Root Cause

If everyone can build software now, what actually decides who survives? John Gallagher, Principal Engineer at Dynatrace and author of Software Design Simplified, says culture. In this episode of Root Cause we get to the root cause of what that word actually means, and why AI just made it ten times more important.

John has spent over 20 years in software, from assembly on microcontrollers to a decade of Ruby on Rails in production, and he lived through the incident we have all had: the app is down, the people who could help are asleep in another time zone, and the logs tell you nothing. He spent the next 18 months changing how his team saw its own system. Downtime dropped 98%. Then he cut the observability bill the new enthusiasm had created.

We dig into why 2,000 engineers with AI still move like 2,000 engineers (Conway's law did not get repealed), the anti-pattern that refuses to die ("we know what the customer wants, we built it"), and the paradox of founders: selfish enough to start a company, humble enough to remember you are solving somebody else's problem. John makes the case that a software company is not a factory stamping out features, and that if you deleted 80% of the features in most products, the software would get better. We get nostalgic about a 2 MB Amiga game on a 1.5 MB machine and what that obsession taught him about debugging, why group think starts with an emotion, and his three ingredients for learning critical thinking if nobody taught you as a kid: obsession, a small feedback loop, and for goodness sake, don't read a book. Plus the book recommendation he refused to tell me before we hit record.

00:00 Introduction of the Episode and the Guest
02:49 Is the AI SRE Market Flooded Because of AI or Because of OpenTelemetry?
07:46 Big Vendors Move Fast Now Too: Can Startups Still Compete?
13:46 Bureaucracy Is Not Good or Bad, It Is the Nature of the Beast
17:48 The Anti-Pattern That Never Dies: "We Know What the Customer Wants"
21:34 Founder Ego: Selfish Enough to Start, Humble Enough to Listen
25:14 Culture Is Now 10x More Important (and the Best Product Does Not Win)
29:11 A Software Company Is Not a Feature Factory
31:36 Rework: Just Let Me Use the Software
36:37 From Floppy Disks to AI: Is This the Same Kind of Revolution?
38:16 Hired Guns on a 1.5 MB Amiga: One Book, No Internet, Pure obsession
47:24 Neither Skeptic Nor Enthusiast: Subsidized Tokens and the Coming Shock
49:53 All Priors Are Gone: Relearning Everything After 20 Years
53:07 Did We Waste a Year Tinkering With AI Tools?
57:31 Invest in Things That Don't Change: Feedback Loops All the Way Down
01:01:51 "It's Definitely a Race Condition in Redis": How Engineers Handle Being Wrong
01:04:36 How to Learn Critical Thinking: Obsession, a Small Feedback Loop, and Don't Read a Book
01:07:57 The Painting Class and the Tennis Court: Emotions as a Feedback Loop

01:10:18 Group Think Starts With an Emotion, and Many More Much Smaller Steps
01:14:30 Book Recommendation: The Psychology of Software Teams
01:19:22 Question from Tamanna Haque and Question for the Next Guest
01:26:24 Closing

Find John at:

• Joyful Programming: https://www.joyfulprogramming.com
• LinkedIn: https://linkedin.com/in/synapticmishap

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/

What is Generative Engine Optimization (GEO), and does it replace SEO? In this episode of Root Cause we get to the root cause of what happens to being findable when people stop Googling and start asking ChatGPT, Gemini or Perplexity. With search engines, a human picks from a list of results. With AI search, the model gives one answer and one recommendation, and if you are not in it, you are not in the consideration set. That shift is what GEO (also called AI search optimization or AI visibility) is trying to address.

