Technical Debt Management Practices

At some point in the past 18 months, it became trendy to say that software engineering was dead. Why learn to code when you can tell an LLM, “make me an app with a login screen and a database” and out pops code? Voilà. Startup in a weekend. Series A by Thursday. Thus was born vibe coding - the art of building software by manifesting it. Mix 1 part natural language, 1 part vague ambition, and 1 part blind confidence as you paste mystery code into production. For a brief, shimmering moment, it almost felt like the future. Until it didn’t. AI code assistants unlocked a wave of creative energy. Non-technical founders spun up MVPs. Engineers offloaded boilerplate. Students built full apps in a weekend. But prototype-grade code isn’t production-grade code. Many teams that began with LLM scaffolds are now spending weeks refactoring. Some have even declared “code bankruptcy.” Because there’s a difference between writing code and building software. The former is syntax. The latter is systems thinking. At some point, every serious technical team has the same realization: you don’t just need code - you need engineering. Vibe coding isn’t a tech failure, it’s a categorization error. It assumes that the problem in software development is generation speed. But for any company past the napkin stage, that’s not the bottleneck. It is: - Understanding and reflecting business logic - Architecting clean, extensible code - Managing state, latency, auth, concurrency, observability - Reasoning through edge cases and failure modes LLMs don’t reason through trade-offs or hold long-term context. They don’t ask, “Why does this route even exist?” So when teams use LLMs to generate full features - or worse, entire codebases - they end up with systems that appear functional but are structurally hollow. Like a house with beautiful wallpaper but no load-bearing walls. There’s a market for vibe-coding. It’s just not software. This is the real distinction: vibe coding and AI in software development are not the same thing. - Vibe coding tries to replace engineering. Hand the keys to the model, hope for the best. - AI in software development amplifies engineering. Accelerate rote work while owning architecture, logic, and trade-offs. The first treats AI as a substitute; the second treats it as a lever. Vibe coding is fantastic 0 → 1. It’s a liability 1 → 100. It’s like bringing a balloon animal to a knife fight. Wonderful at a birthday party. Less helpful in real combat. There’s a real market for fast, disposable, front-of-the-house code. But most tech companies are in the business of building the kitchen, not just plating food. The panic about “engineering being dead” comes from people who don't understand it. Engineering isn’t syntax. It’s constraint management, abstraction design, knowing when to optimize and when to punt. Ironically, as AI makes building easier, the value of engineering judgment goes up. The faster you can go, the more you need someone to steer.

𝗧𝗵𝗲 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗟𝗶𝗲. “I built an app in 3 hours.” Sure. You built a demo. It will take you at least 3 weeks to make it production-ready. And 3 months to clean up the mess. Vibe coding is fun until you have to ship something real. LLMs make development feel effortless. A polished UI with a a hosted backend. Everything responds instantly. Anyone who can write a prompt can spin up something that looks like a product. But the hard part was never building fast. The hard part is building to last. You would not build a house without a foundation. Yet that is exactly what vibe coding encourages. 𝗬𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝗿𝗲𝗮𝗸𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝘆𝗼𝘂 𝘀𝗸𝗶𝗽 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: → Infrastructure design → Security boundaries → Deployment strategy → Error handling → Logging for diagnosis → Monitoring for failure detection → Alerts when things break at 2 a.m. AI-assisted development is genuinely powerful. I have seen delivery timelines compress from weeks to days. Prototyping and early validation have never been faster. I use it myself, and I enjoy it. But here is the uncomfortable truth: AI optimizes for plausibility. Not for simplicity and also not for long-term correctness. Left unconstrained, it produces architectures and technical debt that look reasonable but age badly: → Abstraction layers nobody can explain → Blurred component boundaries → “Best practices” added before there is a problem to solve More code. Lower quality. Slower teams over time. Vibe coding optimizes for speed as the primary metric. Engineer-guided AI treats software as long-lived infrastructure that must be operated, understood, and evolved. AI does not reduce the need for engineering judgment. It increases it. The engineer’s role is shifting: From writing code to constraining, reviewing, and shaping it. AI is an accelerator. Without direction, it accelerates technical debt just as efficiently as it accelerates delivery. ↓ 𝗜’𝗺 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 𝗮 𝗳𝘂𝗹𝗹 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝘁𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗯𝗲𝗵𝗶𝗻𝗱 𝘃𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗼𝗻𝗶𝗴𝗵𝘁 - 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝘁𝗼 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝗶𝘁: https://lnkd.in/dbf74Y9E

