How Vibe Coding Affects Technical Debt

The hidden cost of “Vibe-coding”: How your UI becomes a maintenance nightmare. > You ask an AI assistant to build a login screen. It works, but the styling, spacing, and colors are all 𝗵𝗮𝗿𝗱-𝗰𝗼𝗱𝗲𝗱 directly into the view. > Next, you ask for a settings screen. It also works, but with slightly different colors and padding. > Then comes the profile screen. More custom logic. More one-off styles. 𝗦𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀 𝗹𝗮𝘁𝗲𝗿, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲 𝘆𝗼𝘂𝗿 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗯𝗿𝗮𝗻𝗱 𝗰𝗼𝗹𝗼𝗿. Since those values aren’t centralized, they’re buried everywhere. You update six files but miss three. The app looks broken. Now, you’re hunting through a codebase for the same hex code or background image implemented five different ways. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: AI generates each screen in a vacuum. Without a design system, it "reinvents the wheel" every single time. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻: Design debt starts with hard-coded values. 𝗧𝗵𝗲 𝗳𝗶𝘅? 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗶𝗿𝘀𝘁! > Before asking AI to generate a full feature screen, identify your repeated UI elements and have the AI create them as reusable components. > In every subsequent prompt, give the AI a constraint: "Build this new screen using only AppBackground and StandardButton components". This prevents "𝗰𝗼𝗽𝘆-𝗽𝗮𝘀𝘁𝗲 𝗨𝗜 𝘀𝗽𝗿𝗮𝘄𝗹". Consistency becomes a technical constraint rather than just a hope. Of course, this requires a 𝘀𝘁𝗮𝗯𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 where elements are defined once and reused across the app. It forces you to think about architecture before execution. In this article, I break down "𝗧𝗵𝗲 𝗦𝘆𝗺𝗽𝘁𝗼𝗺𝘀" of this "Vibe-coding" Trap and the solutions I use to build sustainable systems:  https://lnkd.in/eqqBg9ZP I’d love to hear your experience:  • Do you recognize this "sprawl" in your own projects? • Where have you seen AI-driven architectural drift happen most? Follow me for more on sustainable systems, AI architecture, and native mobile development that scales. 🚀 #SoftwareEngineering #UIUX #MobileDevelopment #DesignSystems #Refactoring #iOSDevelopment #Swift #VibeCoding #MobileEngineering #SoftwareArchitecture #CleanCode #Programming

"Just vibe code it" sounds great until you realize your devs are debugging by repeating the same prompt 31 times, spending 20% of time waiting, and frantically resolving P0s in production. So what do we do to mitigate that? A new study on "vibe coding" gets under the hood on what happens when engineers rely (almost) exclusively on natural language prompting to write software, instead of doing it themselves. The promise: democratize development, 10x productivity. The reality: 🚨 Developers with low code literacy are stuck in probabilistic hell. They can't evaluate what the AI generates, so they just keep re-rolling until something works. One researcher called it "debugging by rolling the dice." 🚨 Meanwhile, 20%+ of dev time vanishes into waiting for AI generation. One session hit 50% wait time. Your velocity gains are getting eaten by latency. 🚨 The gap between your senior devs and junior devs is widening. Experienced devs inspect code and give surgical prompts. Less experienced devs give vague instructions, never look under the hood, and accumulate technical debt through "design fixation"—they iterate on the first thing AI spits out instead of exploring alternatives. The reality is it's not a panacea, but it's definitely a powerful tool if used well. That looks like treating AI as a copilot, not an autopilot. Strong version control. Incremental changes. Automated tests. And yes, maintaining the ability to actually read and understand code. The tools are incredible. But over-reliance at this stage has consequences, so tread carefully.

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.

🧠⚙️ 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

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?