We cover GEO vs SEO and why good SEO is only a subset of AI visibility; the what, where and how of AI-readable content (which topics, which sites like Reddit, G2 and Capterra AI trusts, which formats); whether GEO feeds the AI slop machine (AI-generated content is no more readable to LLMs than human writing, and three purposeful articles beat ten); how fast-changing models affect what you optimize; why millennials using ChatGPT as a search engine and Gen Z using it as a companion are two different problems for a brand; B2B vs B2C vs creator use cases; and the fairness question of manufacturing a persona that ranks above the better product. Then the technical part: how you get a deterministic, credit-score-style AI visibility score out of non-deterministic LLMs, why the LLM should be a data extractor and never a grader, and practical GEO advice for content creators (know your proposition, get cited where AI trusts, transcribe your videos, and yes, bring back the blog).

My guest is Tamanna Haque, mathematician, lead data scientist with a decade of production AI on real customer data, and co-founder of GenSight.AI, a GEO platform she built in her personal time with her husband Jonas. This is her first public conversation about it, and I came in openly skeptical.

00:00 Introduction of the Episode and the Guest

02:56 Why Build a GEO Product in the Evenings: Eating Your Own Dog Food

06:08 What Is Generative Engine Optimization?

07:21 GEO vs SEO: Being an Option vs Being the Answer

10:48 The What, the Where and the How of Being Visible to AI

11:51 Does GEO Feed the AI Slop Machine?

15:05 Models Change Fast: Optimize Once or Forever?

17:02 Millennials Search, Gen Z Chats: Two Different Problems

20:46 B2B vs B2C vs Creators: Who Benefits Most?

22:35 Manufacturing a Persona: Who Deserves the Top Spot?

26:58 Deterministic Scores from Non-Deterministic Models

29:39 The LLM as Data Extractor, Never a Grader

31:44 Free Advice for Creators: Know Your Proposition, Bring Back the Blog

36:09 Book Recommendation: Harry Potter

38:05 Question from Fraser Merrifield: Top Movies

39:46 Question for the Next Guest: Which Pet Would You Have?

40:38 Closing

Find Tamanna at:

• GenSight.AI - https://gensight.ai

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/

In this episode of Root Cause we sit down with Fraser Merrifield, founder of FORM Careers in Glasgow and the self-described craft brewer of digital recruitment, to get to the root cause of the gap between what the hiring market says it wants and what it actually rewards. Fraser spent over a decade placing engineers in oil and gas, then bet on software in his late 30s, went fully solo, and now runs deliberately small on what he calls bullshit-free recruitment. He barely reads CVs anymore, and he'll tell you why a perfect one makes him suspicious.

We dig into why AI has made both hiring and getting hired harder while most of the horror stories stay myths: his clients average two interview stages, and the CV-rejecting AI mostly doesn't exist. Fraser explains the 12-month window on the "AI-native engineer" hype, what happens when an engineer burns through more in tokens than their salary, and why leaning on senior hires is the same mistake that left oil and gas with a talent shortage. And the story that sums up the whole market: 200 applications for one graduate role, one candidate picked up the phone, and he got the job, as the only person interviewed. Three years later he is still there.

00:00 Cold Open: Six Yes/No Claims About the Job Market

00:41 Introduction of the Episode and the Guest

03:35 Writing Like a Human: Pop Culture and Starting in Lockdown

05:56 Five Views or Five Million: How to Read Your LinkedIn Visibility

07:45 Everyone's CV Is Perfect Now, So What Stands Out?

09:23 The Online Mood Hoover: Positivity vs Ranting

12:22 The Blitz Round: Yes or No

13:06 Why AI Made Both Hiring and Getting Hired Harder

14:48 Just Another Hype Cycle? The 12-Month Window

16:11 When Engineers Burn More in Tokens Than Their Salary

17:53 Eight-Stage Interviews: Would You Even Want That Job?

20:36 Can Bullshit-Free Recruitment Scale?

