojek and Operating Partner at Peak XV Partners, explains why coding agents are forcing software teams to rethink what “correct” really means. He argues that as expertise becomes cheap, judgment becomes the scarcest and most valuable skill in engineering. Ajey walks through why the classic developer-to-reviewer workflow no longer makes sense when agents can generate thousands of lines a day, and why trunk-based development, feature flags, and rigorous testing matter more than ever. He also shares how he built ClawStation solo, coordinating specialized AI agents through story cards instead of writing code by hand. The conversation covers why most organizations still fail to see returns from AI adoption, and what happens when leaders cut headcount without redesigning how work actually flows. Key topics discussed: Why judgment, not typing speed, is now the scarce skill The workflow mistake behind most failed AI adoption How Ajey built ClawStation solo without writing code Why he advocates trunk-based development to replace PRs The real risk of cutting headcount before rethinking work Why “earning time” matters as much as earning money Southeast Asia’s surprising advantage in the AI era Timestamps: (00:00:00) Trailer & Intro (00:04:10) What Has Life Been Like Since Leaving Peak XV? (00:09:02) Why Should You Create Slack Instead of Always Running Full Speed? (00:13:47) Why Being Uncomfortable and Replaceable Makes You More Valuable (00:18:12) Why Earning Time Matters as Much as Earning Money (00:24:57) Why Is This the Age of Builders, Not Typists? (00:40:55) What Should You Not Do While Navigating the AI Revolution? (00:50:07) What Are the Top Engineering Practices Needed to Leverage the AI Revolution? (00:55:09) How Did Ajey Build ClawStation Solo Using Story Cards and Agent Roles? (00:58:57) How Can Developers Successfully Let Go of Writing Code by Hand? (01:02:29) Why Do 95% of Organizations Fail to See AI ROI? (01:08:49) What Are the Real Dangers of AI-Driven Layoffs? (01:12:20) What Does the Future Hold for the Southeast Asian Tech Scene? (01:15:29) What Must We Do to Avoid Losing Our Fundamental Thinking Skills to AI? (01:19:25) 3 Tech Lead Wisdom _____ Ajey Gore’s Bio Ajey Gore is a technologist, builder, and founder who has spent two decades turning engineering into scale. As Group CTO of Gojek, he grew the platform from 300K to 120M monthly orders and built a 2,000-strong engineering org powering one of Southeast Asia’s largest super-apps. He was an early employee at ThoughtWorks India, founded CodeIgnition (acquired by Gojek), and served as Operating Partner at Peak XV Partners (formerly Sequoia India & SEA), advising founders across the region. Today he builds and advises at the intersection of AI, infrastructure, and product — and is a hands-on practitioner who still ships code. Follow Ajey: Website – ajeygore.in X / Twitter – x.com/ajeygore LinkedIn – linkedin.com/in/ajeygore ClawStation – clawstation.ai Like this episode? Show notes & transcript: techleadjournal.dev/episodes/263 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .

June 29, 2026Episode 2621 hr 5 min

The AI Productivity Paradox: Why 10X Output Doesn't Mean 10X Business Outcome

What if optimizing for AI output is actually slowing your company down? When code becomes nearly free to produce, the organizations still measuring productivity by output are solving the wrong problem. In this episode, Mik Kersten, author of “Project to Product” and the forthcoming “Output to Outcome,” shares why the real challenge of the AI era isn’t generating more code — it’s building organizations that can turn that output into customer and business value. Drawing on Carlota Perez’s model of technological revolutions and the theory of constraints, Mik explains how AI has removed the output bottleneck that software organizations were built around, and where the new constraints now live. He introduces three core models from the book — the outcome loop, the product operating model, and the outcome tree — as a framework for adapting how organizations plan, fund, and deliver value. Mik also addresses one of the most pressing decisions leaders face today: whether to cut headcount based on AI productivity gains, and why doing so without outcome visibility is a dangerous bet. The conversation covers how organizational structure, decision-making accountability, and leadership roles all need to shift — not just development practices. Timestamps: (00:00:00) Trailer & Intro (00:02:40) What Makes Output to Outcome Different From Project to Product? (00:05:02) Why Did Mik Write Every Word of This Book Without AI? (00:08:18) How Do the AI Prompts at the End of Each Chapter Work? (00:11:53) What Happens to Organizations When AI Makes Software Output 10 to 100 Times Cheaper? (00:15:03) How Do Past Technological Revolutions Help Us Understand the AI Era? (00:19:25) Is the Traditional Software Developer Role Gone for Good? (00:23:30) Why Do Some Companies Experience an AI Productivity Paradox? (00:27:47) What Does “Outcome” Mean in Outcome Management? (00:31:50) Has the Product Operating Model Finally Become the Industry Norm? (00:34:24) How Do You Apply the Cynefin Framework to Your Organization? (00:37:18) Why Should AI Augment Human Decision Making in Complex Domains? (00:40:52) Why Are AI-Driven Layoffs a Risky Bet Without Outcome Visibility? (00:43:32) How Can Leaders Increase the Feedback Loop for Strategy and Budgeting? (00:46:07) What Is the Optimal Organizational Structure for an Outcome Management Model? (00:49:50) How Can We Apply Architectural Modularity to Organizational Design? (00:53:17) What Are the Seven Shifts in the Output to Outcome Model? (00:55:10) Will AI Make Middle Management Obsolete? (01:00:55) 3 Tech Lead Wisdom _____ Mik Kersten’s Bio Dr. Mik Kersten is an independent technology strategist and creator of the Flow Framework, best known for his bestselling book Project to Product. He founded Tasktop and led it as CEO until its acquisition by Planview in 2022. Mik began his career at Xerox PARC, where his team created the first aspect-oriented programming language. He then earned his PhD in Computer Science at UBC, pioneering the integration of software development and collaboration tools — work that laid the foundation for Tasktop and the field of Value Stream Management. Today, he helps leaders shift from output-driven to outcome-driven operating models, enabling organizations to harness AI in a human-centric way. Follow Mik: LinkedIn – linkedin.com/in/mikkersten Substack – mikkersten.substack.com Preorder Output to Outcome - https://a.co/d/0aPlI2IF Book’s Website – outputtooutcome.org Like this episode? Show notes & transcript: techleadjournal.dev/episodes/262 . Follow @techleadjournal on LinkedIn and Instagram . Buy me a coffee or become a patron .