Serverless Craic from The Serverless Edge

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May 2026

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Welcome to Serverless Craic from The Serverless Edge with Dave Anderson, Mark McCann and Mike O'Reilly. We want to share our tools and techniques so that you can use them to communicate your Technical Strategy with your C-Suite and business owners. We want to help you to build a serverless first organisation. We will show you how to use Wardley Mapping to gain situational awareness of where your cloud applications and business are. And then how to develop your technical capability in away that builds engineering standards to set your organisation up for sustainable success.Sounds like the tools and techniques that you need - then hit the subscribe!-ABOUT- Dave, Mark and Mike are senior technical architects/leaders passionate about driving technical strategy. They have led transformation journeys, technical excellence, cloud adoption and tech strategies in many industries.Active in various technologies including ML/AI, Public Cloud (IaaS, PaaS, SaaS), Engineering, Product, Cyber and UX.

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May 29, 202632 min

Serverless Craic Ep86 AI and Software Development - the Real Problem

Send us Fan MailAI and software development - the Real Problem with AI-Driven Software Engineering. AI is dramatically accelerating software delivery — but speed alone is not the answer.In this episode of Serverless CrAIc, we explore how AI is reshaping software engineering, platform engineering, architecture, and organisational design.As code generation becomes commoditised, the real differentiator is no longer how fast teams can build software — it’s whether they are building the right thing.We discuss:why clarity of purpose matters more than everhow AI amplifies both good and bad engineering practicesthe growing importance of socio-technical systemsplatform engineering and cognitive loadwhy North Star metrics still matterhow engineering leaders should think about AI adoptionthe risks of accelerating poor organisational decision makingIf your organisation is adopting AI into software delivery, this conversation is essential listening.Chapters00:00 Introduction01:42 AI is changing software engineering05:18 Why building faster is not enough09:34 The danger of accelerating bad decisions14:27 Why clarity of purpose matters18:40 AI as a commodity vs differentiator24:05 Platform engineering and cognitive load30:12 Socio-technical systems in the AI eraResources🌐 Website: The Serverless Edge https://theserverlessedge.com/📘 The Value Flywheel Effect: https://itrevolution.com/product/the-value-flywheel-effect/#o5a04b7992465🎧 Podcast Playlist: Serverless CrAIc Playlist https://open.spotify.com/show/5LvFaitkSkg2q5MWqKLrXu📰 Newsletter: The Serverless Edge on LinkedIN https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7066788643985596416Serverless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

May 22, 2026Episode 8529 min

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

Send us Fan MailWhy 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 teamsSocio-technical systems and AI adoptionWhy human collaboration still mattersStream-aligned teams in an agentic worldThe evolving role of platform teamsWhy enabling teams are more important than everAI agents as “team members” — myth or reality?How engineering organisations scale safely with AIWhy guardrails, standards, and architecture matter more nowThe balance between autonomy and control in AI-enabled organisationsOne 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:collaborationcommunicationcognitive loadorganisational designengineering enablementplatform strategyoperational excellenceThis is a must-watch discussion for engineering leaders, architects, platform teams, and anyone building AI-enabled software organisations.Chapters00:00 – Introduction00:23 – AI, socio-technical systems, and Team Topologies01:02 – Why cognitive load matters more in the AI era02:07 – Drinking from the AI fire hose03:20 – Shifting cognition from code to outcomes04:32 – Why engineers are moving higher up the value chain05:48 – DP1 vs DP2 organisational design principles07:15 – Autonomy, mastery, and purpose in AI teams08:50 – Are AI agents team members?10:45 – Agent orchestration and organisational principles11:44 – 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 world15:34 – Jevons Paradox and accelerating software delivery17:11 – The changing role of platform teams18:46 – Security, governance, and AI platforms20:31 – Why platform teams must stay ahead21:08 – The critical role of enabling teams22:32 – Coaching engineers to work effectively with agents23:23 – AI anti-patterns and “We Jimmy” chaos engineering24:54 – Complicated subsystem teams and deep expertise27:20 – Does Team Topologies still matter?28:06 – Constraints, guardrails, and organisational design28:39 – Closing thoughtsResources & References📘 Books & Concepts MentionedTeam Topologies — Matthew Skelton & Manuel PaisCognitive Load TheorySocio-Technical SystemsTeam Design Interaction ModesStream-Aligned TeamsPlatform TeamsEnabling TeamsComplicated Subsystem TeamsCynefin FrameworkJevons ParadoxWell-Architected SystemsAI Agent Orchestration📚 Key ThemesAI engineering teamsOrganisational designAI agents and workflowsPlatform engineeringDeveloper productivityAI adoptionEngineering leadershipTeam structures in AIGuardrails and governanceHuman + AI collaboration🌐 Learn more:https://theserverlessedge.comServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