In the rush to adopt AI tools, I’m seeing a dangerous conflation of two very different concepts in software development: speed of creation vs. speed of evolution. This image perfectly captures the current dichotomy in our industry. The "AI Vibe Coding" Trap : AI is incredibly good at a basic prototype in record time. It gives you the vibe of a completed product. But look at the foundation. It’s "Spaghetti Architecture." It works right now, today, under current conditions. But what happens when you need to add a second story? (Scale the user base) What happens when a pipe bursts? (Debugging production errors) You have to dig through an unstable jumble of rocks to find the root cause. The cost of changing anything is astronomical. The Engineer-Guided Approach : This takes longer to set up. It requires deliberate thought about systems, layers, and separation of concerns. It doesn't feel as "magical" in the first hour. But look at the result: Predictability. If something breaks in the right house, you know exactly which access panel to open. You don't tear down the walls to fix a leak. AI is a tireless developer that never sleeps, but it lacks foresight. It optimizes for the immediate prompt, not the three-year lifecycle of the application. If you accept AI output without engineering oversight, you aren't moving faster. You are just borrowing time from your future self at a very high interest rate. Technical debt is still debt, even if an AI generated it.

𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗞𝗶𝗹𝗹𝗲𝗿 𝗜𝗻𝘀𝗶𝗱𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲: 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 Most teams feel technical debt. Very few teams can see it early enough to act. Mike Godfrey shares how loveholidays uses static analysis (CodeScene) as an early-warning system for technical debt, not as vanity metrics. What I like about this: it’s basically cybernetics for software delivery: sensors → signals → feedback → control actions. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 “𝘀𝗲𝗻𝘀𝗼𝗿𝘀” 𝗿𝗲𝘃𝗲𝗮𝗹: - Hotspots: where change and effort concentrate (your real pressure points) - Change coupling: files that keep changing together → hidden dependencies - Knowledge distribution (bus factor): where knowledge is lost or stuck in "islands" - Team–code alignment (Conway’s Law): overlap often signals unclear ownership 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗴𝗲𝘁𝘀 𝗿𝗲𝗮𝗹: They integrate it into the workflow with branch analysis, so PRs can be flagged/blocked when code health declines. That’s a closed feedback loop, not a dashboard. If you want sustainable speed, this is the game: Make technical debt observable, actionable, and governed by clear guardrails. Especially now in the age of AI. #TechnicalDebt #StaticAnalysis #CodeHealth #PlatformEngineering #DevOps

🧠⚙️ Is Agent Debt the new Technical Debt? Generative AI is changing how we build software. Tools like Copilot and ChatGPT can generate code in seconds. But after the wow-factor fades, we’re left with a tough question: Who maintains the code AI writes? 💡 Context Is Everything—and AI Doesn’t Have It AI can generate syntax, but it doesn’t understand your architecture, business logic, or roadmap. As a result, its output often lacks: ⮕ Consistency with your design principles ⮕ Scalability beyond the immediate task ⮕ Readability for future developers ⮕ Rationale for why decisions were made The result? Code that’s hard to evolve—and harder to trust. 🔧 Set It and Forget It? Think Again! AI-generated code may "work," but that’s not the same as being maintainable. Without integration, review, and documentation, it becomes a silent form of technical debt. And when no one can explain how or why it was written, that “free” code can become a costly liability. 👨🔧👩💻 Human-in-the-Loop Is Essential AI won’t replace developers—it will demand better ones. The future isn’t autonomous generation—it’s collaborative augmentation. That means: ⮕ Code reviews matter more than ever ⮕ Standards must evolve to include AI output ⮕ Generated code should come with intent metadata ⮕ Documentation isn’t optional Use AI for acceleration, but don’t skip craftsmanship. 🤔 A New Kind of Technical Debt? We need to rethink what tech debt means. It’s no longer just rushed features. It’s also about: Code generated quickly, functional now, but resistant to change. If no one can confidently update it six months from now, you're accruing AI-induced debt. 🚀 Build Smart, Not Just Fast AI gives us speed. But maintainability is what enables scale. So let’s not just build quickly—let’s build wisely. 👋 Have you embraced AI coding? How are you balancing speed and sustainability? I’d love to hear your approach 🙏. #TechnicalDebt #VibeCoding #EnterpriseArchitecture #HumanInTheLoop

