Vibe Coding and Its Impact on Software Engineering

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. Source: Andreas Horn

Vibe coding will cost companies millions. Not today. Maybe not next quarter. But eventually. Right now, teams are shipping products faster than ever. A few prompts, some AI-generated code, and suddenly there's a working application. Everyone celebrates the speed. Very few people think about the maintenance bill. The problem with vibe coding is that it optimizes for getting something working, not understanding why it works. Six months later, nobody understands the architecture. A simple feature takes three days because every change breaks something else. Security gaps appear. Performance drops. Technical debt compounds. Then the company spends millions paying engineers to untangle systems that should have been designed properly from the start. I've said it before and I'll say it again: AI is not replacing engineering fundamentals. AI is making them more important. The engineers who thrive in the AI era won't be the people generating the most code. They'll be the people who understand system design, architecture, scalability, security, and maintainability. Because when the AI-generated code stops making sense, somebody still has to own the system. And that somebody is an engineer.

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.

"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.

Vibe coding - friend or fatal attraction? I've seen a plethora of posts recently about vibe coding. Some celebrate it. Some excoriate it. I understand the appeal. Writing code by talking to your GenAI assistant instead of actually writing the lines yourself? Sounds awesome! It's the next iteration of no-code implementation, with more flexibility, a lower barrier to entry (no tool to learn, really), and huge upside potential for productivity. There are several things concerning about the practice though. 1. How do you enforce secure coding and other best practices when developers are letting the LLM do the writing for them? 2. How do you develop strong software engineers and developers if they aren't actually doing the writing of the code? 3. When a system breach occurs, how do you correct the breach if no one on the team really understands how the code works? 4. How innovative can any solution built using regurgitated and remixed code (which is essentially what generativeAI is doing - regurgitating and recombining code that was written by someone or something else previously) truly be? GenerativeAI is a powerful tool in the hands of engineers and developers when it is used correctly. Vibe coding, in my opinion, is not that case. Look, if you are just creating a throwaway app that you'll use for a few hours, let the LLM write it for you. But if you are trying to build an innovative solution, something that hasn't already been solved for 15 different ways, bring the creativity of a human mind to the effort. If you're an experienced coder who wants to simplify your job by letting generativeAI give you a first shot at a solution, great. Go for it. But please, take the time to look at the code the model generates. Make sure you understand and agree with what it is doing before you push its code into the repository. Use its help to sharpen what you write, don't outsource the writing fully to the model. If you're a less-experienced coder who wants to learn how to code, use generativeAI with care. Write the code yourself first, then use generativeAI to gut check what you did. Don't have it write the code for you and think that you are legitimately learning how to code. Write the code, then ask the model to provide recommendations on how to improve your solution. Or have it provide a solution and compare what it did to what you did. Or ask it to describe potential risks or inefficiencies in your code. Write the code yourself first. Remember, LLMs were trained on a huge volume of data. When it comes to coding, some of that data is great. The code is well written, performant, secure, and resilient to bugs. But some of that data is not great. The code may be error-filled, inefficient, or even malicious in nature. The LLM doesn't differentiate when it is learning! It doesn't know how to. So use it with care. Don't trust, verify. Are you feeling my vibe, or are you feeling that I'm way off in my opinion? Comment below! #LLM #VibeCoding #MyOpinion

Ever heard of “vibe coding”? It’s the practice of leaning on AI tools to write code that just looks or feels right - without really understanding what’s happening under the hood. Over the past year I’ve watched complete newcomers spin up minimum viable products in an afternoon. That’s genuinely exciting; a task that once demanded weeks of heads-down work is now within reach after a few prompt -> test -> tweak cycles. But here’s where things get messy: - Shallow fixes, deeper bugs: When an error crops up, the instinct is to feed the whole traceback back to the model. Without a solid mental model of how the program works, each “fix” often patches one line while quietly breaking three others. - Feature drift: AI assistants try to be helpful - sometimes too helpful - by sprinkling in “bonus” functionality no one asked for. Those stray functions add complexity that beginners struggle to untangle later. - Endless rewrite loops: A lack of fundamentals means each debugging round becomes a game of whack-a-mole: regenerate, test, new error, repeat. Momentum stalls, and the MVP freezes at “just barely works.” So what does work? - Pair the AI with a curious human: Treat the model like a knowledgeable colleague, not an all-knowing oracle. Ask why a snippet is written that way. - Keep the scope laser-focused: Ship the simplest version first, then layer features on intentionally - don’t let the assistant improvise. - Invest in the basics: Even a weekend crash-course in programming concepts pays dividends. Knowing the difference between scope and state, or between synchronous and async calls, turns AI suggestions from “mystery incantations” into understandable code. Vibe coding can unlock creativity and lower barriers - but only when balanced with a dash of old-fashioned comprehension. If we marry AI’s speed with human judgment, we get the best of both worlds: rapid prototypes and maintainable software. Curious to hear your experiences - have you run into the vibe-coding vortex? How did you break the loop? #innovation #technology #future #management #startups