May 15, 2026Episode 8421 min

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

Send us Fan MailIs 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 debtValidation, verification, and testing strategiesObservability and evaluation frameworksSecurity vulnerabilities and unmanaged codeCritical thinking in modern software engineeringWhy “lines of code” ≠ business valueThe return of XP and foundational engineering principlesChapters00:00 – Introduction00:24 – Old engineering principles returning in the AI era00:51 – The return of Extreme Programming (XP)01:43 – “Code is a liability” explained02:46 – AI-generated code and growing technical debt03:32 – Why engineers must review AI-generated code carefully05:16 – The history of generated code and technical debt06:28 – Why more code doesn’t mean more value07:12 – AI hype, supply chains, and unmanaged complexity08:30 – AI accelerates weak engineering practices09:02 – Why teams still struggle with testing strategies10:39 – Observability and deploying with confidence11:54 – Evaluation frameworks for probabilistic systems12:55 – System boundaries and verification13:11 – Engineers are still accountable for AI-generated code13:50 – Critical thinking in probabilistic systems14:44 – Security vulnerabilities and unmanaged legacy code16:27 – Commodity systems vs unnecessary custom code17:25 – AI models finding security vulnerabilities18:38 – Exploration, testing, and security charters20:02 – Why code liability matters more than ever20:24 – Engineering excellence as competitive advantage20:44 – Final thoughtsResources & References📘 Concepts & People MentionedWard Cunningham — Technical DebtKent Beck — Extreme Programming (XP)Dave Farley — Continuous Delivery & modifiable systemsDan North — Engineering practices & architectureElizabeth Hendrickson — “Testing = Checking + Exploring”📚 Topics DiscussedTechnical debtAI-generated codeAgentic AI workflowsEvaluation frameworks (evals)ObservabilityContinuous verificationSecurity scanningProbabilistic systemsPlatform engineeringServerless architectureEngineering excellenceServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

April 24, 2026Episode 8316 min

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

Send us Fan MailPsychological 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 expertiseShifting how teams collaborate and make decisionsAnd 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.Chapters00:00 – Welcome to Serverless CrAIcAI hype, rapid change, and keeping up00:31 – Why psychological safety matters in the AI eraThe difficulty of challenging AI in organisations02:02 – The most aggressive hype cycle we’ve seen?Comparing AI to cloud and previous tech shifts03:25 – The turning point in AI capabilityFrom hype to real engineering impact04:17 – The psychological impact on engineersWhy the pace of change is exhausting04:49 – Innovation vs standardsWhy too much structure too early can slow teams down05:37 – The four stages of psychological safetyFrom inclusion to challenger safety07:01 – The capacity problemWhy senior engineers are struggling to mentor while learning themselves07:38 – Sense-making in fast-moving environmentsHow experienced engineers are adapting09:00 – What skills matter now?Growth mindset, experimentation, and adaptability10:45 – Bias for actionWhy experimenting with AI tools is critical11:59 – Vulnerability, empathy, and humilityKey leadership traits in uncertain times13:23 – Confidence in core engineering skillsWhy experience still matters13:58 – Demand isn’t slowing downWhy engineers are busier than ever15:15 – AI and engineering standardsApplying world-class practices faster than ever16:09 – Final thoughtsPsychological safety as a leadership priorityKey ThemesPsychological safety in high-change environmentsAI hype vs reality in engineering teamsThe impact of rapid change on confidence and learningLeadership challenges in AI-driven organisationsGrowth mindset, experimentation, and vulnerabilityApplying high engineering standards with AIResources & ReferencesConcepts 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 architecturesServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

