The Problem
Fast-moving systems fail differently now. They don't crash. Dashboards stay green. Deployments succeed. Tests pass. But decisions get harder to explain, alignment requires more meetings, and small disagreements escalate into architectural debates.
The culprit isn't technical debt. It's interpretation debt: the gap between what your systems actually do and what your teams think they do.
What This Means in Practice
Technical debt is familiar. Messy code, brittle services, undocumented APIs. It slows throughput. You can measure it in refactoring hours and incident rates.
Interpretation debt accumulates when systems evolve faster than shared understanding. Capabilities change but mental models don't. Decisions persist after their original assumptions expire. In distributed architectures, this manifests as communication overhead that scales worse than the services themselves.
The pattern is consistent: teams ship faster, coordination costs increase non-linearly, and by the time metrics signal problems, months of work has compounded in the wrong direction.
Why It Matters for APAC Enterprise
This hits harder in microservices architectures. When Transport NSW or a major bank scales from 20 to 200 services, technical complexity increases linearly. Organizational complexity increases exponentially. Each team develops local context that doesn't propagate. Conway's Law guarantees your architecture mirrors your communication structure, but nobody tracks when that structure stops matching reality.
Martin Fowler's Technical Debt Quadrant distinguishes prudent versus reckless debt. Interpretation debt is often prudent at first: you move fast, defer documentation, assume context is shared. The recklessness is in not recognizing when velocity itself makes that assumption false.
The Real Question
How do you measure context drift before it becomes coordination crisis? Technical debt shows up in maintenance costs. Interpretation debt shows up in perfectly executed implementations of the wrong thing.
We've seen this pattern before. The question isn't whether your systems work. It's whether 200 people still agree on what "working" means.