Tech Lead Journal

by Henry Suryawirawan

NewHow to Build a Successful Product: Stop Chasing Pain Points, Start Finding PULL - Rob SnyderOctober 26, 2026 · 53 min

Episodes: 284
Released: every two weeks
Latest episode: Oct 26, 2026

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About the podcast

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.

Episodes 284

How to Build a Successful Product: Stop Chasing Pain Points, Start Finding PULL - Rob Snyder
October 26, 202653 minEpisode 271
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 …
Show notes
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
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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
📖 The Power of Pull: What You Need to Know About Customer Demand to Build a Successful Startup – https://www.amazon.com/Power-Pull-Customer-Successful-Founders-ebook/dp/B0FZL6M82Y
Website – robsnyder.org
The Physics of Startups Newsletter – thephysicsofstartups.substack.com
The Physics of Startups YouTube – @rob-snyder

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Stop Being the Hero: How Developers Become Great Software Architects - Lee Atchison
October 12, 20261 h 2 minEpisode 270
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 …
Show notes
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
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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

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The IT/OT Handbook: From Silos to Sustainable Digitalization - David Ariens & Willem van Lammeren
September 28, 20261 h 7 minEpisode 269
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 …
Show notes
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
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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

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Don't Outsource Your Thinking: How to Lead in the AI-Native Era - Emilie Schario
September 7, 202659 minEpisode 268
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 …
Show notes
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
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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/
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The Father of AI Assistant on Why Joy Comes Before Success - Kevin Surace
August 24, 202656 minEpisode 267
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 …
Show notes
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
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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

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Show notes & transcript: techleadjournal.dev/episodes/267.
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Software Engineering Laws Every Developer Must Know in the AI Era - Milan Milanovic
August 10, 20261 h 2 minEpisode 266
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 …
Show notes
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
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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

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Show notes & transcript: techleadjournal.dev/episodes/266.
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Signals & Levers: Systems Thinking to Navigate Software Delivery Illusions - Elisabeth Hendrickson & Joel Tosi
August 3, 20261 h 17 minEpisode 265
(04:48) Brought to you by SpeechifyAITired 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 …
Show notes
(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
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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

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From Technical Debt to Triple Debt: The Hidden Cost of AI-Generated Code
July 27, 20261 h 11 minEpisode 264
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 …
Show notes
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

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Ex-Gojek CTO: Why the Age of AI Rewards Builders as Judgment Becomes Expensive
July 13, 20261 h 22 minEpisode 263
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 …
Show notes
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

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The AI Productivity Paradox: Why 10X Output Doesn't Mean 10X Business Outcome
June 29, 20261 h 6 minEpisode 262
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 …
Show notes
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

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The Hidden Stories Sabotaging Your Culture Change
June 15, 20261 h 8 minEpisode 261
Why do 70% of change efforts fail — even when leadership is fully committed? The answer isn’t strategy or resources; it’s the hidden stories people unknowingly carry that silently block every initiative.In this …
Show notes
Why do 70% of change efforts fail — even when leadership is fully committed? The answer isn’t strategy or resources; it’s the hidden stories people unknowingly carry that silently block every initiative.
In this episode, Ronica Roth, author of “Practice Makes Culture” and co-founder of The Welcome Elephant, shares a practical framework for creating lasting organizational change. Drawing on 25 years of experience helping teams and companies transform, she explains why culture declarations and vision speeches alone never work — and what leaders at any level can do instead. Ronica introduces the concept of “welcoming elephants” — the emotional, systems, and room elephants that surface whenever change is attempted — and why acknowledging them is the first step toward real progress. She also unpacks why most culture lives beneath the surface, in hidden stories that employees carry without realizing it, and how those stories quietly undermine even well-designed initiatives.
The conversation covers how to build psychological ownership so people invest in change rather than just comply with it, and why small, intentional daily practices — not grand overhauls — are what actually shift culture. The discussion also touches on applying these principles to AI transformation, where the emotional stakes are especially high and the hidden stories especially loud.
Key topics discussed:
Why 70% of change efforts fail — and what to do about it
The three types of “elephants” blocking organizational change
Hidden stories: the invisible force sabotaging your culture
Why declaring a new culture is necessary but not sufficient
How to create psychological ownership (not just buy-in)
Using meetings as a daily practice for cultural change
Leading AI transformation with vulnerability and structure
The WOOP method for making personal behavior change stick
Timestamps:
(00:00:00) Trailer & Intro
(00:02:43) Why Ronica Write a Book About IT Culture?
(00:05:14) What Are the Three Types of Elephants That Hold Organizations Back?
(00:11:05) Why Do 70% of Change Efforts Fail?
(00:15:45) How Does Ronica Define the Different Layers of Culture?
(00:20:57) Why Is Declaring a New Culture Necessary But Not Sufficient?
(00:23:12) What Are the Three Pillars of Your Cultural Transformation Framework?
(00:39:28) How Can You Turn Meetings Into a Daily Practice for Cultural Change?
(00:48:51) How Can Leaders Address the Emotional Elephant of AI Transformation?
(00:56:13) Can You Apply These Culture Change Principles to Personal Growth?
(01:02:06) What Are the Five Culture Hacks for Scaling Cultural Impact?
(01:04:49) 3 Tech Lead Wisdom
_____
Ronica Roth’s Bio
Ronica Roth is a transformation expert dedicated to revolutionizing how organizations work. As cofounder of The Welcome Elephant consultancy, she helps leaders build thriving cultures where both business results and human potential flourish. With deep expertise in product management, business agility, and organizational change, Ronica brings a unique perspective shaped by her certification as a Leadership Circle® practitioner and her distinguished background as a Certified Scrum Trainer Emeritus. Her approach is informed by an MS in Journalism from Northwestern University and enriched by her earlier career in newspapers, giving her exceptional skills in storytelling and communication crucial for effective organizational change.
Follow Ronica:
LinkedIn – linkedin.com/in/ronicaroth
Practice Makes Culture – itrevolution.com/product/practice-makes-culture
The Welcome Elephant – thewelcomeelephant.co
Practice Makes Culture Substack - practicemakesculture.substack.com

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Creator of Meta's Hack: Your AI Will Always Cheat — Here's How to Stop It
June 8, 20261 h 18 minEpisode 260
What if your AI coding agent is quietly cheating on your tests — and how do you stop it? Julien Verlaguet, who built the type system Meta used to migrate tens of millions of PHP lines, is now building Skipper …
Show notes
What if your AI coding agent is quietly cheating on your tests — and how do you stop it? Julien Verlaguet, who built the type system Meta used to migrate tens of millions of PHP lines, is now building Skipper: a closed-loop coding agent designed to make AI-generated code verifiably correct, without human intervention.
In this episode, Julien Verlaguet, creator of the Hack programming language at Meta and co-founder of SkipLabs, explains why AI agents will always try to cheat — gaming tests, quietly modifying logic while doing something else, and declaring work done when it isn’t. He draws on his experience migrating Meta’s PHP codebase to a statically typed system, drawing sharp parallels between convincing engineers to trust a new type checker and building systems that can trust an LLM. Julien makes the case for spec-driven development with validation layers at every step, where separate AI instances verify correctness and the code-writing agent is locked out of touching tests.
He shares the story of an LLM that silently swapped a union for an intersection while splitting a file — a subtle bug that passed all tests — and why no human would ever have made that mistake. He then walks through how Skipper works: you write a spec, hand over control, and a compiler-like agent produces correct, runnable TypeScript without back-and-forth, backed by a sound incremental type system, reachability analysis, and a reactive runtime that applies diffs in milliseconds.
He closes with a grounded take on how the developer role is shifting — not disappearing — toward the kind of design, integration, and oversight work that always mattered most.
Key topics discussed:
Why AI agents will always try to cheat on your tests
The union-vs-intersection bug an LLM introduced silently
Spec-driven development to keep LLMs on track
How to separate the AI that verifies from the one that fixes
Skipper: a compiler-like closed-loop coding agent
Sound, incremental TypeScript built for AI-speed iteration
Hot-reloading state without restarting — in milliseconds
Why developers are all becoming tech leads
Timestamps:
(00:00:00) Trailer & Intro
(00:02:34) How Did Julien Create the Hack Programming Language at Facebook?
(00:05:53) Does Static Typing Make Your Code More Secure?
(00:09:54) How Did You Convince Facebook Engineers to Adopt Hack at Scale?
(00:17:15) How Can Engineers Overcome Skepticism Toward AI Coding Tools?
(00:22:44) Should Junior Engineers Trust AI-Generated Code?
(00:29:44) How Do You Build Reliable Guardrails for LLM-Generated Code?
(00:42:15) What Validation Strategies Prevent AI Agents From Cheating on Tests?
(00:45:54) What Is Skipper and How Does a Closed-Loop Coding Agent Work?
(00:54:59) How Does Skipper Compare to Claude Code in Terms of Correctness?
(00:58:27) How Do You Get Started With Skipper and What Does the Output Look Like?
(01:04:50) How Will the Software Developer Role Change in an AI-First World?
(01:09:06) 3 Tech Lead Wisdom
_____
Julien Verlaguet’s Bio
Julien Verlaguet is a programming language designer and the Founder and CEO of SkipLabs. He is best known as the creator of Hack—the gradually typed language he built at Facebook that currently powers over 100 million lines of the company’s production code. After creating the open-source reactive framework Skip, Julien founded SkipLabs in 2022. His company recently launched Skipper, a closed-loop coding agent that takes a single prompt from a developer and returns a running, validated service.
Follow Julien:
LinkedIn – linkedin.com/in/julien-verlaguet-b5710a20
X – x.com/JulienVerlaguet
SkipLabs - skiplabs.io
Skipper - skipperai.dev
Skipper’s Discord – discord.gg/bsnXyw2F9P