22:06 CVs After AI: Rejecting the Quick-Appliers, Rewarding the Extra Mile

25:25 One of 200 Applicants Picked Up the Phone. He Got the Job

27:06 Glasgow AI and the Return of Offline Networking

29:10 The Undercurrent of Real People on LinkedIn

31:18 How Fraser Actually Uses AI (and How It Argued Back)

37:38 Fraser Flips the Script: The Most In-Demand Skill in Five Years

43:21 Juniors vs Seniors: The Oil and Gas Warning

46:11 Asking About Training Without Entitlement

47:17 Question from Evan Marshall: When Are We Getting Robots?

52:14 Question for the Next Guest: Top Five Movies

52:45 Movie Recommendations: Her, Ex Machina, Transcendence

54:24 Closing

Find Fraser at:

• LinkedIn - https://www.linkedin.com/in/fraser-merrifield-7b54524/

• Form Careers - https://weformcareers.com/

• Glasgow AI Meetup - https://www.meetup.com/meetup-group-xnqxwqzu/

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/
• Envimate: https://envimate.com/

In this episode of Root Cause we sit down with Evan Marshall, co-founder and CTO of Ito.ai, to get to the root cause of where the honest answer sits on the AI adoption spectrum, somewhere between the teams that won't let AI near their codebase and the ones that let it ship with barely a human in the loop. Most of us are somewhere in the middle, moving around and quietly wondering if we've got it right. This conversation is meant to help you gauge where you actually are, and where you want to be. Evan studied computer science at MIT and spent four years building privacy infrastructure for zero-knowledge blockchains, where a slipped bug is a disaster. That experience pushed him toward quality: he now builds a QA agent that actually runs your application and checks the code before it merges.

We dig into the pressure on Bay Area companies to claim a "software factory" while their engineers might still be shipping like everyone else, why free code generation can at most double you if coding was half your job (and it never was), and the plastic age of coding, where code is generated to be thrown away. Evan shares what a sane monthly AI spend actually looks like and his advice for integrating AI into your software development lifecycle: treat the SDLC as an optimization problem, because once generation is free, the bottleneck moves to verification and quality control. And when someone says "I can just do that with Claude Code", the work that follows is environments that have to heal themselves, caching that breaks on the next migration, and memory layers. As Evan puts it: we spent the tokens so you don't have to.

00:00 Pre-show Banter and the Cats

00:59 Introduction of the Episode and the Guest

03:00 Is the Bay Area Really an AI Echo Chamber?

05:45 The Pressure to Claim a Software Factory

07:36 How Much Does AI Actually Speed You Up?

10:23 How Ito Uses AI in Its Own Engineering

14:13 The Plastic Age of Coding

15:53 Can the Software Factory Be Productized?

18:39 Subscriptions vs API: The Real Cost of Tokens

20:19 Open Models and Switching Between Them

21:25 Are the Models Plateauing?

22:59 "I Can Just Use Claude Code": Where the Hard Parts Hide

26:19 The Bottlenecks You Don't See: Environments, Caching, Memory

28:55 Will QA Engineers Be Replaced?

31:39 Replicators, Waymos, and 3D Printers: Why Change Takes Time

36:49 Why SaaS Isn't Dead and How Much You Should Spend

39:23 Advice for Integrating AI into Your SDLC

42:50 Question from Barry O'Reilly: Building a Computer in 1822

44:31 Shared Reality, Social Media, and Cooperation

50:25 Book Recommendations

53:54 Question for the Next Guest and Closing

Find Evan at:

• LinkedId: https://www.linkedin.com/in/evan--marshall/

• his startup - https://www.ito.ai/

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/

In this episode of Root Cause we sit down with Barry O'Reilly, 25 years in software architecture, former chief architect at Microsoft, startup CTO, and the creator of Residuality Theory, to get to the root cause of what senior architects actually do when they say they run on gut feeling. When Microsoft asked Barry to teach junior architects how he did his job, he discovered he couldn't describe his own methods, and neither could any of the senior architects he asked. That frustration turned into two books and a PhD in complexity science. We dig into why a random simulation of stress produces architectures that survive events nobody predicted, why equirements and risk management quietly limit the very thinking they were supposed to support, why our industry spreads ideas through charisma instead of scientific proof, and whether an LLM can ever produce an architecture. If you've ever been told "that's just experience," this episode puts words on it.

