Serverless Craic from The Serverless Edge

Serverless CrAIc Ep85 Why Team Topologies Matters More Than Ever in the AI Era

Send us Fan Mail [https://www.buzzsprout.com/1908105/fan_mail/new] Why Team Topologies Matters More Than Ever in the AI Era.  Are AI agents changing how software teams should be structured? In this episode of Serverless CrAIc, David Anderson, Mark McCann, and Michael O’Reilly explore one of the biggest questions emerging in the AI era: 👉 Does Team Topologies still matter when AI agents can generate code, tests, and workflows at incredible speed? The discussion dives deep into: Cognitive load in AI-driven engineering teams Socio-technical systems and AI adoption Why human collaboration still matters Stream-aligned teams in an agentic world The evolving role of platform teams Why enabling teams are more important than ever AI agents as “team members” — myth or reality? How engineering organisations scale safely with AI Why guardrails, standards, and architecture matter more now The balance between autonomy and control in AI-enabled organisations One key theme runs throughout the conversation: AI may accelerate software delivery — but the human systems around software are still critical. As development speeds increase, organisations must rethink: collaboration communication cognitive load organisational design engineering enablement platform strategy operational excellence This is a must-watch discussion for engineering leaders, architects, platform teams, and anyone building AI-enabled software organisations. Chapters 00:00 – Introduction 00:23 – AI, socio-technical systems, and Team Topologies 01:02 – Why cognitive load matters more in the AI era 02:07 – Drinking from the AI fire hose 03:20 – Shifting cognition from code to outcomes 04:32 – Why engineers are moving higher up the value chain 05:48 – DP1 vs DP2 organisational design principles 07:15 – Autonomy, mastery, and purpose in AI teams 08:50 – Are AI agents team members? 10:45 – Agent orchestration and organisational principles 11:44 – Why AI is not truly a “team member” 13:09 – Can you really pair program with AI? 13:52 – Stream-aligned teams in an AI world 15:34 – Jevons Paradox and accelerating software delivery 17:11 – The changing role of platform teams 18:46 – Security, governance, and AI platforms 20:31 – Why platform teams must stay ahead 21:08 – The critical role of enabling teams 22:32 – Coaching engineers to work effectively with agents 23:23 – AI anti-patterns and “We Jimmy” chaos engineering 24:54 – Complicated subsystem teams and deep expertise 27:20 – Does Team Topologies still matter? 28:06 – Constraints, guardrails, and organisational design 28:39 – Closing thoughts Resources & References 📘 Books & Concepts Mentioned Team Topologies — Matthew Skelton & Manuel Pais Cognitive Load Theory Socio-Technical Systems Team Design Interaction Modes Stream-Aligned Teams Platform Teams Enabling Teams Complicated Subsystem Teams Cynefin Framework Jevons Paradox Well-Architected Systems AI Agent Orchestration 📚 Key Themes AI engineering teams Organisational design AI agents and workflows Platform engineering Developer productivity AI adoption Engineering leadership Team structures in AI Guardrails and governance Human + AI collaboration 🌐 Learn more: https://theserverlessedge.com Serverless CrAIc from The Serverless Edge [https://www.theserverlessedge.com] Check out our book The Value Flywheel Effect [https://theserverlessedge.com/the-value-flywheel-effect/] Follow us on X @ServerlessEdge [https://twitter.com/ServerlessEdge] Follow us on LinkedIn [https://www.linkedin.com/company/the-serverless-edge/] Subscribe on YouTube  [https://www.youtube.com/channel/UCO3tqOCJdCDSDW0IBo133AQ]

Serverless CrAIc Ep84 AI-Generated Code Is a Liability: Technical Debt & Engineering Excellence