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Eric Ries: Why Good Tech Companies Go Bad, and How to Stop It
June 1, 20261 hEpisode 259
Why do companies with the best intentions end up betraying their customers, employees, and mission? Eric Ries calls it “financial gravity” — an invisible force that pulls even the most principled companies …
Show notes
Why do companies with the best intentions end up betraying their customers, employees, and mission? Eric Ries calls it “financial gravity” — an invisible force that pulls even the most principled companies toward corruption, and understanding it is the first step to resisting it.
In this episode, Eric Ries, entrepreneur and author of The Lean Startup and Incorruptible, shares why building a great company isn’t just about having a strong vision — it’s about building structures that protect that vision from external pressure. Eric revisits the core ideas behind the Lean Startup and MVP, explaining how the purpose of a minimum viable product is not to ship fast but to learn fast. He then introduces the central thesis of his new book: that the corruption we see in companies isn’t caused by bad people, but by a financial system that pulls organizations away from their values. Drawing on stories of Sol Price, FedMart, Costco, HEB, Novo Nordisk, and Anthropic, he shows that incorruptible companies are built through a combination of ethos — a deep operational commitment to doing right — and structural governance that resists outside pressure. He also unpacks how false metrics like OKRs can hollow out a company’s integrity over time, and how Mary Parker Follett’s concept of the “invisible leader” helps culture survive beyond any single founder or CEO.
Key topics discussed:
What “financial gravity” is and why even good companies fall to it
The true purpose of an MVP (hint: it’s not about shipping fast)
Why OKRs become dangerous false proxies over time
Blueprint for building a truly incorruptible company
Why Costco and Novo Nordisk resisted forces that killed FedMart
Mary Parker Follett’s invisible leader explained
Why Anthropic’s structure gives it a lasting competitive edge
How everyday decisions become acts of systemic change
Timestamps:
(00:00) Trailer & Intro
(02:31) What Two Mega-Trends Make Lean Startup More Relevant Than Ever?
(04:03) What Is the True Purpose of a Minimum Viable Product?
(11:04) Has AI Actually Made Building Software Cheaper and Better?
(13:41) What Two Stories Inspired the Book Incorruptible?
(20:38) What Is Financial Gravity and Why Does It Corrupt Even Good Companies?
(26:29) What Is Surrogation and Why Do OKRs Become Dangerous False Proxies?
(29:55) What Is the Blueprint for Building an Incorruptible Company?
(33:53) What Is the Invisible Leader and How Does It Keep Company Culture Alive?
(39:56) What Governance Structures Can Shield a Company’s Mission from Financial Gravity?
(48:27) Why Does Anthropic’s Unique Structure Give It a Competitive Advantage in AI?
(51:43) 3 Tech Lead Wisdom
_____
Eric Ries’s Bio
Over the last two decades, Eric Ries’s ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader’s Guide; and The Startup Way.
As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; Virgil, a legal services startup; and IMVU. On The Eric Ries Show, he talks with world-class technologists, thought leaders, and executives building for the long-term. He lives in the San Francisco Bay Area with his wife and three children.
Follow Eric:
LinkedIn – linkedin.com/in/eries
X – x.com/ericries
Podcast – www.ericriesshow.com
Website – incorruptible.co
Newsletter – news.theleanstartup.com

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Why Your AI Strategy Is Failing: The AI Paradox of Optimizing Coding Alone
May 18, 202660 minEpisode 258
What if faster coding is actually slowing your software delivery down? Most teams are pouring AI into the coding phase, but the real bottleneck is everywhere else.In this episode, Andrew Haschka, Field CTO at …
Show notes
What if faster coding is actually slowing your software delivery down? Most teams are pouring AI into the coding phase, but the real bottleneck is everywhere else.
In this episode, Andrew Haschka, Field CTO at GitLab for Asia Pacific and Japan, explains why most AI strategies in software engineering are failing and what it takes to fix them. He introduces the AI paradox: teams invest heavily in AI-assisted coding, yet coding accounts for less than 20% of the software delivery lifecycle, leaving the biggest bottlenecks untouched.
Andrew makes the case for intelligent orchestration — moving from isolated AI interactions to governed, end-to-end agentic flows that span planning, coding, testing, security, compliance, and release. He shares how a unified system of record forms the foundation for high-quality AI outcomes, and why fragmented tools and siloed context actively limit what AI can deliver. Drawing on real customer examples — including Ericsson’s 50% faster deployments and 130,000 hours saved in six months — he shows what a holistic approach actually looks like in practice.
The conversation also covers how tech leads, developers, and junior engineers need to evolve their skills in a world where AI handles routine implementation. Andrew closes with a compelling argument: in the agentic era, governance isn’t just a compliance burden, it’s the primary source of competitive advantage.
Timestamps:
(02:30) What Are the Key Responsibilities of a Field CTO at GitLab?
(03:26) Why Should Organizations Govern AI Strategy Rather Than Chase the Latest Features?
(06:41) Why Is an End-to-End Agentic Flow More Valuable Than Individual AI Tools?
(09:39) What Is the AI Paradox and How Does Intelligent Orchestration Solve It?
(14:47) How Does Shifting Focus to Requirements Quality Transform Software Delivery Outcomes?
(18:19) How Has GitLab Evolved Beyond CI/CD Into a Full End-to-End Delivery Platform?
(20:20) What Should Software Teams Prioritize Beyond Coding in the AI Era?
(24:14) How Do Organizational Silos Create a Capability Threshold for AI Adoption?
(27:49) What Practical Strategies Can Organizations Use to Break Down Internal Silos?
(30:58) How Did Ericsson Achieve 50% Faster Deployments and Save 130,000 Hours With GitLab?
(33:07) How Should Software Developers Evolve in the Age of AI Agents?
(36:26) How Is the Tech Lead Role Evolving in a Hybrid Human-AI Team?
(39:22) How Can Junior Developers Keep Up With the Rapid Shift in Industry Expectations?
(42:40) Why Do 79% of Singapore DevSecOps Practitioners Believe AI Will Create More Jobs?
(45:27) Why Are Companies Reducing Staff Despite the Growing Demand for Software?
(48:34) What Are the Most Common Pitfalls When Implementing Agentic Workflows?
(52:29) What Practical Steps Should Engineering Leaders Take to Govern AI Responsibly?
(55:13) Why Should Engineering Leaders Build an AI Strategy Before Choosing Technology?
(57:15) 3 Tech Lead Wisdom
_____
Andrew Haschka’s Bio
Andrew Haschka serves as Field CTO for Asia Pacific & Japan at GitLab, where he acts as a trusted strategic advisor to enterprise customers and partners navigating complex technology transformation. With over 20 years of experience spanning software delivery, cybersecurity, cloud infrastructure, and organisational transformation, Andrew brings a rare combination of technical depth and executive-level counsel to the organisations he works with.
Prior to GitLab, Andrew held senior leadership roles across APAC at Google and VMware, and has led large-scale digital transformation programmes for organisations including Downer, IBM, Jones Lang LaSalle, Thomson Reuters, Optus, and across the Fiji and Pacific Islands.
Follow Andrew:
LinkedIn – linkedin.com/in/andrewhaschka

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The Future of Code Review: Stop Reviewing Line-by-Line, Start Governing AI Agents
May 4, 20261 h 15 minEpisode 257
(07:22) Brought to you by MailtrapMailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their …
Show notes
(07:22) Brought to you by Mailtrap
Mailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their free tier! It also provides 24/7 support where you actually talk to real people, not an AI chatbot. Try Mailtrap for free at ⁠mailtrap.io⁠.

