What If Your Biggest Technical Debt Is Social?
Ward Cunningham's original technical debt metaphor in 1992 was not about bad code. Debt could be a business and learning strategy: we build with the understanding we have today, use the software, learn something new, and discover that yesterday's good design no longer fits what we now know.
More recently, Kent Beck has reframed a similar tension as Features vs. Future: delivering value now while preserving our ability to respond to what comes next.
AI-accelerated code production makes that learning debt more urgent.
This talk explores why faster execution loops, better DDD-informed specifications, disciplined TDD, and stronger AI harnesses are only part of the answer. The deepest learning debt is social and non-linear. It accumulates in our pursuit to understand the problem, notice assumptions, let our mental models shift, decide what matters, take ownership, and act under uncertainty.
Drawing on Domain-Driven Design, collaborative modeling, cognitive science, and dialogical practices, we'll explore how we can make thinking together a deliberate part of software design.
Borrowing Kari Zeller's language, how might we* re-place ourselves* as AI changes how software gets made? Instead of simply inserting AI into our existing processes and ways of thinking, what would it mean to rethink how we work with each other and with AI, where we create value, and what we take responsibility for?
Key Takeaways
- Technical debt is also a learning story. Revisit technical debt through single- and double-loop learning, and explore what changes when AI accelerates the execution loop.
- Thinking together is part of software design. Explore a cognitivescience-informed language and practical framework for noticing assumptions, opening perspectives, and learning together.
- Re-place ourselves and take leadership. Explore leadership as a distributed capacity available to all of us: the capacity to step forward when certainty has not arrived, rethink where we create value, and reclaim our agency and accountability as the ground keeps shifting.
Who Is It For?
- Anyone who shapes software: developers, architects, tech leads, product, UX, and business.
- Anyone experimenting with AI-assisted development and wondering what these changes mean not only for how we work, but for who we are as software professionals.
- Anyone who has seen technically successful teams struggle with shared understanding, ownership, or the ability to change direction together.
Level
Practitioner to advanced
