Tech Lead Journal

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
Website – robsnyder.org
The Physics of Startups Newsletter – thephysicsofstartups.substack.com
The Physics of Startups YouTube – @rob-snyder

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Show notes & transcript: techleadjournal.dev/episodes/271.
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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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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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Show notes & transcript: techleadjournal.dev/episodes/269.
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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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Show notes & transcript: techleadjournal.dev/episodes/268.
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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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