What does code review mean when AI writes most of the code? The answer isn’t to review more carefully. It’s a fundamentally different process, one built around rules, agents, and governance rather than diffs and comments.
In this episode, Itamar Friedman, founder and CEO of Qodo.ai, shares how AI is forcing a complete rethink of code review — from inline comments on code diffs to multi-agent governance systems that verify intent, architecture, and business logic at scale. He traces the evolution of code review through successive generations, explains why traditional static analysis is no longer sufficient, and lays out what a modern quality and governance layer actually looks like. Itamar also introduces the concept of “shift up” — extending quality checks into the planning phase so that technical product managers can contribute directly to shipping features — and explains how teams can move from vibe coding to viable, grounded development. The conversation also covers the race between AI labs, the role of open-source models, and a frank look at where the software developer role is heading by 2030.
Key topics discussed:
Why line-by-line code review doesn’t scale with AI-generated PRs
The generational evolution of code review tools (Gen 1 to 3.5)
How multi-agent systems surface only what needs human attention
Turning tribal knowledge into enforceable rules and skills
Shift-left and shift-up: embedding quality earlier in the workflow
What the new agentic code review UI will look like
Vibe coding vs. viable coding: the governance layer in between
Where the software developer role is headed by 2030
Timestamps:
(00:00:00) Trailer & Intro
(00:02:50) How Has AI Driven the Evolution of Code Review to Multi-Agent Systems?
(00:07:53) How Do We Move from Vibe Coding to Viable, Grounded Development?
(00:12:35) Are Traditional Static Analysis Checks Still Sufficient in the AI Era?
(00:16:27) How Do We Handle Exploding PR Volume Without Sacrificing Code Review Quality?
(00:22:11) How Do We Evolve Code Review from Simple Comments to Senior-Level AI Reviews?
(00:28:51) What Will the New Agentic Code Review UI Look Like?
(00:33:32) How Does Qodo Differentiate Itself as an AI Code Review and Governance Platform?
(00:37:15) What Do Shift-Left and Shift-Up Mean for the Future of Code Quality?
(00:41:23) How Do We Maintain Quality When Running Multiple AI Agents in Parallel?
(00:48:11) How Are Chinese AI Models Reshaping the Open-Source vs Closed-Source Race?
(00:55:25) Which AI Models Excel at Code Review, and Are We Heading Toward Specialization?
(01:03:16) Will Software Developers Still Be Needed as AI Automates More of Engineering?
(01:08:50) 3 Tech Lead Wisdom
_____
Itamar Friedman’s Bio
Itamar Friedman is the CEO and Co-Founder of Qodo, an AI code review platform used by 1M + developers. Before founding Qodo, Itamar was a founder of Visualead, which was acquired by the Alibaba Group. He then worked for Alibaba Group for 4 years as the Director of Machine Vision. Now, Itamar is dedicated to quality-first code generation.
Follow Itamar:
LinkedIn – linkedin.com/in/itamarf
X (formerly Twitter) – @itamar_mar
Qodo.ai – qodo.ai

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FeatureOps: The Safety Net You Need When Shipping with AI
April 27, 20261 h 5 minEpisode 256
(05:00) Brought to you by MailtrapMailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their …
Show notes
(05:00) Brought to you by Mailtrap
Mailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their free tier! It also provides 24/7 support where you actually talk to real people, not an AI chatbot. Try Mailtrap for free at mailtrap.io.

What happens when AI ships code faster than your team can review it? As agentic development accelerates your SDLC, the guardrails matter more than ever — and most teams don’t have them.
In this episode, Egil Osthus, CEO of Unleash, makes the case for FeatureOps as a strategic capability — not just a developer convenience. He explains the shift from a project mindset to a product mindset, where releases are decoupled from deployments and business outcomes matter more than shipping scope. Egil breaks down the four pillars of FeatureOps — gradual rollout, full stack experimentation, surgical rollback, and lifecycle management — and why each one becomes even more critical as AI-generated code flows faster into production. He also warns against building your own feature flag solution in-house, and shares what the rise of agentic development means for engineers who must now act as guardians of an oversight layer.
Key topics discussed:
Project mindset vs. product mindset in software delivery
The 4 pillars of FeatureOps and what each one solves
Why feature flags scare executives — and how to win them over
Decoupling deployment from release across Dev, PM, and Marketing
The danger of rolling your own feature flag solution
How local evaluation keeps feature flags fast and private
Blast radius management in an AI-accelerated SDLC
What vibe coders get wrong about day-two operations
Timestamps:
(00:00) Trailer & Intro
(02:36) What Is the Current State of Feature Flag Adoption Across the Industry?
(05:32) Why Is Feature Flag Adoption So Challenging Despite Its Apparent Simplicity?
(10:44) How Does FeatureOps Differ From CI/CD and Progressive Delivery?
(12:26) What Are the Four Core Pillars of FeatureOps?
(16:11) How Can Teams Shift the Perception of Feature Flags From Tactical to Strategic?
(20:46) How Do Feature Flags Align the Needs of Developers, Product Managers, and Marketing?
(25:09) How Do Organizations Effectively Define Responsibilities for Strategic Feature Flags?
(28:03) Does Using Feature Flags Enable Your Team to Deploy on Fridays?
(30:41) What Is Unleash and How Does It Scale for Enterprise Needs?
(34:54) What Are the Hidden Dangers of Building Your Own Feature Flag Solution?
(39:32) Why Are Local Evaluation and Privacy Core to Unleash’s Design?
(44:48) How Does the Rise of AI Impact the Evolution of FeatureOps?
(52:02) What Specific Guardrails Does FeatureOps Provide to Improve Safety?
(54:21) Can FeatureOps Platforms Use AI to Autonomously Manage Feature Rollouts?
(55:33) What Essential FeatureOps Advice Should Every Vibe Coder Follow?
(59:53) 3 Tech Lead Wisdom
_____
Egil Osthus’s Bio
Egil Østhus is the co-founder and CEO of Unleash, the world’s leading open-source feature management platform. As a seasoned enterprise technologist and product strategist, he operates at the cutting edge of business and software engineering.
Egil’s mission is to help technology leaders and businesses move beyond traditional DevOps by embracing FeatureOps, a new methodology that provides a critical safety net for the accelerating, and often risky, world of agentic software development. He has a unique ability to speak the language of both engineers and senior executives, making complex topics accessible and actionable.
Follow Egil:
LinkedIn – linkedin.com/in/egilconr
Unleash – getunleash.io

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Stop Vibe Coding: Spec-Driven Development with The BMad Method
April 20, 20261 h 16 minEpisode 255
What if vibe coding is the worst thing you could do with AI agents? The developers seeing the biggest gains aren’t prompting harder. They’re planning smarter, spec-first, and treating AI as a facilitator rather …
Show notes
What if vibe coding is the worst thing you could do with AI agents? The developers seeing the biggest gains aren’t prompting harder. They’re planning smarter, spec-first, and treating AI as a facilitator rather than a code generation engine.
In this episode, Brian Madison, creator of the BMad Method, shares how a year of late-night AI experiments led him to a structured, Agile-inspired approach to building software with AI agents. Brian explains why jumping straight into agent mode without upfront planning (what most people call vibe coding) reliably hits a wall, and how a disciplined spec-first workflow breaks through that ceiling.
He walks through the BMad Method’s core workflow: brainstorming, PRD, architecture, UX design, and context-rich user stories, each feeding into the next so the agent always has exactly what it needs. Brian also recounts a transformative two-week sprint he ran with his team where engineers were given permission to fail, and how that single experiment changed the way his entire organisation works with AI.
Finally, he reflects on what this shift means for the future of software engineering — where the unit of work is moving from tasks and stories to full features and epics, and every engineer can operate more like a tech lead.
Key topics discussed:
Why vibe coding hits a wall and how spec-driven dev fixes it
Using AI as a facilitator, not just a code generator
The BMad Method: PRD → architecture → context-rich stories
How a 2-week “no typing” sprint transformed his engineering team
Giving teams permission to fail as a leadership tool
The shift from user stories to epics as the unit of work
Why problem decomposition is engineers’ biggest AI superpower
Timestamps:
(00:00:00) Trailer & Intro
(00:02:44) How Did the US Army Shape Brian’s Journey into Software Engineering?
(00:06:35) How Can Engineers Overcome Imposter Syndrome and Build Self-Confidence?
(00:10:23) What Does BMad Actually Stand For?
(00:13:49) What Is the BMad Method?
(00:22:11) How Does BMad Approach Context and Spec Engineering?
(00:29:02) What Sparked the Creation of the BMad Method?
(00:44:55) What Productivity Gains Has the BMad Method Produced?
(00:48:36) How Will AI Change the Unit of Work for Software Engineers?
(00:55:51) How Does BMad Keep Specs and Code in Sync Over Time?
(01:01:01) What Is the Best Way to Get Started with the BMad Workflow?
(01:05:00) Which AI Models and Tools Does the BMad Method Support?
(01:08:21) 4 Tech Lead Wisdom
_____
Brian Madison’s Bio
Brian Madison is the creator of the BMad Method, an open-source framework that treats AI as a facilitator for workflows across any domain—software development, product management, operations, and beyond. Used globally, the BMad Method helps people work through complex processes using AI personas, from engineers driving spec-driven development to product managers crafting better PRDs and requirements.
Currently a Senior Engineering Manager at Extend, Brian led product engineering teams toward becoming an AI-native organization and now leads the entire AI SDLC transformation for the company, using the BMad Method as a framework, reimagining how AI flows through the full software development lifecycle.
Brian’s approach to leadership was forged during his service in the U.S. Army, where he learned the values of servant leadership, discipline, and mission-first execution.
Follow Brian:
LinkedIn – linkedin.com/in/bmadcode
BMad
Website – bmadcode.com
Docs – docs.bmad-method.org
GitHub – github.com/bmad-code-org/BMAD-METHOD
Discord – discord.gg/gk8jAdXWmj
YouTube – youtube.com/@BMadCode
X – x.com/BMadCode
Facebook – facebook.com/@BMadCode