00:00 Cold Open: The Architect vs the Implementer

02:06 Guest Introduction: Structuring the Magic

03:00 Pure Mathematics: Help or Trap?

06:20 Think Less, Do More? The Industry's Thinking Problem

09:09 Why Software Isn't Developing as a Science

12:26 Pop Culture, Gurus, and the Hype Machine

15:09 "My Industry Is Low Stakes": Why Quality Still Pays

17:54 Two Books That Decide If You're an Architect

22:09 Creativity vs Craft: Naming the Magic

25:39 Residuality Theory: The Marbles Analogy

31:59 How to Start: The Naive Architecture and Random Stress

36:35 Attractor States: Why Stressing Works

39:30 All Architecture Is Stress

44:14 The Trouble with Non-Functional Requirements

48:49 Should Architects Become Domain Experts?

52:02 Does This Apply to Small Systems and Startups?

57:36 Answering the Critics: "We've Always Done This"

01:01:28 Stressor Analysis: Architecture Never Stops

01:04:57 Shared Language and Reflective Practice

01:07:04 Has AI Made Code Cheaper?

01:12:54 Can LLM Agents Do Residuality?

01:18:43 The Walk Around the Problem Is the Point

01:20:40 First Steps into Residuality

01:25:18 Book Recommendations

01:29:39 A Benign Pride: Fly Fishing at 34 Meters

01:32:01 Question for the Next Guest and Closing

Find Barry at:

• LinkedIn: https://www.linkedin.com/in/barry-o-reilly-b924657/

• Barry’s first book: https://leanpub.com/residuality

• Barry’s second book: https://leanpub.com/architectsparadox

• The intro talk you should watch: https://www.youtube.com/watch?v=_MPUoiG6w_U

Find me (Nune) at:

* LinkedIn: https://www.linkedin.com/in/nisabek/

* Substack: https://www.thoughtfultechnologist.com/

In this episode of Root Cause we sit down with Eric Lubow, a CTO who treats organizations like distributed systems, which means the root cause of a broken team is usually a design problem, not a people problem. Eric has spent more than 20 years building and repairing teams and platforms, from co-founding SimpleReach to running engineering through dozens of acquisitions at Thrasio, and he is now Chief Product and Technology Officer at Mapp. He is also a jiu-jitsu coach, and that shapes how he leads. We get to the root cause of what actually makes a team healthy, why becoming a manager means changing your definition of done, and why ceding control is the part nobody warns you about. We talk about leading AI agents the way you would lead a person, why silent heroes quietly turn into silent burnouts, and how to hire into a team instead of into a vacuum. Honest and specific, with none of the leadership-content platitudes, including the lonely parts of the job most people at the top never say out loud.

00:00 Introduction: What Makes a Healthy Team

03:05 Jiu-Jitsu, Languages, and a Healthy Ego

08:23 Martial Arts, Balance, and "Everything Is Maintenance"

13:12 Teams as Distributed Systems and Conway's Law

16:38 The IC-to-Manager Transition: Ceding Control

19:10 Delegation and a New Definition of Done

22:24 Giving Away Your Legos

23:49 Can You Learn to Be a Manager?

25:21 Don't Just Become the Opposite of Your Bad Manager

27:54 Leading AI Agents Like You Lead People

31:55 Will AI Replace People? Force Multiplier, Not Replacement

36:45 What Actually Makes a Team Healthy

38:56 Adoption Curves and Why Teams Mimic Leaders

42:06 A Culture of Sharing vs Shadow IT and Shadow AI

47:41 The Hero Problem: Silent Heroes, Silent Burnout

53:20 Burnout, Boundaries, and Trusting the Team

55:33 Hiring Into a Team, Not a Vacuum

1:02:15 The Loneliness of Leadership

1:06:48 Why "I Don't Know" Is a Sign of Strength

1:09:13 Book Recommendations: Sci-Fi and Leadership

1:13:49 What Eric Would Tell His Younger Self

1:15:45 The Most Benign Thing You're Proud Of

Find Eric at:

