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.

Tech Lead Journal

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

Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard.
In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall’s Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway’s Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart’s Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter’s Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field, his top five must-read books, and how he personally uses AI for research and planning rather than implementation.
Key topics discussed:

Gall’s Law: the trap of generating complex systems with AI
Conway’s Law now runs twice, on humans and on AI agent topology
Why software architects should help design your org chart
Goodhart’s Law and the danger of “tokenmaxxing” as a metric
The 90-90 rule: why AI’s last 10% still costs half the project
Dunning-Kruger effect on vibe coders and experts
Why less code beats more, even with AI writing it for free

Timestamps:

(00:00) Trailer & Intro
(03:02) What Inspired Milan to Write Laws of Software Engineering?
(05:32) How Did a Book on Software Laws Reach Such a Global Audience?
(06:49) How Should You Read the Laws of Software Engineering Book?
(08:51) Why Does the Book Cover People and Planning, Not Just Technical Laws?
(11:07) What Is Gall’s Law in Software Engineering?
(14:46) How Can You Apply Gall’s Law When Using AI?
(17:34) What Is Conway’s Law in Software Engineering?
(19:34) How Should Big Corporations Build New Products Without Structural Reorganization?
(21:26) How Does Conway’s Law Apply to Small AI-Powered Product Teams?
(23:25) What Is Goodhart’s Law in Software Engineering?
(25:52) What Are the Best Examples of Counterbalance Metrics for Leaders Today?
(28:06) What Kind of Outcomes Should You Actually Measure?
(31:01) What Is Hofstadter’s Law in Software Engineering?
(33:35) How Does Hofstadter’s Law Apply in the Age of AI?
(36:41) What Is the Dunning-Kruger Effect?
(40:10) How Does the Dunning-Kruger Effect Apply to Experienced Engineers Learning AI?
(42:46) Are Any of These Laws Becoming Less Relevant Because of AI?
(44:32) What Does the Lindy Effect Say About the Fate of Software Developers?
(47:49) What Is the Best Career Advice for Junior Developers Entering the AI Landscape?
(50:34) What Are the Top Five Must-Read Books for Software Engineers?
(55:46) How Can Software Engineers Apply These Laws in Their Daily Work?
(57:26) 3 Tech Lead Wisdom

_____
Milan Milanovic’s Bio
Milan Milanović is the CTO and the author of Laws of Software Engineering. He holds a PhD in Computer Science, has more than 20 years of experience across .NET, Azure, and mobile development, and is a Microsoft MVP. He runs Tech World With Milan, a software engineering newsletter and community followed by more than 400,000 engineers. He writes about architecture, engineering leadership, and how AI is changing the way software gets built.
Follow Milan:

LinkedIn – linkedin.com/in/milanmilanovic
Twitter / X – @milan_milanovic
Personal website – milan.milanovic.org
 Book’s website - lawsofsoftwareengineering.com
 Newsletter - newsletter.techworld-with-milan.com


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

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

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

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

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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Show notes & transcript: techleadjournal.dev/episodes/263.
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Ex-Gojek CTO: Why the Age of AI Rewards Builders as Judgment Becomes Expensive

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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Show notes & transcript: techleadjournal.dev/episodes/262.
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The AI Productivity Paradox: Why 10X Output Doesn't Mean 10X Business Outcome

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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The Hidden Stories Sabotaging Your Culture Change

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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Creator of Meta's Hack: Your AI Will Always Cheat — Here's How to Stop It

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

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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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Show notes & transcript: techleadjournal.dev/episodes/259.
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Eric Ries: Why Good Tech Companies Go Bad, and How to Stop It

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

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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


Like this episode?
Show notes & transcript: techleadjournal.dev/episodes/258.
Follow @techleadjournal on  LinkedIn, Twitter, and Instagram.
Buy me a coffee or become a patron.