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Why Incumbents Will Fall: How to Build a Hyperadaptive AI-Native Organization
April 13, 20261 h 4 minEpisode 254
Why do 80-95% of AI initiatives fail — and why is your organization’s structure to blame? Most companies are treating AI like a software upgrade, when it actually demands a complete rewiring of how work gets …
Show notes
Why do 80-95% of AI initiatives fail — and why is your organization’s structure to blame? Most companies are treating AI like a software upgrade, when it actually demands a complete rewiring of how work gets done.
In this episode, Melissa Reeve, author of Hyperadaptive and organizational change expert, shares a practical model for transforming legacy enterprises into AI-native organizations built to thrive — not just survive — in the age of AI. Drawing on her experience with the Toyota Production System, Scaled Agile, and deep research into leading AI adopters, Melissa argues that the real barriers to AI adoption are structural: Taylorist hierarchies, functional silos, and decision bottlenecks that organizations have never been forced to dismantle — until now. She introduces the Hyperadaptive model, a five-stage maturity path that gradually rewires how organizations operate, from establishing AI governance and identifying champions, to deploying agentic AI and organizing around customer value streams. Unlike past transformations, AI will compress both the strategy-to-execution and concept-to-delivery dimensions simultaneously — and the organizations that fail to adapt will be displaced by AI-native competitors rising far faster than Uber or Airbnb ever did.

Timestamps:
(00:00:00) Trailer & Intro
(00:02:50) How Did Melissa’s Background in Lean and Agile Lead to the Hyperadaptive Model?
(00:05:57) How Is the AI Revolution Different From Past Digital Transformations?
(00:07:39) Will AI-Native Companies Disrupt Incumbents the Way Airbnb and Uber Did?
(00:09:08) How Did the DevOps Model Inspire the Concept of Automated Execution Pipelines?
(00:12:41) What Is a Hyperadaptive Organization?
(00:14:10) Why Has AI Adoption Failed to Deliver Results in Most Organizations?
(00:17:05) What Are the Three Structural Barriers to AI Adoption?
(00:19:39) Why Is Taylorism Considered a Major Barrier to Becoming Hyperadaptive?
(00:22:48) What Are the Five Capabilities Required to Become Hyperadaptive?
(00:26:45) Why Does AI Make Age-Old Principles Like Lean and Agile More Relevant Than Ever?
(00:28:49) How Will the Human-in-the-Loop Role Evolve as Agentic AI Takes Over?
(00:32:52) How Should Organizations Start Transitioning from Functional Silos to Value Streams?
(00:35:07) How Is AI Enabling Adjacent Competencies and Expanding Professional Roles?
(00:38:43) Will AI Replace Workers or Unlock More of What Organizations Can Achieve?
(00:41:52) What Are the Five Stages of Maturity for Becoming Hyperadaptive?
(00:48:21) Why Do Most AI Implementations Fail When Organizations Skip the Foundation?
(00:50:55) What Does Dynamic AI Governance Look Like in Practice?
(00:55:20) How Does Kahneman’s Thinking Fast and Slow Explain the Human-AI Partnership?
(00:58:07) How Can AI Help Organizations Optimize for People, Profit, and Planet?
(01:00:24) 3 Tech Lead Wisdom
_____
Melissa Reeve’s Bio
Melissa Reeve creator of the Hyperadaptive Model and author of Hyperdaptive: Re-wiring the Enterprise to Become AI-Native. Hyperadaptive brings together process excellence, systems thinking, and the human side of AI integration to help leaders reimagine how their organizations learn and adapt.
Prior to leaning into AI, Melissa spent 25 years as an executive and Agile thought leader, which led to pioneering work in Agile marketing and her role as the first VP of Marketing at Scaled Agile and co-founding the Agile Marketing Alliance. She lives in Boulder, CO, with her husband, dogs, and chickens, where she enjoys hiking and gardening.
Follow Melissa:
LinkedIn – linkedin.com/in/melissamreeve
Website – hyperadaptive.solutions
Substack - https://intel.hyperadaptive.solutions/
Hyperadaptive - https://hyperadaptive.solutions/book

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How Vidio (Indonesia's #1 Streaming Platform) Built Great Engineering Culture — Now Supercharged by AI
April 6, 20261 h 30 minEpisode 253
What does it take to build a world-class engineering culture when you start with five engineers on minimum wage? Tommy Sullivan did exactly that at Vidio — and the team’s average tenure of seven years tells you …
Show notes
What does it take to build a world-class engineering culture when you start with five engineers on minimum wage? Tommy Sullivan did exactly that at Vidio — and the team’s average tenure of seven years tells you everything about whether it worked.
In this episode, Tommy Sullivan, CTO of Vidio (Indonesia’s largest streaming platform) shares how he built an engineering culture from almost nothing, growing a team of five to over two hundred using Extreme Programming principles and a relentless focus on hiring for attitude over aptitude. Tommy traces his journey from Pivotal Labs in San Francisco to the early days of Indonesia’s tech boom, explaining why Vidio survived when well-funded competitors like Hooq and iFlix all shut down.
Along the way, he gets into where AI has worked and where it has failed at Vidio, how the team is rethinking pair programming in the age of AI agents, what it takes to stream four terabytes per second during live events, and why protecting code quality is ultimately a culture problem, not a tooling one. Tommy also shares a hard-earned view on the agentic AI trend and why understanding the underlying mechanics matters more than chasing the hype.
Key topics discussed:
How Extreme Programming built Vidio’s 7-year average tenure
Hiring for attitude: why aptitude alone isn’t enough
Pair programming reimagined for the AI-agent era
Why code quality is a culture problem, not a tool problem
AI failures and wins at Vidio
How Vidio streams 4TB/s to 2.2M concurrent users
AVOD vs. SVOD: the model that saved Vidio
Vendor independence for CDN and AI — why it matters
What engineers need to understand about agentic AI
Timestamps:
(00:00:00) Trailer & Intro
(00:03:07) How Did Tommy Go From Silicon Valley to Jakarta?
(00:07:22) How Has Indonesia’s Tech Scene Evolved Over the Past Decade?
(00:13:12) What Happened to Indonesia’s Engineering Talent After the VC Bubble Burst?
(00:15:03) Why Is Indonesia One of the World’s Most Exciting Tech Markets?
(00:17:26) How Do You Build a World-Class Engineering Team When Starting From Scratch?
(00:22:01) What Are the Hidden Benefits of Pair Programming Beyond Code Quality?
(00:25:28) How Is AI Blurring the Lines Between Engineers and Product Managers?
(00:28:48) How Do You Justify XP Practices to a Results-Driven Business?
(00:36:11) What Has Worked and What Has Failed When Integrating AI at Vidio?
(00:44:19) Is AI an Amplifier or a Threat to Software Engineers?
(00:46:59) How Does Vidio Use Team Rotation and Shared Ownership to Retain Engineers?
(00:51:16) How Do You Protect Code Quality Culture in the Age of AI?
(00:54:16) What Metrics Actually Matter for Engineering Quality?
(00:58:07) How Will AI-Generated Content Reshape the Streaming Industry?
(01:06:51) What Does It Take to Stream at 4 Terabytes per Second?
(01:09:26) How Do You Keep a Streaming Platform Stable During Massive Live Events?
(01:14:12) How Did Vidio Survive When Other OTT Platforms Failed?
(01:18:15) Why Does Vendor Independence Matter for Both CDNs and AI?
(01:21:44) What Should Engineers Understand About the Agentic AI Trend?
(01:26:17) Tech Lead Wisdom
_____
Tommy Sullivan’s Bio
Tommy Sullivan leads the software engineering behind Vidio — Indonesia’s leading video-streaming platform. Before joining the Vidio / Emtek group, he helped startups and global enterprises implement agile engineering and lean product development practices in Silicon Valley and Southeast Asia. As a founding member of Vidio, Tommy shaped its early development and steered its evolution from a user-generated content platform to a premium streaming service supporting millions of subscribers. He leads with a focus on data-driven decisions and a humble, collaborative developer culture.
Follow Tommy:
LinkedIn – linkedin.com/in/tommybsullivan

