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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
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🎙 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

Serverless Craic from The Serverless Edge
Check out our book The Value Flywheel Effect
Follow us on X @ServerlessEdge
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AI 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.

Chapters

00:00 – Welcome & context
Why AI + serverless + the Value Flywheel is colliding right now

01:50 – Myth 1: “Software engineering is dead”
Why engineering skills are more valuable, not less

07:06 – Myth 2: “My skills will become irrelevant”
Moving up the value chain, domain expertise, and growth mindset

13:40 – Myth 3: “The quality isn’t good enough”
Standards, constraints, and why worst it’ll ever be is today

18:44 – Myth 4: “The model understands the problem”
Pattern matching vs understanding, context, and critical thinking

24:20 – Myth 5: “I’ll be forced to use AI”
Workflows, guardrails, security, and excessive privileges

31:54 – Myth 6: “We’ll need fewer engineers”
Jevons Paradox, lowered barriers, and the coming demand explosion

34:22 – Closing thoughts
AI, velocity, and the future of sustainable software engineering

Key Themes Discussed

AI as an abstraction layer, not a replacement for engineering
Why standards, constraints, and operability still matter
Domain-Driven Design as AI-amplifying, not obsolete
Agentic systems, skills, prompts, and containment
Security risks: excessive privileges & supply-chain concerns
Velocity vs sustainability in AI-assisted development

Resources & References

The Value Flywheel Effect – principles referenced throughout
Wardley Mapping & situational awareness
Domain-Driven Design (DDD)
OWASP Top 10 for LLMs (excessive privileges, agent risks)
Jevons Paradox (efficiency driving increased demand)
Early cloud cost & governance parallels
Threat modelling for AI and agentic systems

Serverless Craic from The Serverless Edge
Check out our book The Value Flywheel Effect
Follow us on X @ServerlessEdge
Follow us on LinkedIn
Subscribe on YouTube