Send us Fan Mail [https://www.buzzsprout.com/1908105/fan_mail/new] Is AI-generated code creating more value — or more liability? In this episode of Serverless Craic, David Anderson, Mark McCann, and Michael O’Reilly explore why one of software engineering’s oldest principles is suddenly more relevant than ever in the age of AI: “Code is a liability. The system is the asset.” As agentic AI and code generation tools accelerate development, teams are producing more code, more tests, and more complexity than ever before. But: Does more code actually mean better outcomes? Are organisations creating massive technical debt without realising it? What happens when AI accelerates poor engineering practices? And how do you maintain confidence, security, and quality in probabilistic systems? This episode explores: AI-generated code and technical debt Validation, verification, and testing strategies Observability and evaluation frameworks Security vulnerabilities and unmanaged code Critical thinking in modern software engineering Why “lines of code” ≠ business value The return of XP and foundational engineering principles Chapters 00:00 – Introduction 00:24 – Old engineering principles returning in the AI era 00:51 – The return of Extreme Programming (XP) 01:43 – “Code is a liability” explained 02:46 – AI-generated code and growing technical debt 03:32 – Why engineers must review AI-generated code carefully 05:16 – The history of generated code and technical debt 06:28 – Why more code doesn’t mean more value 07:12 – AI hype, supply chains, and unmanaged complexity 08:30 – AI accelerates weak engineering practices 09:02 – Why teams still struggle with testing strategies 10:39 – Observability and deploying with confidence 11:54 – Evaluation frameworks for probabilistic systems 12:55 – System boundaries and verification 13:11 – Engineers are still accountable for AI-generated code 13:50 – Critical thinking in probabilistic systems 14:44 – Security vulnerabilities and unmanaged legacy code 16:27 – Commodity systems vs unnecessary custom code 17:25 – AI models finding security vulnerabilities 18:38 – Exploration, testing, and security charters 20:02 – Why code liability matters more than ever 20:24 – Engineering excellence as competitive advantage 20:44 – Final thoughts Resources & References 📘 Concepts & People Mentioned Ward Cunningham — Technical Debt Kent Beck — Extreme Programming (XP) Dave Farley — Continuous Delivery & modifiable systems Dan North — Engineering practices & architecture Elizabeth Hendrickson — “Testing = Checking + Exploring” 📚 Topics Discussed Technical debt AI-generated code Agentic AI workflows Evaluation frameworks (evals) Observability Continuous verification Security scanning Probabilistic systems Platform engineering Serverless architecture Engineering excellence Serverless CrAIc from The Serverless Edge [https://www.theserverlessedge.com] Check out our book The Value Flywheel Effect [https://theserverlessedge.com/the-value-flywheel-effect/] Follow us on X @ServerlessEdge [https://twitter.com/ServerlessEdge] Follow us on LinkedIn [https://www.linkedin.com/company/the-serverless-edge/] Subscribe on YouTube  [https://www.youtube.com/channel/UCO3tqOCJdCDSDW0IBo133AQ]

Serverless CrAIc Ep 83 Psychological Safety in the AI Era (No One Talks About This)

Send us Fan Mail [https://www.buzzsprout.com/1908105/fan_mail/new] Psychological Safety in the AI Era: AI is moving so fast it’s not just changing how we build software — it’s changing how teams think, learn, and work together. But there’s a problem no one is talking about enough: What happens to psychological safety when everything is changing at once? In this episode of Serverless CrAIc, Dave Anderson, Mark McCann, and Michael O’Reilly explore the human side of the AI revolution — from hype cycles and uncertainty to leadership, learning, and team dynamics. Because while AI is accelerating engineering, it’s also: Creating pressure to “keep up” Challenging confidence and expertise Shifting how teams collaborate and make decisions And without psychological safety, teams won’t question, won’t challenge — and won’t build well. “It’s psychologically exhausting trying to keep up with the pace of change.” This is a conversation about what it really takes to build high-performing, resilient teams in the AI era. Chapters 00:00 – Welcome to Serverless CrAIc AI hype, rapid change, and keeping up 00:31 – Why psychological safety matters in the AI era The difficulty of challenging AI in organisations 02:02 – The most aggressive hype cycle we’ve seen? Comparing AI to cloud and previous tech shifts 03:25 – The turning point in AI capability From hype to real engineering impact 04:17 – The psychological impact on engineers Why the pace of change is exhausting 04:49 – Innovation vs standards Why too much structure too early can slow teams down 05:37 – The four stages of psychological safety From inclusion to challenger safety 07:01 – The capacity problem Why senior engineers are struggling to mentor while learning themselves 07:38 – Sense-making in fast-moving environments How experienced engineers are adapting 09:00 – What skills matter now? Growth mindset, experimentation, and adaptability 10:45 – Bias for action Why experimenting with AI tools is critical 11:59 – Vulnerability, empathy, and humility Key leadership traits in uncertain times 13:23 – Confidence in core engineering skills Why experience still matters 13:58 – Demand isn’t slowing down Why engineers are busier than ever 15:15 – AI and engineering standards Applying world-class practices faster than ever 16:09 – Final thoughts Psychological safety as a leadership priority Key Themes Psychological safety in high-change environments AI hype vs reality in engineering teams The impact of rapid change on confidence and learning Leadership challenges in AI-driven organisations Growth mindset, experimentation, and vulnerability Applying high engineering standards with AI Resources & References Concepts and ideas mentioned in the discussion: Psychological Safety (Amy Edmondson – The Fearless Organization) Four Stages of Psychological Safety (Mutual respect → Challenger safety) Growth vs Fixed Mindset (Carol Dweck) Bias for Action (engineering and product principle) Well-Architected Frameworks (cloud and serverless design principles) Event-driven and serverless architectures Serverless CrAIc from The Serverless Edge [https://www.theserverlessedge.com] Check out our book The Value Flywheel Effect [https://theserverlessedge.com/the-value-flywheel-effect/] Follow us on X @ServerlessEdge [https://twitter.com/ServerlessEdge] Follow us on LinkedIn [https://www.linkedin.com/company/the-serverless-edge/] Subscribe on YouTube  [https://www.youtube.com/channel/UCO3tqOCJdCDSDW0IBo133AQ]