March 13, 2026Episode 8214 min

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

Send us Fan MailAI 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 eraThe importance of leading vs lagging metricsHow observability and telemetry support decision-makingWhy product management and engineering roles are shiftingThe growing need for product-oriented engineering teamsIf AI increases your delivery velocity, your strategy and decision-making must evolve just as quickly.Chapters00:00 – Welcome to Serverless CraicAI 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 dangerousHow AI can lead teams to build the wrong things faster.02:20 – Experience as an advantage in the AI eraWhy 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 prototypesUsing AI-driven prototyping to discover meaningful product signals.05:41 – Observability hasn’t changedWhy understanding what to measure is still the hardest problem.06:32 – Leading vs lagging metricsHow telemetry and instrumentation help teams track progress.07:53 – The shift toward systems thinkingWhy engineers increasingly need a systems engineering mindset.08:29 – Product management pressure in the AI eraThe growing importance of solving real customer problems.09:27 – Wardley mapping, user needs, and rapid iterationWhy product strategy becomes more important as teams move faster.10:38 – The danger of overwhelming users with featuresUnderstanding organisational and user adoption limits.11:01 – Decision-making speed in large organisationsWhy strategic decisions must flow faster through organisations.11:55 – Engineering teams becoming product teamsAutonomy and product ownership in high-velocity environments.12:48 – Making good decisions against the North StarWhy strong leadership and judgement still matter.Resources & ReferencesNorth Star Framework – aligning teams around a single product metricLeading vs Lagging Metrics – measuring immediate vs long-term outcomesObservability in modern systems – instrumentation and telemetryThe Build Trap (concept discussed by product leadership thinkers)Wardley Mapping – understanding user needs and strategic positioningDORA Metrics – measuring engineering delivery performanceServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

February 13, 2026Episode 8122 min

Serverless CrAIc Ep81 AI - differentiator or commodity?

Send us Fan Mail🎙 AI: Commodity or Differentiator? | The Value Flywheel in 2026AI 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 differentiatorWhy clarity of purpose matters more than everThe risks of “vibe coding” critical systemsSecurity, blast radius, and agent containmentAccelerating your Value Flywheel safely with AIWhy you cannot outsource critical thinking to an LLMIf you're a CTO, architect, or engineering leader trying to navigate AI adoption without introducing systemic risk — this conversation is for you.⏱ Chapter Markers00:00 – The AI Step Change: What Happened in Late 2025?02:00 – AI as Commodity vs Differentiator03:10 – The Cost of Building What’s Already a Commodity04:20 – Internal Acceleration vs Product Features05:30 – Training Data, Sovereignty & Enterprise Risk06:45 – Where AI Becomes Dangerous in Your Organisation08:00 – Agentic AI & Blast Radius: Why Containment Matters09:20 – Competing With the Platform Providers11:10 – SaaS Killed by AI? The AWS Reinvent Effect13:00 – “Vibe Coding” Core Business Systems (And Why That’s Madness)15:00 – Where You Shouldn’t Experiment With AI16:00 – Faster Feedback Loops & Engineering Throughput17:30 – Discipline, Metrics & the North Star18:20 – Using AI to Improve Your Own Thinking19:00 – Context Is Everything (And Harder Than You Think)20:10 – Organisational Design for Humans and Agents21:00 – Turning the Flywheel Before Adding AI21:50 – Why Sitting on the Sidelines Isn’t an Option🔎 Key Themes1. Clarity Before CapabilityMost organisations should consume AI, not build foundational models. The differentiator is rarely the LLM itself — it’s how clearly you understand:Your user needsYour value chainYour supply chain dependenciesYour regulatory boundariesWithout that clarity, AI simply accelerates confusion.2. Blast Radius & ContainmentAgentic systems introduce a new risk profile.If you deploy AI into:Poorly governed SDLC environmentsWeakly defined security domainsLegacy operational processesYou expand blast radius unintentionally.Think SaaS isolation. Think sandboxing. Think containment by design.3. Speed Changes EverythingIf AI compresses delivery cycles from weeks to hours:Your product discovery loop must accelerateDecision-making must tightenMetrics must be explicitEngineering must sit inside the feedback loopAI increases velocity — but velocity without direction is chaos.4. You Cannot Outsource Critical ThinkingAI can help:Refine your North StarImprove impact mappingSharpen KPIsDraft Wardley MapsAnalyse value chainsBut it cannot replace:ContextJudgementOrganisational alignmentStrategic trade-offsYou still need to do the hard yards.📚 Related Concepts & ResourcesThe Value Flywheel EffectWardley MappingNorth Star FrameworkImpact MappingSDLC AccelerationAgentic AI ArchitectureBlast Radius & SaaS Isolation ModelsServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