After years as a Team Lead at Google, I can confidently say: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗜𝘁’𝘀 𝗮 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. The problem isn't the code quality; it's the 𝗹𝗶𝗲 that leadership accepts when prioritizing 100% features and 0% maintenance. If you lead a team, stop thinking about debt as 'bad code' and start thinking about it as a 𝗵𝗶𝗱𝗱𝗲𝗻 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗮𝘅 on every feature you ship. Here are the three unexpected costs of "quick wins" that eventually crush teams: 𝗧𝗵𝗲 "𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗟𝗼𝗮𝗱" 𝗧𝗮𝘅: Every piece of ignored debt adds complexity. New engineers spend 2x longer onboarding. Existing engineers spend 3x longer debugging. Your velocity looks good on the spreadsheet but is silently being suffocated by mental friction. 𝗧𝗵𝗲 "𝗔𝘁𝘁𝗿𝗶𝘁𝗶𝗼𝗻" 𝗧𝗮𝘅: Your best, most detail-oriented engineers leave first. They leave because they came to solve challenging new problems, not fight the same old mess inherited from a rushed deadline two years ago. Debt is a talent retention killer. 𝗧𝗵𝗲 "𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗢𝗻𝗹𝘆" 𝗧𝗮𝘅: By only tackling debt during an immediate, catastrophic failure (the outage), you guarantee two things: 1) The work is done under maximum stress, increasing risk, and 2) You solidify the negative perception that maintenance work is only necessary when the business is actively losing money. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: As a leader, you must mandate and protect a 20% 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗛𝗲𝗮𝗹𝘁𝗵 𝗕𝘂𝗱𝗴𝗲𝘁 every sprint. If the business won't budget 20% for maintenance, they are implicitly budgeting for 100% of future chaos. #TechnicalDebt #SoftwareEngineering #EngineeringLeadership #ProductManagement

I was a CTO at a company with a LOT of technical debt. Here's how I handled it. 1. I found someone in the org (non-exec) who cared about the issue and was organized. 2. We created a framework to rank our tech debt & built a common mini "language" to talk about it easily. 3. Next we documented the entire tech ecosystem & applied the framework to categorize it all. 4. We met with business stakeholders like Product & Sales to add their perspective into the ranking. 5. We grouped the tech debt into a) never touch, b) fix ASAP and c) fix incrementally. 6. We calculated the potential ROI on each item to help acquire funding to fix it. (This was difficult). 7. We built a plan for remediation and integrated the plan into the roadmap. 8. We created a tracking / monitoring best practice specifically for the tech debt remediation work. 9. We were pretty hardcore about reporting the ROI up to the CEO on all the tech debt fix work. 10. After a while of doing this tech debt remediation got baked into our organization. What's the big lesson? Anything can be done in an org if its important enough, you focus on it and you work hard to achieve it. Interesting in more content like this? Sign up for my free newsletter at https://buff.ly/4ccyrM0. #TechLeadership #softwaredevelopment #CTO

Today one of my engineers asked if they should create a ticket for a small refactor that’s been bothering them. I laughed and said, “Come on, you know you don’t need permission to make code better.” But then I realized - this question tells a bigger story. How many teams are still treating refactoring like it needs executive approval? How many engineers are sitting on improvements because they’re waiting for the “right time” or the “refactor sprint”? Here’s what I’ve learned works way better than big planning sessions: The Boy Scout Rule in action: • Working on a feature? Clean up the code around it while you’re there • See something that’ll save the next person 10 minutes? Fix it • Find a confusing variable name? Rename it • Spot duplicated logic? Extract it No tickets. No meetings. No permission needed. The magic happens in those small, continuous improvements. Not in the mythical “technical debt sprint” that never comes. My only rule: if the refactor is bigger than the actual feature work, then we talk about it. Otherwise? Just make it better. The result? Our codebase stays healthier, engineers feel ownership, and we avoid those massive “everything is broken, we need to rewrite it” moments. Technical debt isn’t solved by grand gestures. It’s solved by dozens of small acts of care. Do your engineers feel empowered to improve code as they go? Or are they waiting for permission that might never come?