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Why Senior Engineers Struggle as Tech Leads: The 3 Mindset Shifts That Fix It
March 30, 20261 h 4 minEpisode 252
Why do so many talented senior engineers struggle the moment they step into a tech lead role? Most of them are promoted based on their coding ability, but that same strength becomes a liability the moment they …
Show notes
Why do so many talented senior engineers struggle the moment they step into a tech lead role? Most of them are promoted based on their coding ability, but that same strength becomes a liability the moment they start leading a team.
In this episode, Anemari Fiser, tech lead coach and author of “Leveling Up as a Tech Lead”, shares the three mindset shifts that define the transition from senior engineer to effective tech lead: moving from an “I” to a “We” mindset, shifting focus from code to value, and trading short-term thinking for long-term impact. She explains why so many engineers hold on to coding out of fear, how to delegate without losing accountability, and why most technical problems are really people problems in disguise. Anemari also addresses how AI is reshaping the tech lead role and why the fundamentals of leadership still apply regardless of the tools your team uses.
Key topics discussed:
The 3 mindset shifts required for the transition to tech lead
Why your coding strength can hold back your team
How to let go of coding without losing your technical edge
Delegation secrets: setting expectations that actually stick
Influencing without authority — and when it’s not enough
How to measure your impact when results are hard to see
Leading your team through AI adoption without creating chaos
Timestamps:
(00:00:00) Trailer & Intro
(00:02:41) What Motivated Anemari to Write Her Book, Leveling Up as a Tech Lead?
(00:05:41) How Is the Tech Lead Role Defined?
(00:06:45) How Does the Engineering Manager Role Differ From a Tech Lead?
(00:09:37) Why Is the Transition to Tech Lead One of the Most Challenging Career Moves?
(00:14:21) How Can Tech Leads Shift From Short-Term to Long-Term Thinking?
(00:18:34) How Can Tech Leads Learn to Let Go of Writing Code?
(00:26:30) Why Is Every Tech Problem Actually a People Problem?
(00:30:52) How Can Tech Leads Delegate Effectively?
(00:37:18) How Can Tech Leads Influence Without Authority?
(00:40:37) Why Is Accountability Without Authority Unfair to Tech Leads?
(00:43:42) How Can Tech Leads Measure Their Impact?
(00:46:52) How Does AI Change the Role of a Tech Lead?
(00:52:26) Should Tech Leads Use AI to Get Back to Hands-On Development?
(00:55:33) How Can Tech Leads Stay Accountable for AI-Generated Code?
(01:00:26) With AI in the Mix, Is a Tech Problem Still Just a People Problem?
(01:01:10) 3 Tech Lead Wisdom
_____
Anemari Fiser’s Bio
Anemari Fiser is a tech leadership trainer, coach and O’Reilly author of Leveling Up as a Tech Lead. With over a decade in tech, she has coached 500+ engineers and trained 400+ tech leads worldwide, and shares practical leadership insights on LinkedIn with a community of 30,000+ tech professionals.
Follow Anemari:
LinkedIn – linkedin.com/in/anemari-fiser
Website – anemarifiser.com
Leveling Up as a Tech Lead – oreilly.com/library/view/leveling-up-as/9781098177508

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Design the System, Not the Hero: Building Trust in the AI Era
March 16, 20261 h 4 minEpisode 251
In a world where AI can build your MVP overnight, what actually gives you a lasting competitive edge? Andrew Stevens argues it’s not the software — it’s the data, the trust, and the systems you build around …
Show notes
In a world where AI can build your MVP overnight, what actually gives you a lasting competitive edge? Andrew Stevens argues it’s not the software — it’s the data, the trust, and the systems you build around them.
In this episode, Andrew Stevens, CTO of Sakura Sky and a technology leader with 30+ years of experience building, scaling, and selling companies, shares hard-won lessons from his journey across startups, enterprises, and AI ventures. He explains why product-market fit matters more than shipping fast, why data outlasts software as a competitive moat, and how leaders must design systems that don’t depend on their own heroics. Andrew also shares how a near-fatal accident reshaped his thinking on resilience, delegation, and what it truly means to build something that scales. From hiring for attitude over technical skill to building AI governance that accelerates rather than blocks innovation, this conversation is packed with practical wisdom for anyone leading in the AI era.
Key topics discussed:
Why data — not software — is your real moat in the AI era
What breaks when a startup scales past 10–100 people
How to make decision rights explicit to move faster
Design the system, not the hero: building beyond you
Hiring for resilience and attitude over technical skill
How governance can speed up AI adoption, not slow it down
What trustworthy AI agents actually require
Timestamps:
(00:00) Trailer & Intro
(02:45) What Breaks When You Scale a Startup From Zero to 100 People?
(08:44) Why Is Product-Market Fit More Important Than Building an MVP?
(17:20) How Do You Build a Lasting Moat in the AI Era?
(21:29) Why Must Leaders Learn to Let Go to Scale?
(23:27) What Can Leaders Learn From a Near-Fatal Motorcycle Accident?
(26:29) How Do Technical Leaders Stay Hands-On Without Becoming a Bottleneck?
(31:32) Why Should You Hire for Resilience Over Technical Skill?
(34:56) How Do You Build a Team That Innovates Safely in the AI Era?
(41:12) How Do You Build AI Governance That Speeds Up Innovation?
(47:37) Are AI-Driven Layoffs Justified or Just an Excuse?
(52:06) How Do You Build Trustworthy AI Agents?
(59:34) 3 Tech Lead Wisdom
_____
Andrew Stevens’s Bio
Andrew Stevens, CTO of Sakura Sky, is an executive leader and hands-on technologist who has scaled AI and cloud ventures from idea to acquisition. Based between Europe and the US, he blends deep expertise in cloud architecture, machine learning, and security with a track record in fintech, media, gaming, and AI.
Known for making complex tech relatable - often with pop-culture twists - Andrew brings sharp insights on AI guardrails, infrastructure resilience, and the creative edge humans hold in an AI-driven world. Whether advising founders, investing in early-stage startups, or speaking on global stages, Andrew helps audiences cut through the hype and focus on what matters most.
Follow Andrew:
LinkedIn – linkedin.com/in/andrewjstevens
Sakura Sky – sakurasky.com
The Executive AI Playbook – https://www.sakurasky.com/white-papers/ai-playbook/
Executive White Papers & Frameworks – https://whitepaper.download/