February 9, 2026Episode 8034 min

Serverless CrAIc Ep 80 AI Myths in Software Engineering

Send us Fan MailAI Myths in Software Engineering. AI is colliding with software engineering at full speed — and a lot of myths are emerging along the way.In this episode of Serverless Craic, Dave Anderson, Mark McCann, and Michael O’Reilly unpack how AI, GenAI, and agentic systems intersect with the ideas behind The Value Flywheel Effect. Rather than hype or fear, this is a grounded engineering discussion about quality, responsibility and long-term value.We explore six common myths about AI and software engineering — and why good engineering judgement, domain knowledge, and clarity of purpose matter more than ever.If you care about building sustainable systems, not just shipping demos, this one’s for you.Chapters00:00 – Welcome & contextWhy AI + serverless + the Value Flywheel is colliding right now01:50 – Myth 1: “Software engineering is dead”Why engineering skills are more valuable, not less07:06 – Myth 2: “My skills will become irrelevant”Moving up the value chain, domain expertise, and growth mindset13:40 – Myth 3: “The quality isn’t good enough”Standards, constraints, and why worst it’ll ever be is today18:44 – Myth 4: “The model understands the problem”Pattern matching vs understanding, context, and critical thinking24:20 – Myth 5: “I’ll be forced to use AI”Workflows, guardrails, security, and excessive privileges31:54 – Myth 6: “We’ll need fewer engineers”Jevons Paradox, lowered barriers, and the coming demand explosion34:22 – Closing thoughtsAI, velocity, and the future of sustainable software engineeringKey Themes DiscussedAI as an abstraction layer, not a replacement for engineeringWhy standards, constraints, and operability still matterDomain-Driven Design as AI-amplifying, not obsoleteAgentic systems, skills, prompts, and containmentSecurity risks: excessive privileges & supply-chain concernsVelocity vs sustainability in AI-assisted developmentResources & ReferencesThe Value Flywheel Effect – principles referenced throughoutWardley Mapping & situational awarenessDomain-Driven Design (DDD)OWASP Top 10 for LLMs (excessive privileges, agent risks)Jevons Paradox (efficiency driving increased demand)Early cloud cost & governance parallelsThreat modelling for AI and agentic systemsServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

January 16, 2026Episode 7935 min

Serverless Craic Ep79 — Reflecting on The Value Flywheel Effect (5 Years On)

