Technical Debt Plaguing Us All

I've been thinking about this exact tension for the past few years while building tools for operational efficiency. The real predictor of long‑term pain isn't complexity per se; it's interdependence. LLMs make writing code cheap, but they don't instinctively architect for modularity. They'll happily generate a tightly‑coupled monolith that "works" today but becomes a coordination nightmare tomorrow (same as with human-implemented poor architectures). What I've observed (Prof. Christensen's modularity framework applies here) is that teams that treat LLMs as "fast writers" but enforce "good enough" modular interfaces (clear boundaries, well‑defined jobs‑to‑be‑done for each component) can scale without the usual debt explosion. Teams that let the LLM dictate the architecture end up with a "house of cards"; every change risks cascading failures. So the measurable signal I'd look for isn't lines of code or even cyclomatic complexity; it's change‑propagation distance. How many other files/modules need updating when you modify one? That's a proxy for interdependence, and it's something static analysis can actually track. On LLM‑based evaluation in CI: I'd trust it for spotting obvious anti‑patterns (e.g., "this function is 500 lines") but not for judging architectural coherence. That still requires human context about the business's actual jobs‑to‑be‑done. Have others found useful heuristics for measuring interdependence in real codebases?

Hey everyone, I’d love to get a broader industry take from vibe coders and other developers here on technical debt in the LLM era.

We’re clearly in a phase where code generation is faster than ever, iteration cycles are shorter, and teams are shipping at a pace that would have felt unrealistic a few years ago. At the same time, we still do not have a shared understanding of what code health actually means in a world where writing code is cheap but maintaining coherence over time remains difficult. I am curious how you all believe companies should think about debt when LLMs are embedded in the development loop. What really predicts long-term pain? Is it complexity, churn, architectural drift, unclear ownership, or something harder to quantify? Should code health be distilled into measurable signals, or is it fundamentally multidimensional and resistant to simple scoring? And would you ever trust LLM-based evaluation in CI, or should it remain advisory?

I have not seen many compelling solutions yet. As an experiment, I have been working on an open-source technical debt analyzer that combines static analysis, heuristic checks, and LLM-based evaluation to surface hotspots and suggest improvements. The goal is to make debt more visible and easier to prioritize, especially for growing teams or inherited codebases. If you were evaluating a tool like this, what metrics would you expect it to include? Would LLM-based signals be something you would take seriously in a production workflow?

I am genuinely curious how others are thinking about this. It feels like a large and growing industry problem, and I would love to hear what has or has not worked in practice.