Vibe coding tax: $87,500 in fraud and $2,500 in Stripe fees. He shipped a Claude Code app with API keys on the front end. Hackers charged 175 customers $500 each. $87,500 total. A founder built his startup with Claude Code and shipped it live. He skipped the security audit and architecture review, relying only on prompts and production deployment. He trusted the AI to handle security because it handled everything else. API keys sat on the front end where anyone could take them. Someone found them and walked right in. The fix would have been one sentence: "Make sure all API keys are not on the front end and all security measures are taken." This is the vibe coding tax and it always comes due. The tools are incredible for demos, prototypes, and proof of concepts. They're dangerous when founders mistake velocity for readiness. AI cannot design systems, it only answers the questions you think to ask. Every shortcut in development will become a liability in production. This founder learned at $2,500. Others are learning at $200K in rebuild costs, 4-8 months of re-architecture, and customers who never come back. The pattern repeats daily: - MVP works beautifully in testing. - Real users arrive with real edge cases. - Security holes, scaling failures, integration breakdowns surface. - The rebuild costs more than hiring engineers would have from day one. Vibe coding created a generation of startups that shipped prototypes as products. The cleanup is coming. If you're building with AI tools right now, ask yourself "what did I forget to prompt?" Because someone will find it and likely exploit it long before you do.

The Good, The Bad, and the Ugly of Vibe Coding Andrej Karpathy coined "vibe coding" in February 2025. Within a month, Merriam-Webster added it to the dictionary. By March, 25% of Y Combinator startups had codebases that were 95% AI-generated. Almost eight months later, reality is setting in. ➡️ The Good You describe what you want in plain English. AI writes the code. No syntax knowledge required. The speed is real. Replit's ARR jumped from $10 million to $100 million in nine months. Small businesses now build tools that would have cost $50,000 and six months. People in Nairobi, Mumbai, and Lagos are shipping products that wouldn't otherwise exist. More ideas get tested. More diverse voices get heard. ➡️ The Bad Vibe coding hits a complexity ceiling fast. Works for prototypes. Breaks when you need scale or security. Fastly surveyed developers: 95% spend extra time fixing AI-generated code. Stack Overflow reports 46% of professional developers distrust AI coding tools. Only 30% say these tools handle complex tasks well. The Tea app left 72,000 sensitive images exposed. Security researchers blame AI-generated code. Lovable had vulnerabilities in 170 out of 1,645 apps it created. ➡️ The Ugly Senior engineers are becoming "AI babysitters." Some companies now hire "vibe code cleanup specialists." PayPal engineer Jack Zante Hays calls AI codebases "development hell." The code creates technical debt faster than teams pay it down. Coinbase CEO Brian Armstrong bragged that nearly half his exchange's code is AI-generated. The developer community responded with ridicule. Fast Company reported in September that senior engineers cite "development hell," "toxic waste," and "evil genies" when describing vibe-coded projects. Simon Willison: "If an LLM wrote every line but you've reviewed, tested, and understood it all, that's not vibe coding. That's using an LLM as a typing assistant." ⭐️ What This Means Vibe coding works for weekend projects and internal tools. Use it to test ideas quickly. Stop there. When you handle user data, need security, or want scale, bring in people who understand the code. AI generates fast. Humans ensure it works safely. Don't ship code you don't understand to production systems that matter. ➡️ Where We Go From Here I've spent 25 years in digital strategy. Every new tool follows the same pattern. Early adopters claim it changes everything. Reality sets in. We figure out what it's good for. Vibe coding democratizes prototyping. That's valuable. But it does not replace understanding how systems work, how security functions, or how to maintain code. Use AI tools to move faster on the right things. Don't use them to skip the hard work of building software that matters. The companies that will win long-term are figuring out how to move vibe-coded prototypes into production without creating technical debt. We're almost eight months into this experiment. The honeymoon is over. The real work begins.

Vibecoding is a superpower. It's also a trap. I've spent the last year talking to founders who built their MVPs with AI code generators over a weekend. The first 80% felt like magic. Then came the last 20%. The payment bug that AI couldn't fix. The security audit that found hardcoded secrets. The cloud bill that 5x'd when real users showed up. The senior dev they hired who took one look at the codebase and said: "It's faster to rewrite this from scratch." We mapped the 5 systemic problems every vibecoded project eventually hits: 1. Invisible technical debt (works at 100 users, crumbles at 10K) 2. The security "illusion of correctness" (looks clean, riddled with vulnerabilities) 3. The last-20% debugging wall (prompt loop > new bug > prompt loop > stuck) 4. Scalability & cloud-bill bloat (easy code ≠ efficient code) 5. The hiring gap (new engineers can't maintain what AI wrote) Vibecoding is an incredible prototyping tool. But shipping prototypes as products is how startups silently self-destruct. There's a better way. More on that soon.