Send us Fan MailIn the first Serverless Craic episode of 2026, Dave Anderson, Mark McCann, and Michael O’Reilly reflect on a five-year journey that began in early 2021 with the idea for The Value Flywheel Effect.This episode closes out the book series by looking back—warts and all—at what it really took to write, publish, promote, and apply the ideas in practice. The conversation spans writing fatigue, editing realities, imposter syndrome, enterprise adoption, and why the flywheel is arguably more relevant than ever in an AI-first world.If you care about modern software delivery, cloud strategy, serverless-first thinking, and leading technology change, this one is for you.⏱️ Chapters00:00 – Welcome & contextEpisode 79, first show of 2026, and closing out the book series 01:20 – How the book started (2021 → 2026)From an idea to a five-year journey01:35 – Did we enjoy writing the book?Ideation, Guinness-fuelled drafts, and the reality of writing02:30 – Shaping the narrativeWhy writing is harder than it looks, and why shared context doesn’t scale04:10 – Atomic essays & capturing thinking earlyGitHub, short-form writing, and building habits05:00 – Would this book have helped us 15 years ago?Modernisation gaps, agile limits, and why the flywheel mattered06:00 – The editing process (and thick skin)What professional editors really do to your manuscript07:40 – Feedback, criticism, and author psychologyWhy the one negative comment sticks09:15 – Has the book made an impact?Enterprises, conferences, and unexpected adoption stories12:40 – Applying the flywheel in real organisationsNorth Stars, Team Topologies, serverless-first in practice14:30 – The hardest part of the whole journeyFinishing, introductions, and the truth about selling a book17:30 – Promotion, modesty, and imposter syndromeWhy marketing a book is a full-time job19:00 – Influences & supportersKent Beck, Adrian Cockcroft, Simon Wardley, and standing on shoulders21:45 – Is the book still relevant in the age of GenAI?Why the flywheel + AI is a force multiplier23:00 – AI, context engineering, and agentic systemsUsing codified principles to guide AI effectively25:30 – Lowering the barrier to good practiceHow AI helps teams apply architecture, security, and governance29:00 – Business strategy vs technical strategyIs the divide finally disappearing?31:45 – The emerging “builder” personaShifting left, shifting right, and new SDLC realities34:50 – Closing thoughts & what’s nextAISDLCs, Brownfield challenges, and future episodes📚 Resources & Links📘 The Value Flywheel Effect — principles for modern cloud and serverless transformation🌐 The Serverless Edge: https://theserverlessedge.com🎥 Subscribe on YouTube for weekly Serverless Crack episodes💬 Follow the conversation on LinkedIn💡 Key TakeawaysWriting a book is much harder than most engineers expectThe flywheel was never about tech or business—it’s about bothAI makes codified principles (North Stars, well-architected practices) more valuable, not lessCritical thinking remains non-negotiable, even with powerful modelsContext is now a first-class architectural concern👍 If you found this useful, like, subscribe, and share with your team.💬 Let us know in the comments how you are applying the flywheel—or where it’s challenged you.Cheers,The Serverless EdgeServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

December 11, 2025Episode 7811 min

Serverless Craic Ep78 How the BBC Built a Serverless-First Architecture at Global Scale Case Study

Send us Fan MailHow the BBC Built a Serverless-First Architecture: In this episode of Serverless Craic, Dave, Mark, and Michael explore one of the most compelling real-world examples of serverless at scale: the BBC’s serverless-first transformation.We break down how the BBC News engineering team delivers global, highly-spiky traffic, meets strict public-service constraints, reduces incidents, and accelerates delivery—all with a pragmatic serverless-first mindset.Expect insights on production readiness, architectural constraints, continuous delivery, problem prevention at scale, and how the BBC evolved a massive digital estate by keeping things intentionally simple.⏱️ Chapter Markers00:00 – Intro00:03 – Welcome to the episode00:21 – BBC case study overview02:11 – Complexity, scale and global distribution03:42 – User experience, design systems, and history of BBC transformation05:03 – Serverless-first and focusing on differentiating value05:47 – Spiky traffic, transcoding and pragmatic trade-offs06:42 – Constraints and why serverless works for the BBC07:59 – Team size, reducing maintenance load, and continuous delivery08:27 – Serverless-first, not serverless-only09:05 – BBC traffic levels and operational performance09:47 – Problem prevention, reliability and long-term value10:02 – Improvements in BBC Media Player and user experience10:06 – Evolution, complexity, and Gold’s Law11:01 – Closing thoughts and what’s coming next11:08 – Outro & Call to action🔧 Resources & MentionsBBC Article – Delivering BBC Online Using Serverless https://www.bbc.co.uk/articles/clynq1gyn1roThe Serverless Edge – The Value Flywheel Effect Frameworkhttps://theserverlessedge.com/12-key-tenets-of-the-value-flywheel-effect/Gall's Law – Complex systems evolving from simple systemshttps://en.wikipedia.org/wiki/John_Gall_(author)Adrian Cockcroft – Serverless constraints and production readinesshttps://medium.com/@adriancoAWS Serverless Best Practiceshttps://builder.aws.com/content/2pYmkuLReVaqZ29ew1P3Dn4iAvH/best-practices-for-serverless-technologies-in-awsServerless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube

December 5, 2025Episode 7722 min

Serverless Craic Ep77 Mapping the Emerging Value – Chapter 19 | The Value Flywheel Effect

Send us Fan MailIn this episode of The Serverless Craic, Dave, Mark, and Michael dive into Chapter 19: Mapping the Emerging Value from The Value Flywheel Effect. This chapter explores how leaders can use Wardley Mapping to uncover long-term strategic opportunities, align product thinking, and identify “land grab” moments that unlock future business value.The conversation covers:How mindset, purpose, and cloud strategy form organisational pipelinesThe three major value chains: sustainable operations, long-term goals, and land-grab opportunitiesHow emerging capabilities, product thinking, and situational awareness combine to create competitive advantagePractical leadership behaviours and gameplay patterns from Wardley MappingExamples from AI, cloud evolution, open source, and platform teamsWhy high-performing engineering organisations create the conditions for long-term innovationThis is a deep dive for technology leaders who want to create adaptive, strategically aligned organisations capable of sensing and seizing new market opportunities.📍 Chapters00:00 – Intro00:12 – Setting the context: Chapter 1901:00 – Why long-term value isn’t just architecture01:50 – The future CEO as an anchor for the map02:25 – The three organisational pipelines03:30 – Sustainable operations value chain04:40 – Long-term goals and product mindset06:33 – Psychological safety, experimentation & ambition07:30 – Run → Grow → Transform09:05 – Ineffective innovation & product debt11:32 – The “land grab” and adjacent market opportunities12:51 – Cloud, customer obsession & competitive advantage13:57 – Revisiting the map15:13 – Wardley Mapping gameplay patterns16:20 – FUD and competitive games18:01 – Open source as an accelerator18:33 – Market enablement in the AI era19:07 – Toxicity, constraints & real-world leadership20:14 – Sensing engines, co-creation & alliances21:03 – Competitive moves: talent raids & fast-followers22:11 – Wrapping up & next steps🔗 Resources & Links📘 The Value Flywheel Effect Bookhttps://itrevolution.com/the-value-flywheel-effect/🌍 Learn Wardley Mappinghttps://learnwardleymapping.com/🎙️ The Serverless Craic Podcast & Bloghttps://theserverlessedge.com/📚 Escape Velocity – Geoffrey Moorehttps://www.goodreads.com/book/show/11103017-escape-velocity🧭 Wardley Mapping Community Resourceshttps://wardleypedia.org/👥 About The Serverless CraicWe explore modern cloud, serverless-first engineering, Wardley Mapping, the Value Flywheel, and high-performance technology leadership — with practical insights you can apply in your organisation today.If you want resilient systems, rapid delivery, strong engineering culture, and adaptive strategy, you’re in the right place.👍 Like, Subscribe & Join the CraicIf you enjoy these episodes, hit like, subscribe, and drop us a comment.Let us know what topics you'd like us to dive into next.Serverless CrAIc from The Serverless EdgeCheck out our book The Value Flywheel Effect Follow us on X @ServerlessEdgeFollow us on LinkedIn Subscribe on YouTube