Tech Lead Journal

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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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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Show notes & transcript: techleadjournal.dev/episodes/265.
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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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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
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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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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
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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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