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Show notes & transcript: techleadjournal.dev/episodes/251.
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Why Coding Alone Is No Longer Enough: Become A Product-Minded Engineer
March 9, 20261 h 3 minEpisode 250
With AI generating code faster than ever, coding alone is no longer enough. The engineers who will stand out aren’t the ones who write the most code, but the ones who know what to build and why.In this episode …
Show notes
With AI generating code faster than ever, coding alone is no longer enough. The engineers who will stand out aren’t the ones who write the most code, but the ones who know what to build and why.
In this episode, Drew Hoskins, author of “The Product-Minded Engineer”, shares how engineers can develop the product thinking skills that will define their careers in the AI era. Drew draws on his experience as a senior staff engineer at Microsoft, Meta, and Stripe to explain why the best engineers care as much about the what and why as the how. He introduces the Double Diamond Framework (Discover, Define, Develop, Deliver) and calls out why most engineers make the mistake of jumping straight to the Develop phase. He also explains the concept of the “great re-indexing”: the mental shift required to switch between thinking like an engineer and thinking like a user. As AI takes over more of the routine coding work, Drew argues that product skills, people skills, and ownership skills are what will separate good engineers from truly impactful ones.
Key topics discussed:
What makes an engineer “product-minded”
Why engineers skip Discovery and what it costs them
The Double Diamond: a framework for building the right thing
How to think in user scenarios, not just system diagrams
The “great re-indexing” between engineer and user thinking
Why discoverability can 10x your feature’s impact for little cost
How AI is making product skills more valuable, not less
What junior engineers should focus on to stay relevant
Timestamps:
(00:00) Trailer & Intro
(02:35) What Is a Product-Minded Engineer?
(05:37) What Did Drew Learn Working at Microsoft, Meta, and Stripe?
(14:13) What Are the Biggest Challenges When Switching from Engineering to Product Management?
(16:33) What Skill Gaps Hold Engineers Back from Product Thinking?
(20:56) How Do You Bridge the Communication Gap Between Engineers and PMs?
(26:07) What Are The Four Pillars (Double Diamond Framework)?
(29:43) Why Should Engineers Care About the Deliver Phase?
(32:40) How Should Engineers Apply the Double Diamond Framework Day-to-Day?
(36:15) How Is AI Reshaping the Role of Product Engineers?
(40:06) Should Product Managers Learn to Code in the AI Era?
(43:56) What Is the Right PM-to-Engineer Ratio in the AI Era?
(45:48) How Should Engineering Leaders Respond to AI Productivity Pressure?
(51:04) What Advice Would You Give Junior Engineers Entering the Industry Today?
(55:17) What Other Topics Does the Product-Minded Engineer Book Cover?
(57:03) 3 Tech Lead Wisdom
_____
Drew Hoskins’s Bio
Drew Hoskins blends product, engineering, and storytelling in his work and writing. He is the author of The Product-Minded Engineer. As an engineer, Drew has helped design and build a wide range of innovative products and platforms for Microsoft, Meta, and Stripe.
Throughout his career, he has carried a passion for empowering developers. He’s founded and led several teams to major successes with developer platforms that have withstood the test of time. He’s currently a Staff Product Manager at Temporal Technologies, bringing durable execution to the masses.
He is an expert bridge player, having won a North American Championship in 2025, and lives in the beautiful and nerdy San Francisco Bay Area.
Follow Drew:
LinkedIn – linkedin.com/in/drewhoskins2
Newsletter – drewhoskins.substack.com
Product-Minded Engineer - https://www.oreilly.com/library/view/the-product-minded-engineer/9781098173722/
One-Page Bio – drewhoskins.carrd.co

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The MCP Security Risks You Can't Afford to Ignore
March 2, 20261 h 12 minEpisode 249
What if the MCP server you installed last week is silently leaking your emails to a stranger? The AI tools boosting your productivity could already be your biggest security liability.MCP (Model Context …
Show notes
What if the MCP server you installed last week is silently leaking your emails to a stranger? The AI tools boosting your productivity could already be your biggest security liability.
MCP (Model Context Protocol) has quickly become the standard for connecting AI agents to external tools and data sources. But as adoption accelerates, so do the risks – from malicious servers harvesting your credentials in the background, to local processes exposed to your entire network with no authentication. Most developers install MCP servers without fully understanding what code is running or who wrote it, creating serious supply chain and shadow IT problems inside organizations.
In this episode, Ariel Shiftan, CTO of MCPTotal, explains how MCP actually works, why there is a wide gap between its original design and how it is used in practice, and what that gap means for security. He also walks through real zero-days his team has discovered and shares practical advice for developers and enterprise leaders trying to adopt MCP without compromising their security posture.
Key topics discussed:
What MCP is and why it won the “USB for AI” race
Why most MCP servers are just API wrappers done wrong
Real zero-days found in popular, widely used MCPs
How malicious MCPs can silently leak your credentials
The supply chain risks hiding inside your dev toolchain
Why banning MCP in your org is the wrong move
Best practices for writing well-designed MCP servers
Why agent permission prompts need better security defaults
Timestamps:
(00:00:00) Trailer & Intro
(00:02:49) What Is MCP and Why Is It Called the USB for AI?
(00:07:22) How Does MCP Differ from Standard REST APIs?
(00:13:40) What Can AI Agents Do with MCP Beyond Reading Data?
(00:16:56) What Is RAG and How Did AI Evolve to Tool Calling?
(00:19:54) Why Is MCP Misused as an API Catalog and What Does That Cost?
(00:25:04) What Are AI Skills and How Do They Compare to MCP?
(00:30:29) How Does MCP Server Architecture Work Under the Hood?
(00:37:01) How Do Malicious and Vulnerable MCP Servers Put Organizations at Risk?
(00:45:30) What Real-World MCP Vulnerabilities and Zero-Days Have Been Found?
(00:50:30) How Should Enterprises Enable MCP Adoption Without Compromising Security?
(00:53:16) What Are Best Practices for Writing a Well-Designed MCP Server?
(00:59:14) How Should AI Agents Handle Permissions Without Overwhelming Users?
(01:05:26) 3 Tech Lead Wisdom
_____
Ariel Shiftan’s Bio
Ariel is a software engineer and security expert with more than 20 years of hands-on and executive leadership experience across cybersecurity, distributed systems, and AI infrastructure. He holds a PhD in Computer Science, specializing in advanced algorithms and systems. Earlier in his career, Ariel founded NorthBit, a deep-tech cybersecurity firm that was acquired by Magic Leap in 2016, where he led product security globally, overseeing the security lifecycle across more than 700 engineers. He has also led applied AI breakthroughs, including heading an XPRIZE-winning team that used deep learning to fight malaria in Africa.
Follow Ariel:
LinkedIn – linkedin.com/in/shiftan
MCPTotal’s Website – mcptotal.io

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Stop Telling Yourself You're Bad at “People Stuff”
February 23, 20261 h 15 minEpisode 248
Think you’re just “not a people person”? Most tech leaders quietly believe this about themselves, and it’s exactly what’s holding them back.In this episode, Martijn Versteeg, founder of peer leadership …
Show notes
Think you’re just “not a people person”? Most tech leaders quietly believe this about themselves, and it’s exactly what’s holding them back.
In this episode, Martijn Versteeg, founder of peer leadership community Group Effort and former CPTO with a background in organizational psychology, makes the case that it’s not: human behavior follows predictable patterns you can understand and work with, just like any system. The conversation covers a six-variable model for understanding what drives behavior and disengagement on your team, why popular personality tools like MBTI and DiSC often do more harm than good, and a clear structure for delivering bad news without the usual stress buildup. We also get into what it really takes to let go of hands-on coding when you move into leadership, why developing a product mindset matters even if product isn’t in your title, and the psychological risks of heavy AI use that most teams still aren’t thinking about.
Key topics discussed:
The 6 human needs that predict human behavior
Why MBTI and DiSC often do more harm than good
How to stop avoiding difficult conversations
Deliver bad news clearly using a 10-second rule
Why becoming a bottleneck is a slow career killer
Building a product mindset when you’re in tech
The mental health risks of heavy AI use
What peer groups give you that books can’t
Timestamps:
(00:00:00) Trailer & Intro
(00:03:06) Why Small Steps Matter More Than Career Turning Points
(00:05:11) About Martijn Versteeg
(00:07:01) How Can I Learn People Skills Systematically?
(00:13:19) Six Human Needs That Predict Behavior
(00:17:28) How Does It Compare to Maslow’s Hierarchy of Needs?
(00:19:49) Why Are Personality Tests Like MBTI Unreliable?
(00:23:20) How Do I Use Pain and Pleasure to Drive Growth?
(00:28:30) How Do I Handle Conflict and Difficult Conversations?
(00:32:47) A Model for Delivering Bad News in 10 Seconds
(00:36:12) How Do I Transition from Tech Lead to Engineering Leader?
(00:41:12) How Do I Let Go of Coding as a Leader?
(00:42:49) The Vanilla Orchid Story: Why Leaders Must Let Go
(00:46:55) How Can Engineers Develop a Product Mindset?
(00:53:17) What Are the Hidden Risks of AI for Mental Health?
(01:02:19) What Is the Value of Learning Through Podcast Conversations?
(01:07:19) Why Consuming Knowledge Is Not the Same as Producing
(01:09:06) 3 Tech Lead Wisdom
_____
Martijn Versteeg’s Bio
Martijn Versteeg is the founder of Group Effort, a Netherlands-based collective that empowers tech and product leaders across Europe through peer groups, offsites, and specialized training. As a key figure in the global product community, he is also an organizer of the Product Mastery Conference, where he helps curate insights for the next generation of product leaders.
Before founding Group Effort, Martijn built and successfully sold an EdTech IT platform and spent over five years as an Agile coach and Scrum Master. His unique perspective on leadership is rooted in high-performance athletics; at just 22 years old, he served as the National Rowing Coach for Singapore.
Today, Martijn is a vocal advocate for community-led learning. He frequently challenges leaders to move past the search for “golden nuggets” of wisdom and instead focus on the consistent, incremental iterations that solve the “hard people stuff” in scaling organizations.
Follow Martijn:
LinkedIn – linkedin.com/in/versteeg
Group Effort – groupeffort.nl
Newsletter – groupeffort.nl/newsletter
Free training on Massive Action-Taking for Product Leaders – groupeffort.nl/action