• Linkedin: https://www.linkedin.com/in/eblubow/

• His blog: https://eric.lubow.org (which heavily influenced this episode)

• Beyond the Belt podcast: https://www.youtube.com/@beyondthebeltpod

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/

In this episode of Root Cause we sit down with Orel Zilberman, the solo builder behind WriteStack, the biggest tool out there for Substack creators. Orel spent 599 days shipping around ten products that made zero dollars, then picked one thing, stuck with it for six months, and turned it into a six-figure business. He has lived three versions of solo building in three years: before the current AI tooling, as it arrived, and now. We get to the root cause of what AI actually changed for one-person companies and what it didn't. Building got faster, but knowing what to build, and sticking with it long enough to find out if it works, is exactly as hard as it always was. Honest and unfiltered, including the messy parts most "become an entrepreneur" content leaves out.

00:00 Introduction to Root Cause and Solo Building

04:57 The Journey of Building WriteStack

06:42 What is SubStack and WriteStack?

09:28 "Why Bother" if the platform / big players will replace you

13:14 "Why bother" if AI will replace all SaaS

14:46 Knowing WHAT to Build and How to Distribute

18:30 Finding the Right Idea to Stick With

23:22 Learning from Opportunities and Failures

26:10 Navigating the Cold Start Problem

28:10 Leveraging AI Tools for Productivity

30:49 The Future of Software Engineering in the Age of AI

33:53 Advice for Aspiring Entrepreneurs

37:20 Being Better Than Most

38:49 Book Recommendations and Lifelong Learning

41:04 Reflections on Education and Career Choices

Find Orel at:

• His product - https://www.writestack.io/ 

• His journey - https://theindiepreneur.substack.com/ 

• Substack notes analysis - https://thewritingedge.substack.com/

• Substack profile - https://substack.com/@orelzilberman

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/
• Substack: https://www.thoughtfultechnologist.com/

In this episode of Root Cause we sit down with Ia Mg - seasoned engineer, former CS and digital literacy teacher at the Free University of Tbilisi, and the author of the blog Bits Complicated - to get to the root cause of learning itself, and whether the machines we've built are about to make us better at it or worse.

We dig into why everyone should understand technology even if they never write code, why domain knowledge has always mattered more than programmers wanted to admit, and what happens to learning when the answer is always one prompt away. Along the way: tech debt as a tax instead of a failure ("legacy as a service"), the "learning debt" that builds every time you accept a ready-made answer, why conversation-based coding is a process problem and not just an output problem, and why learning might be the most rebellious thing you can still do for yourself.

00:00 Guest intro and episode goal

02:45 Teaching Programming to Non-Coders

04:13 The Importance of Technology Literacy

09:41 AI and the Future of Programming

16:10 AI as a Teacher of Programming

22:16 Evaluating AI Responses in Learning

26:49 Concerns About the Next Generation

30:08 AI as an Abstraction in Programming

33:40 Conversational Development vs. Traditional Coding

37:34 Understanding Tech Debt

42:09 Learning Debt in the Age of AI

45:06 AI is the next social network?

