Remember the last time you built or created something and felt genuinely proud of it? Chances are, you understood it deeply. And you built it with people who did too.

Great software has a story behind it. A shared one. Today, AI accelerates execution. But it cannot generate the thinking and caring together that keep the shared story alive. You can feel when that story is missing. As code becomes cheap, meaning becomes expensive.

This is the ground AI-accelerated teams are losing. Two risks are compounding:

• Cognitive risk: velocity outpacing shared understanding
• Identity risk: shipping systems we have prompted, but don’t feel connected to

This masterclass is about reclaiming that ground. Domain-Driven Design, practiced as a sociotechnical discipline, is one of the most powerful tools available for designing shared understanding and keeping it alive. In the age of AI, thinking together is the edge. DDD has never mattered more.

Over two days, you’ll learn the strategic core of DDD from someone with more than a decade of experience using it to decouple software and connect people in complex environments. Hands-on, analog, and in conversation — the way shared understanding actually gets built.

Who Is It For?

Developers, architects, tech leads, product managers, UX designers, business analysts.

Anyone who has felt the gap between what a system does and how well it’s actually understood.

What You’ll Walk Away With

You will be able to:

• Know when to slow down to discover the domain before speeding up execution
• Use conversation as a design tool: surface what’s assumed, challenged, or missing in old and new code
• Design systems with clear intention and boundaries: from core domain to context mapping
• Apply Ubiquitous Language to keep shared understanding alive
• Walk away with a lived experience of DDD by thinking and modeling together, not just learning the theory

Day 1: Thinking Together

Day 1 is about foundational skills. You start with a real domain and model it together from scratch.

• Languaging: Name complexity in problem, solution and possibility space
• Modeling: Discover the domain through conversation, events and user needs mapping
• Connecting: Design Bounded Contexts around where your work and the rest of the system intersect

Day 2: Design at Scale

Day 2 is about strategic clarity. You take what you built on Day 1 and make larger-scale design decisions with it.

• Core Domain Strategy: Prioritize a large domain landscape and make trade-offs explicit
• Context Mapping: Map how systems and teams relate, depend, and conflict
• DDD+AI: Add Continuous Comprehension (CC) to the CI/CD mix in AI-assisted software development

Both days: sticky notes, shared walls, making the implicit explicit.

Prerequisites

Experience working with software in teams and curiosity about what the future of software development asks of us.

AI can generate features fast. It cannot generate shared understanding, or the ownership that comes from thinking and caring together.

Every piece of code carries traces of the conversations that were had or skipped, the compromises made under pressure, the misunderstandings no one named out loud. Read a system long enough and you’re not just reading architecture. You’re reading the organization, its power dynamics, and the quality of its collaboration.

That’s social debt. And unlike technical debt, there’s no git revert.

Key Takeaways

1. Code is a fossil record of our conversations. How do we architect the conversations themselves?
2. Social, cognitive, and emotional debt compound under AI acceleration. How do we name what nobody wants to say out loud?
3. Architecture is a sociotechnical seesaw. When AI tips the scales, how do we keep our balance?

Who Is This For?

• Anyone who shapes software: developers, architects, tech leads, product, UX, business
• Anyone who has sat through a “collaborative” session and felt something was missing
• Anyone asking what great collaboration looks like when AI is part of the team

Level

Practitioner to advanced

Format

Slides

Full description

In complex teamwork, where no one person holds the full picture, the quality of collaboration is a real architectural concern. When design processes collapse into individual decisions made in isolation, or into workshops that look collaborative but lack genuine diversity of perspective, the debt is baked into the system before a line of code is written.

That’s social debt, accruing interest.

Now add AI. Code generation accelerates. The conversations that should be happening get squeezed out by the pressure to ship. Cognitive debt compounds social debt when output outpaces shared understanding, when systems grow faster than our collective ability to explain them.

This is not an argument against AI. It is an argument for honesty about the full cost. The uncomfortable question is whether we are scaling output or scaling misalignment. In the rush to celebrate productivity, are we paying a price in human cohesion, craft, and the joy of building something together?

Drawing on research and practical experience with clients, this session explores sociotechnical architecture beyond Inverse Conway: what it takes to keep your balance on the sociotechnical seesaw when AI tips the scales. We’ll look at social, cognitive, and emotional debt not as inevitable costs of progress, but as signals pointing toward better ways of navigating complexity together.