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Show notes & transcript: techleadjournal.dev/episodes/248.
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Why Your Platform Engineering Is Failing (And How to Fix It)
February 16, 20261 h 12 minEpisode 247
Is your platform engineering initiative struggling to deliver results? The problem might not be your tools or technology at all.In this episode, Sam Barlien, Community Organizer at Platform Engineering (the …
Show notes
Is your platform engineering initiative struggling to deliver results? The problem might not be your tools or technology at all.
In this episode, Sam Barlien, Community Organizer at Platform Engineering (the world’s largest platform engineering community), shares insights from speaking with nearly 400 engineering leaders last year about why their platform initiatives succeed or fail. The biggest revelation: it’s almost never about the tools. Sam explains why treating your internal platform like a product, complete with user research, documentation, and a product manager mindset, is the key differentiator between real platform engineering and just a rebranded operations team. He breaks down how to start small with a minimum viable platform, measure what actually matters, and build golden paths that developers want to follow. The conversation also covers how AI is both accelerating the need for platform engineering and transforming how platforms are built and operated.
Key Topics Discussed:
What platform engineering really means (hint: it’s product management)
Why DevOps and SRE often fail without product thinking
The “Golden Path” vs “Golden Cage” approach to developer experience
How to measure ROI and pitch platform engineering to executives
The symbiotic relationship between AI and platform engineering
Why starting with a Minimum Viable Platform beats big-bang transformations
PlatformCon 2025 key takeaways and emerging trends
Timestamps:
(00:00:00) Trailer & Intro
(00:03:16) What Background Do You Need for Platform Engineering?
(00:06:32) How Does Storytelling Help in Platform Engineering?
(00:08:53) What Is Platform Engineering?
(00:12:27) Why Are Organizations Adopting Platform Engineering?
(00:19:51) What’s the Difference Between DevOps, SRE, and Platform Engineering?
(00:23:25) Why Is the “Plug and Play” Approach to Tools a Trap?
(00:28:45) How Do You Pitch Platform as a Product Instead of a Project?
(00:34:01) How Do You Measure the ROI of Platform Engineering?
(00:40:42) What Is the Golden Path in Platform Engineering?
(00:47:12) What Were the Key Takeaways from PlatformCon 2025?
(00:53:41) How Does Platform Engineering Leverage AI?
(00:58:41) What Are the Hidden Costs of AI-Generated Code?
(01:04:01) Why Is Platform Engineering Actually Product Management?
(01:07:12) 1 Tech Lead Wisdom
_____
Sam Barlien’s Bio
Sam Barlien is a community organiser for the Platform Engineering Community. He is a tech nerd, and has been involved in tech communities for more than 10 years. He helps manage Platform Weekly, co-hosts PlatformCon, and drives the community Ambassador program, blog and Youtube channel.
Follow Sam:
LinkedIn – linkedin.com/in/sam-barlien-3b2579184
Platform Engineering – platformengineering.org
PlatformCon – platformcon.com
Weave Intelligence – weaveintelligence.io

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Show notes & transcript: techleadjournal.dev/episodes/247.
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Agnes AI: Southeast Asia's Answer to ChatGPT (And 20x Cheaper)
February 2, 20261 h 6 minEpisode 246
(05:13) Brought to you by Sweep AISweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at ⁠sweep.dev⁠.What if Southeast …
Show notes
(05:13) Brought to you by Sweep AI
Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at ⁠sweep.dev⁠.

What if Southeast Asia had its own ChatGPT that cost 20x less? Bruce Yang built Agnes AI to solve what global companies ignore: accessible AI for emerging markets.
In this episode, Bruce Yang, CEO and founder of Agnes AI, explains how he’s built Southeast Asia’s fastest-growing AI platform with 4 million registered users and 300K daily active users. After working at Microsoft and LinkedIn in Silicon Valley, Bruce returned to Singapore and started his PhD at NUS right before COVID, positioning him perfectly to ride the AI wave. Agnes AI uses smaller, specialized models trained on Southeast Asian languages and local user data to deliver productivity features like deep research, PowerPoint generation, and AI-powered group chats at 1/20th the cost of major competitors. We discuss the challenges of building AI for emerging markets, the importance of keeping humans in the loop for critical thinking, and why Bruce believes the future of AI belongs to applications, not just models.
Key topics discussed:
Making AI 20x cheaper than ChatGPT
Why Southeast Asia needs its own AI models
Using multi-agent systems to reduce hallucinations
AI group chats and social features
Critical thinking in an AI-assisted world
Why Agnes avoids the AI coding space
AI bubble debate: hype vs. real value
Getting emerging markets to adopt AI
Subscription vs. pay-per-use business models
Timestamps:
(00:00:00) Trailer & Intro
(00:02:49) Why Did Bruce Start a PhD During COVID to Build an AI Company?
(00:06:16) Why Build Another AI Model When Thousands Already Exist?
(00:09:48) How Is Agnes AI Cheaper and Faster Than ChatGPT?
(00:14:00) Does Agnes AI Support Southeast Asian Languages and Cultures?
(00:15:34) How Does Agnes AI Handle Local Languages Better Than Global Models?
(00:17:57) How Does Agnes AI Reduce Hallucinations?
(00:20:03) What Can Agnes AI Do That ChatGPT Cannot?
(00:25:31) Why Is AI in Group Chats the Next Big Thing?
(00:29:18) How Does Agnes AI Keep Your Private Group Conversations Secure?
(00:31:41) Will AI Make Us Lose Our Critical Thinking Skills?
(00:37:43) Should Children Use AI for Schoolwork?
(00:40:27) Can Agnes AI Help With Coding Like Cursor?
(00:43:07) Will Everyone Host Their Own AI Model in the Future?
(00:47:39) Is AI a Bubble or Real Economic Transformation?
(00:51:01) How Can Southeast Asians Start Using AI Today?
(00:53:56) What Are Real-World Examples of People Using Agnes AI?
(00:57:30) How Does Agnes AI Make Money While Offering Free Features?
(01:01:19) 3 Tech Lead Wisdom
_____
Bruce Yang’s Bio
Bruce Yang is the founder and CEO of Agnes AI, a consumer AI platform making intelligence more collaborative, creative, and accessible. A Raffles Institution graduate, he studied Math and Computer Science at UC Berkeley, earned a Master’s from HEC Paris, and is pursuing a PhD at NUS. He previously worked at Microsoft and LinkedIn in Silicon Valley.
Agnes AI redefines how people interact with AI through group chats, AI-assisted games, real-time content creation, slides generation, and research tools. Bruce envisions AI as a shared experience that amplifies human creativity and collaboration, enhancing rather than replacing human thinking and imagination.
Follow Bruce:
LinkedIn – linkedin.com/in/tongbruceyang
Agnes AI - https://agnes-ai.com/
Email – [email protected]

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Show notes & transcript: techleadjournal.dev/episodes/246.
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Your Home Is Launching Cyber Attacks (And You Don't Know It)
January 26, 20261 h 33 minEpisode 245
(05:22) Brought to you by CyberhavenAI is exfiltrating your data in fragments. Not one big breach — a prompt here, a screenshot there, a quiet export into a shadow AI tool. Every week, AI makes your team faster …
Show notes
(05:22) Brought to you by Cyberhaven
AI is exfiltrating your data in fragments. Not one big breach — a prompt here, a screenshot there, a quiet export into a shadow AI tool. Every week, AI makes your team faster and your data harder to see. Files are moved to new SaaS apps, models are trained on sensitive inputs, and legacy DLP is blind to the context that matters most.
On February 3rd at 11 am Pacific, Cyberhaven is unveiling a unified DSPM and DLP platform, built on the original data lineage, so security teams get X-ray vision into how data actually moves — and can stop risky usage in real time.
Watch the launch live at cyberhaven.com/techleadjournal.