AI vibe coding: promise, pitfalls and a practical path forward Premanand Chandrasekaran’s Thoughtworks article “Can Vibe Coding Produce Production-Grade Software?” is required reading for anyone leading engineering or product teams. What they did • Experiment 1: Gave an agentic IDE a high-level spec and let it rip. Result: a surprisingly complete JavaScript app—but brittle and hard to evolve. • Experiment 2: Added guardrails; TDD, mutation testing, modularity. The AI switched to TypeScript, raised coverage near 100 percent, yet still broke builds when context was lost. • Experiment 3: Treated the model as a collaborator, debating trade-offs and design. The Python/FastAPI code was markedly cleaner and more maintainable. Key takeaways • Tool choice matters: different models excel at different tasks. • Context windows are finite; assume amnesia between sessions. • Human-AI pairing beats autopilot: oversight, tests and static analysis remain non-negotiable. • Cost is real: one afternoon of “fast” requests blew through a premium quota. Recommendations! + Start pilots now—treat the AI as a junior dev who never tires. + Codify guardrails (templates, quality gates, mutation thresholds). + Upskill leads on prompt engineering and AI-augmented reviews. + Track unit economics; tokens budgets are the new gravity well (cloud costs). + Production-grade software still demands human intent, but the gap is closing fast. Ignore this wave and you risk legacy before you ship. Read the full article (https://lnkd.in/gQ3uSTxg ) and share your lessons learned. #GenerativeAI #SoftwareEngineering #Leadership #DevEx

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

I love that vibe coding lets nontechnical people build technical artifacts. AI is democratizing web development, software engineering, and data analytics. But what influencers tell you about vibe coding is 100% BS. Here are all the things they should say, but don’t. Source control and versioning are more important than ever. GitHub is cheaper than regret. AI can walk you through using GitHub. It’ll even explain branching strategies so you can vibe with your friends. Get familiar with IDEs like VS Code and Jupyter Notebooks. Learn how to use the code navigation and debugging features. Learn to run code locally and the basics of containers. YouTube tutorials are your friend. AI will tell you how to solve problems without explaining the potential upstream and downstream impacts. Use tools with long context windows. Provide code for dependencies and integration points along with your prompt. AI doesn’t create secure code, so anything exposed to the internet is vulnerable. Ask for best practices to secure data, private keys, webpages, infrastructure, and user authentication. AI won’t provide multiple solutions and explain the tradeoffs of each one. There are always multiple ways to do things. For complex implementations, ask for 3-5 alternatives with the pros and cons. AI doesn’t take resource usage into account unless you tell it to. I have seen AWS bills end vibe coding careers. Always test locally and watch resource utilization. Learn to set up limits, or at least alerts, on cloud usage. Before drawing any conclusions from data, ask about the limitations of the analysis. Every data exploration and visualization has limits based on the methods used and data quality/completeness. The more complex the model or data, the more caveats come with it. It’s a great time to get your feet wet with vibe coding tools, but a terrible time to ship vibe code to production. Tools will improve by the year's end, so learning now is a great idea. However, avoid the influencer hype. Software engineers showcasing vibe coding tools forget to mention all the background knowledge that makes impressive demos possible. People who don’t ship code or models to production say whatever it takes to go viral. They’ve never had to fix a critical production issue at 3 am on Saturday because they pushed code on Friday at 5 pm, or broken out in a cold sweat over a “351,297 rows updated” message for a 3 row update. I won’t talk about AI tools until I see them work under real-world conditions. I wish more people followed that rule.