Best of Technical Debt — November 2025

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   Article

   DEV·26w

   "Technical Debt Will Bite Us in the Ass": How to Make Non-Technical Stakeholders Actually Care

   Engineers often struggle to get stakeholders to prioritize technical debt because they use technical jargon instead of business language. The key is translating code quality issues into tangible business impacts using relatable metaphors (infected wounds, cracked foundations) and quantifiable metrics (time, money, bug rates). Frame technical debt discussions by acknowledging stakeholder priorities first, connecting technical problems to their goals, quantifying costs, proposing clear ROI, and empowering them to make informed decisions. Cross-discipline communication isn't a soft skill—it's a core engineering competency.

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   Article

   Frederick's delirious rantings·28w

   Architectural debt is not just technical debt

   Architectural debt extends far beyond code-level technical debt into business and strategic layers. While developers focus on code patterns and structure, enterprise architects must address application integration patterns, data flows, vendor lock-in, business process documentation, ownership clarity, and strategic framework alignment. Debt at the business layer creates operational inefficiencies and compliance risks, while strategic-level debt can derail multi-year transformation initiatives. Enterprise architects have the visibility and access to flag these issues through dashboards and business cases, but must prioritize battles carefully and be prepared to lead remediation efforts themselves.

   48

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3. 3
   Article

   InfoWorld·25w

   Software development has a ‘996’ problem

   The push for high-volume code generation through AI tools mirrors the problematic '996' work culture, prioritizing quantity over quality. Research shows AI-assisted development increases code churn by 55% while reducing refactoring, creating bloated, unmaintainable codebases. Every line of code is a liability requiring maintenance and security oversight. Effective AI usage should free developers to focus on critical thinking—problem framing, ruthless editing, and system ownership—rather than generating endless code. Innovation requires mental space to determine what not to build, not just faster typing.

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   Article

   The Daily WTF·26w

   Using an ADE: Ancient Development Environment

   A developer shares their experience maintaining software built with a deprecated IDE from a defunct vendor (last updated in 2002). The tooling frequently corrupts files, requires reinstallation after each session, and uses a non-standard C++ dialect that wraps an ancient Pascal library. The codebase features an 18,000-line main form, global state management, and confusing file naming conventions. Despite these challenges, strong team culture and management support make the work manageable while they develop a .NET replacement.

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   Article

   The New Stack·25w

   Is AI Creating a New Code Review Bottleneck for Senior Engineers?

   AI coding tools can rapidly generate scaffolding and basic patterns, but often leave developers struggling with the remaining 70% of work—integration, security, edge cases, and debugging. Google's Addy Osmani discusses how AI-generated code creates a code review bottleneck for senior engineers, as trust in AI output declines despite rising adoption. He recommends understanding generated code thoroughly, investing in context engineering, maintaining strong test coverage, and using AI as a learning tool rather than a crutch. Real productivity gains appear to be less than 2x, with greenfield projects seeing better results than legacy codebases with technical debt.

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