𝗗𝗼𝗲𝘀 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗧𝗿𝗮𝗱𝗲 𝗦𝗽𝗲𝗲𝗱 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁? Developers report 10x productivity gains from AI coding agents, yet a Carnegie Mellon study of 806 open-source GitHub repositories found something different. Researchers compared Cursor-adopting projects against 1,380 matched control repositories, tracking code output and quality monthly using SonarQube. Here are the key findings: 𝟭. 𝗧𝗵𝗲 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗯𝗼𝗼𝘀𝘁 𝗶𝘀 𝗿𝗲𝗮𝗹 𝗯𝘂𝘁 𝗱𝗶𝘀𝗮𝗽𝗽𝗲𝗮𝗿𝘀 𝗳𝗮𝘀𝘁 Projects saw a 𝟮𝟴𝟭% 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗹𝗶𝗻𝗲𝘀 𝗮𝗱𝗱𝗲𝗱 and a 𝟱𝟱% 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 during the first month after Cursor adoption. By month three, both metrics dropped back to pre-Cursor levels. The spike looks great on a dashboard. It just doesn't last. 𝟮. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗯𝘁 𝗮𝗰𝗰𝘂𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝘀𝘁𝗮𝘆𝘀 Static analysis warnings rose by 𝟯𝟬% and code complexity increased by 𝟰𝟭% on average. This decline in quality was persistent in the project. 𝟯. 𝗧𝗵𝗮𝘁 𝗱𝗲𝗯𝘁 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗮 𝘀𝗲𝗹𝗳-𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝘀𝗹𝗼𝘄𝗱𝗼𝘄𝗻 The researchers found a feedback loop between quality and velocity. A 𝟭𝟬𝟬% 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗰𝗼𝗱𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 caused a 𝟲𝟰.𝟱% 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲 in future development velocity. A 𝟭𝟬𝟬% 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝘀𝘁𝗮𝘁𝗶𝗰 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗮𝗿𝗻𝗶𝗻𝗴𝘀 caused a 𝟱𝟬.𝟯% 𝗱𝗿𝗼𝗽 in lines added. The two-month speed boost generates enough technical debt to drag down productivity for months afterward. 𝟰. 𝗔𝗜 𝘄𝗿𝗶𝘁𝗲𝘀 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝗻 𝗵𝘂𝗺𝗮𝗻𝘀 Regardless of the codebase's size, Cursor-adopting projects still had 𝟵% 𝗵𝗶𝗴𝗵𝗲𝗿 𝗰𝗼𝗱𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 than comparable projects producing the same volume of code. This means that such projects are harder to maintain. QA has to keep up with higher output. We can say that teams adopting agentic coding tools without upgrading their processes are borrowing speed from the future. The paper even suggests tools should consider "self-throttling," reducing suggestion volume when project complexity crosses healthy thresholds. 𝗟𝗶𝗻𝗲𝘀 𝗼𝗳 𝗰𝗼𝗱𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝗲𝗱 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗮𝘀 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗺𝗮𝗱𝗲 What processes has your team put in place to manage code quality alongside AI coding tools?

Your PM just spent 45 minutes in 2026 planning making the case for critical tech debt. They had everything: Last quarter's three production outages. Customer complaints about reports timing out. That security vulnerability that made the CISO lose sleep. Even showed how fixing this would save 200 engineering hours next year. The CEO listens politely. Then: "This sounds important, but can it wait? We really need that enterprise feature to close the xyz deal." Tech debt loses. Again. When you try to trade infrastructure work against revenue features one by one, infrastructure loses every single time. Revenue is tangible now. Technical risk is abstract future. In our recent Supra 2026 planning session, Rich Mironov shared an effective approach to scope tech debt. He doesn't fight feature by feature. He negotiates the entire portfolio upfront. His breakdown: ↳ ~50% on customer-facing features (stuff they ask for by name) ↳ ~35% on "keeping executives out of jail" tech debt (compliance, security, scalability)   ↳ ~10% on executive escalations (the random fires from sales) ↳ ~5-10% on innovation/discovery That second bucket; Rich literally calls it "keeping you from being arrested" work. When executives push back, he gets specific: - "Remember last month when the site went down during the customer conference and you had to apologize on stage?" - "Remember when the board grilled you for 20 minutes about why our main competitor's app loads 10x faster?" - "Want to explain to investors why we had to pause new customer onboarding because our database is melting?" Now they're listening. What makes this work is that you negotiate this percentage once at the start of the year. Not every sprint. Not every quarter. After that, that 40% is sacred. Someone wants to raid it for their pet feature? "We agreed that not getting sued or having the site explode was worth 40% of our capacity. Let's discuss in our next board meeting to ensure everyone is ok with the change." No more justifying individual infrastructure projects. No more death by a thousand "but can't this wait?" conversations. The portfolio is approved. The percentage is locked. I've watched too many product leaders burn out trying to defend every single piece of infrastructure work. Meanwhile, the technical debt compounds until something catastrophic happens, and suddenly it's "why didn't anyone warn us?" + the product leader is on the hook for the consequences. Rich's model flips the whole thing. Instead of begging for permission to keep the lights on, you're protecting executives from explaining to the board why everything caught fire.