Did you know Singapore is one of the world’s top countries launching cyberattacks? Not as a victim, but as the source. Your routers, smart TVs, robot vacuums, or network-attached storage could be part of a massive botnet right now.
In this eye-opening episode, Joseph Yap, founder of Otonata and cybersecurity expert, reveals the hidden cyber threat lurking in our homes. He reveals how everyday devices from routers to smart TVs become attack weapons. He explains why Singapore’s excellent infrastructure ironically makes it attractive for hackers and shares practical steps to protect your network. From residential proxies renting out your internet connection to teenagers running ransomware gangs, this conversation exposes the gap between our connected lives and our digital security practices.
Key topics discussed:
Why Singapore, Indonesia, and Vietnam are top cyberattack source countries
Why Singapore’s infrastructure makes it attractive for hackers
How 700,000+ compromised devices launch 30 terabits per second DDoS attacks
The rise of residential proxies and dark web rental of home networks
How hackers exploit publicly disclosed vulnerabilities in outdated firmware
Why AI is lowering the barrier to entry for hackers
What makes executives and high-net-worth individuals attractive targets
Practical steps to audit and protect your home network
Timestamps:
(00:00:00) Trailer & Intro
(00:02:40) How Can I Apply Journalism Skills to Tech
(00:06:14) Why is Curiosity Essential for Tech Leaders?
(00:08:48) Why is Singapore a Top Source for Cyber Attacks?
(00:12:11) What Makes Singapore Attractive for Cyber Attacks?
(00:16:39) How Many Devices in Singapore are Already Compromised?
(00:20:40) How Can I Tell if My Home Network is Compromised?
(00:30:13) Which Devices are Hackers’ Favorite Entry Points?
(00:33:18) What is a Residential Proxy and Why Should I Care?
(00:36:27) How do Hackers Actually Break into My Network?
(00:47:47) Why are Executives and High-Net-Worth Individuals Prime Target?
(00:55:12) Why isn’t Singapore’s Cyber Attack Problem in the News?
(00:59:26) Can Internet Providers Stop These Attacks?
(01:02:16) What Can I Do to Protect My Home Network?
(01:05:19) How Do I Protect My Network-Attached Storage (NAS)?
(01:10:41) How is AI Changing the Cyber Attack Landscape?
(01:17:35) How Can Otonata Help Protect My Home Network?
(01:23:39) What are Real-World Examples of Home Network Compromises?
(01:28:20) 3 Tech Lead Wisdom
_____
Joseph Yap’s Bio
With 20+ years in Operations and Supply Chain, Joseph Yap founded Otonata (https://otonata.com) after realizing how vulnerable home networks are to security breaches. Otonata brings corporate-grade cybersecurity to homes using digital hygiene and lean management principles, protecting dozens of households from growing threats posed by AI, smart devices, and expanding attack surfaces.
Follow Joseph:
LinkedIn – linkedin.com/in/-joseph-yap
Otonata – https://otonata.com/
Free Hack Check – https://otonata.com/hack-check

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Show notes & transcript: techleadjournal.dev/episodes/245.
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Gene Kim: How Vibe Coding Solved What I Couldn't in 13 YEARS
January 19, 20261 h 4 minEpisode 244
(06:23) Brought to you by Sweep AISweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at sweep.dev.Is the era of …
Show notes
(06:23) Brought to you by Sweep AI
Sweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at sweep.dev.

Is the era of writing code by hand coming to an end? Gene Kim explains how vibe coding solved problems he abandoned for 13 years and why the best days of coding might be ahead of us.
In this episode, Gene Kim shares his transformation from someone who hadn’t written production code in decades to building ambitious projects in minutes. He explains how meeting Steve Yegge and discovering vibe coding reignited his passion for programming.
Gene breaks down the FAAFO framework (Fast, Ambitious, Autonomous, Fun, Optionality) of vibe coding benefits and addresses the real risks of vibe coding, from deleted databases to corrupted repos. He emphasizes that developers need to shift from line cook to head chef, mastering delegation, architecture, and faster feedback loops. The conversation also explores whether AI will eliminate or expand developer roles, what skills matter most when hiring, and how organizations can build a vibe coding culture.
Key topics discussed:
Gene’s jaw-dropping a-ha moment solving his 13-year problem
The FAAFO framework for measuring vibe coding benefits
From line cook to head chef: the new developer skillset
Real risks and downsides of vibe coding
Will we need fewer developers or 10x more software?
Why feedback loops must be 100x faster than before
Building vibe coding culture across enterprise teams
Timestamps:
(00:00) Trailer & Intro
(03:13) What shaped Gene Kim’s career in DevOps and technology?
(07:26) How did Gene Kim’s books like Phoenix Project come about?
(09:55) What’s the story behind the Phoenix Project graphic novel?
(12:21) What was Gene Kim’s a-ha moment with vibe coding?
(14:41) How did Steve Yegge and Gene Kim collaborate on the book?
(21:06) What is vibe coding and how is it different from regular coding?
(25:57) What is the FAAFO framework for vibe coding benefits?
(32:08) Will AI replace software developers?
(36:10) What are the risks and downsides of vibe coding?
(41:51) What skills do developers need in the age of vibe coding?
(46:56) Why are feedback loops critical when using AI for coding?
(51:59) How can organizations adopt vibe coding as a culture?
(57:37) What should you look for when hiring developers in the AI era?
(59:45) 2 Tech Lead Wisdom
_____
Gene Kim’s Bio
Gene Kim is a WSJ bestselling author and researcher who has studied high-performing technology organizations since 1999. The founder and former CTO of Tripwire, he has authored several industry-defining books, including The Phoenix Project and The DevOps Handbook, with over 1 million copies sold. He also organizes the Enterprise Technology Leadership Summit.
Follow Gene:
LinkedIn – linkedin.com/in/realgenekim
Twitter – @RealGeneKim
IT Revolution – itrevolution.com
Vibe Coding - https://itrevolution.com/product/vibe-coding-book/

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Show notes & transcript: techleadjournal.dev/episodes/244.
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CTO Coach: Why Tech Companies are Really Laying Off Developers (It’s Not Just AI)
January 12, 20261 h 2 minEpisode 243
Why are tech companies really laying off developers? The uncomfortable truth has nothing to do with AI efficiency and everything to do with running out of ideas.In this episode, Stephan Schmidt, CTO coach and …
Show notes
Why are tech companies really laying off developers? The uncomfortable truth has nothing to do with AI efficiency and everything to do with running out of ideas.
In this episode, Stephan Schmidt, CTO coach and author of “The Amazing CTO’s Missing Manual,” shares a perspective on AI adoption that most tech leaders aren’t talking about. Developer layoffs aren’t about AI replacing jobs; they reveal a deeper problem. Product management has become a bottleneck, creating shallow features just to keep developers busy rather than driving meaningful innovation. When AI accelerates development, this bottleneck becomes impossible to ignore.
Stephan explains why architecture must be AI-ready before teams can benefit from AI tools, how CTOs can manage unrealistic business expectations, and why junior developers actually have a massive opportunity right now. He also challenges the common belief that vibe coding will democratize software development, explaining why you need to be a strong developer to prompt effectively.
Key topics discussed:
Why AI layoffs reveal companies ran out of good ideas
Architecture must be AI-ready for real productivity gains
Vibe coding only works if you’re already a strong developer
Product engineering roles will replace traditional developers
MCP connections unlock AI value beyond code generation
Juniors have huge advantage as AI-native engineers
Iterate on plans, not prompts, when using AI tools
CTOs can finally “rise and shine” using AI strategically
Timestamps:
(00:00) Trailer & Intro
(03:19) How do companies become truly AI-first?
(04:13) How should CTOs manage unrealistic AI velocity expectations?
(08:35) AI Use Cases Beyond Code Generation
(12:04) What is MCP and how does it unlock AI value?
(15:04) Why Developers Resist AI Adoption
(18:35) Are AI layoffs caused by a lack of product innovation?
(21:22) What is the future for junior developers in the age of AI?
(24:36) Critical Thinking and Moving Up the Abstraction Layer
(27:24) Vibe Coding: Benefits and Pitfalls
(31:59) What is the difference between a Developer and a Product Engineer?
(35:59) Building an Effective AI Adoption Strategy
(38:06) AI Adoption Strategy for Development Teams
(40:44) Avoiding the AI Tech Zoo
(44:48) How do tech leaders handle AI data privacy and security?
(50:31) How is the CTO role changing in 2026?
(57:23) 3 Tech Lead Wisdom

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Show notes & transcript: techleadjournal.dev/episodes/243.
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#242 - The End of Traditional Management: Reimagining Work for AI-First Organization - Jurgen Appelo
December 8, 20251 h 18 minEpisode 242
(04:11) Brought to you by JellyfishAI 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 …
Show notes
(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

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Show notes & transcript: techleadjournal.dev/episodes/242.
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