Serverless CrAIc Ep82 AI Is Changing Software Engineering — Why Your North Star Matters

Send us Fan Mail [https://www.buzzsprout.com/1908105/fan_mail/new] AI is dramatically increasing the speed at which teams can build software. But if you can ship features in hours instead of months, a new problem emerges: How do you know you’re building the right thing? In this episode of Serverless CrAIc, Dave Anderson, Mark McCann, and Michael O’Reilly explore why clarity of purpose and a strong North Star are more important than ever in an AI-accelerated world. As AI tools and agentic systems remove friction from development, teams can prototype, build, and deploy faster than ever before. But without clear direction, that speed can quickly turn into chaos, feature overload, and wasted effort. We discuss: Why the North Star framework still matters in the AI era The importance of leading vs lagging metrics How observability and telemetry support decision-making Why product management and engineering roles are shifting The growing need for product-oriented engineering teams If AI increases your delivery velocity, your strategy and decision-making must evolve just as quickly. Chapters 00:00 – Welcome to Serverless Craic AI everywhere and the coming singularity (maybe). 00:31 – Does the North Star still matter in the AI era? Why clarity of purpose becomes even more critical when you can build faster. 01:30 – Why speed without direction is dangerous How AI can lead teams to build the wrong things faster. 02:20 – Experience as an advantage in the AI era Why experienced engineers ask better questions of AI systems. 03:06 – The first North Star question: What game are you playing? Defining your problem space before building anything. 04:29 – Rapid experimentation with AI prototypes Using AI-driven prototyping to discover meaningful product signals. 05:41 – Observability hasn’t changed Why understanding what to measure is still the hardest problem. 06:32 – Leading vs lagging metrics How telemetry and instrumentation help teams track progress. 07:53 – The shift toward systems thinking Why engineers increasingly need a systems engineering mindset. 08:29 – Product management pressure in the AI era The growing importance of solving real customer problems. 09:27 – Wardley mapping, user needs, and rapid iteration Why product strategy becomes more important as teams move faster. 10:38 – The danger of overwhelming users with features Understanding organisational and user adoption limits. 11:01 – Decision-making speed in large organisations Why strategic decisions must flow faster through organisations. 11:55 – Engineering teams becoming product teams Autonomy and product ownership in high-velocity environments. 12:48 – Making good decisions against the North Star Why strong leadership and judgement still matter. Resources & References North Star Framework – aligning teams around a single product metric Leading vs Lagging Metrics – measuring immediate vs long-term outcomes Observability in modern systems – instrumentation and telemetry The Build Trap (concept discussed by product leadership thinkers) Wardley Mapping – understanding user needs and strategic positioning DORA Metrics – measuring engineering delivery performance Serverless CrAIc from The Serverless Edge [https://www.theserverlessedge.com] Check out our book The Value Flywheel Effect [https://theserverlessedge.com/the-value-flywheel-effect/] Follow us on X @ServerlessEdge [https://twitter.com/ServerlessEdge] Follow us on LinkedIn [https://www.linkedin.com/company/the-serverless-edge/] Subscribe on YouTube  [https://www.youtube.com/channel/UCO3tqOCJdCDSDW0IBo133AQ]

Serverless CrAIc Ep81 AI - differentiator or commodity?