46:47 Embracing the Chaos of Innovation

47:59 The Joy of Coding vs. AI

53:22 Navigating the Job Market in Tech

57:30 Book Recommendations

01:02:08 Summary of the Discussion

01:02:41 Learning as a Rebellious Act

01:04:09 Practical Recommendation on using AI consciously

01:06:21 Engineering is more than coding

Find Ia at: 

• Blog: https://iyamg.com/bitscomplicated/
• LinkedIn: https://www.linkedin.com/in/ia-mgvdliashvili-0b459768/ 

Find me (Nune) at:

• Blog: https://www.thoughtfultechnologist.com/ 
• LinkedIn: https://www.linkedin.com/in/nisabek/ 

In this episode of Root Cause we sit down with Adrian Hornsby — former AWS Principal Engineer, founder of Resilium Labs, and author of Why We Still Suck at Resilience — to get to the root cause of what's quietly breaking inside engineering organizations as AI absorbs more of the thinking. We dig into the gap between how we imagine our systems work and how they actually work, why that gap is where all the real learning lives, and what happens to a team when the thinking itself gets delegated to something that sounds confident but doesn't know which walls are load-bearing. 

00:00 Pre-show Banter and the Dog

01:08 Introduction of the Episode and the Guest

05:00 What's Wrong with the Name of the Show

07:35 The Importance of Learning from Successes

09:53 Defining Work as Imagined and Work as Done

13:43 Leading vs Being Hands-On

28:49 Learning From the Gap

30:39 Chaos Engineering

31:31 Load Testing

32:50 Game Days

34:00 ORRs - Operational Readiness reviews

34:41 Learning From the Incident

36:25 How AI has Affected the Gap

41:57 Navigating the Complexity of AI Delegation

43:45 Skill Atrophy and the New Generation of Engineers

47:12 Building Tools That Keep You Smart

52:18 We Need a Crisis to Slow Down

58:30 Building Intuition in AI-Driven Systems

01:03:17 The Human Touch in Technology

01:07:32 Facing Fears and Embracing Change

01:16:28 Question for the Next Guest and Closing

Adrian has generously decided to run a discount with the promocode “rootcausebynune” - for the first 30 copies you can buy his book for as low as 14.99 - that’s over 70% discount over the suggested price!

https://leanpub.com/whywestillsuckatresilience/c/rootcausebynune 

Find Adrian at:

• Resilium Labs: resiliumlabs.com
• LinkedIn: https://www.linkedin.com/in/adhorn/ 
• Substack: https://newsletter.resiliumlabs.com/

Find me (Nune) at:

• LinkedIn: https://www.linkedin.com/in/nisabek/ 
• Substack: https://www.thoughtfultechnologist.com/ 

In this very first episode of Root Cause we sit down with Marc Babin - an award-winning digital marketing professional and creator of over a dozen of podcasts - to get to the root cause of personal branding - why it matters more than ever and how a busy professional who doesn't like the empty talks can survive the content noise and still make themselves visible.

00:00 Show and Guest Introduction
02:54 The Value of Authentic Content in a Noisy World
06:18 AI and Content: Good Authentic Content is King
09:47 Reel-Thinking vs Podcast Creation
13:57 Creating Engaging Content in Niche Markets
18:14 Sales vs. Marketing: Building Trust Through Content
22:02 The Long Game in Content Creation
25:48 Personal Branding in the Digital Age
28:28 Setting Up for Success in Content Creation
33:05 Overcoming Perfectionism in Content Creation
38:27 Embracing the Silence of Early Content
43:29 Navigating Privacy and Online Presence
48:36 The Discomfort of Starting
53:14 The Root Causes of Expert Silence
57:53 How to Start Creating Content
01:00:30 Question for the Next Guest and Closing

Follow Marc Babin:
LinkedIn - https://www.linkedin.com/in/babinmarc/
The Podcast Blueprint Website - https://www.yourpodcastblueprint.com/
The Podcast Blueprint LinkedIn Page - https://www.linkedin.com/company/podcast-blueprint/

Additional Material mentioned in the episode:
The Podcast Consumer 2025 report from Edison Research - https://www.edisonresearch.com/wp-content/uploads/2025/07/The-Podcast-Consumer-2025-revised-FINAL.pdf
Read People Like a Book: How to Analyze, Understand, and Predict People’s Emotions, Thoughts, Intentions, and Behaviors By Patrick King - https://www.goodreads.com/en/book/show/56199402-read-people-like-a-book