Send us Fan Mail [https://www.buzzsprout.com/1908105/fan_mail/new] 🎙 AI: Commodity or Differentiator? | The Value Flywheel in 2026 AI has taken a step change. Over the past few months, adoption has accelerated dramatically — but are organisations applying it with clarity, or just chasing hype? In this episode of The Serverless Edge, we unpack: AI as a commodity vs differentiator Why clarity of purpose matters more than ever The risks of “vibe coding” critical systems Security, blast radius, and agent containment Accelerating your Value Flywheel safely with AI Why you cannot outsource critical thinking to an LLM If you're a CTO, architect, or engineering leader trying to navigate AI adoption without introducing systemic risk — this conversation is for you. ⏱ Chapter Markers 00:00 – The AI Step Change: What Happened in Late 2025? 02:00 – AI as Commodity vs Differentiator 03:10 – The Cost of Building What’s Already a Commodity 04:20 – Internal Acceleration vs Product Features 05:30 – Training Data, Sovereignty & Enterprise Risk 06:45 – Where AI Becomes Dangerous in Your Organisation 08:00 – Agentic AI & Blast Radius: Why Containment Matters 09:20 – Competing With the Platform Providers 11:10 – SaaS Killed by AI? The AWS Reinvent Effect 13:00 – “Vibe Coding” Core Business Systems (And Why That’s Madness) 15:00 – Where You Shouldn’t Experiment With AI 16:00 – Faster Feedback Loops & Engineering Throughput 17:30 – Discipline, Metrics & the North Star 18:20 – Using AI to Improve Your Own Thinking 19:00 – Context Is Everything (And Harder Than You Think) 20:10 – Organisational Design for Humans and Agents 21:00 – Turning the Flywheel Before Adding AI 21:50 – Why Sitting on the Sidelines Isn’t an Option 🔎 Key Themes 1. Clarity Before Capability Most organisations should consume AI, not build foundational models. The differentiator is rarely the LLM itself — it’s how clearly you understand: Your user needs Your value chain Your supply chain dependencies Your regulatory boundaries Without that clarity, AI simply accelerates confusion. 2. Blast Radius & Containment Agentic systems introduce a new risk profile. If you deploy AI into: Poorly governed SDLC environments Weakly defined security domains Legacy operational processes You expand blast radius unintentionally. Think SaaS isolation. Think sandboxing. Think containment by design. 3. Speed Changes Everything If AI compresses delivery cycles from weeks to hours: Your product discovery loop must accelerate Decision-making must tighten Metrics must be explicit Engineering must sit inside the feedback loop AI increases velocity — but velocity without direction is chaos. 4. You Cannot Outsource Critical Thinking AI can help: Refine your North Star Improve impact mapping Sharpen KPIs Draft Wardley Maps Analyse value chains But it cannot replace: Context Judgement Organisational alignment Strategic trade-offs You still need to do the hard yards. 📚 Related Concepts & Resources The Value Flywheel Effect Wardley Mapping North Star Framework Impact Mapping SDLC Acceleration Agentic AI Architecture Blast Radius & SaaS Isolation Models Serverless CrAIc from The Serverless Edge [https://www.theserverlessedge.com] Check out our book The Value Flywheel Effect [https://theserverlessedge.com/the-value-flywheel-effect/] Follow us on X @ServerlessEdge [https://twitter.com/ServerlessEdge] Follow us on LinkedIn [https://www.linkedin.com/company/the-serverless-edge/] Subscribe on YouTube  [https://www.youtube.com/channel/UCO3tqOCJdCDSDW0IBo133AQ]