In February 2025, Andrej Karpathy—a luminary in artificial intelligence—tweeted about a curious new phenomenon: programmers were crafting software by describing their goals in plain English while AI tools wrote the actual code. His term for this practice, vibe coding, has since sparked a global debate. Is this the democratization of programming or a shortcut to technical chaos? From Silicon Valley startups to hobbyist tinkerers, developers are grappling with a seismic shift in their craft—one where natural language replaces syntax and intuition guides code review.
What Exactly Is Vibe Coding?
Vibe coding refers to an AI-assisted development method where programmers describe their objectives in conversational language (e.g., "Build a login page with two-factor authentication") and let large language models (LLMs) generate the corresponding code. Unlike traditional programming, practitioners often accept AI-generated code without full comprehension, trusting iterative testing and refinement to achieve functional results.
The Origin Story
Karpathy introduced the concept while experimenting with voice-controlled coding via tools like Cursor Composer. "I just see things, say things, run things, and copy-paste things," he noted, emphasizing that vibe coding works best for disposable projects rather than production-grade systems. The term quickly went viral, reflecting a growing trend—25% of Y Combinator’s Winter 2025 startups now rely on AI for 95% of their codebases.
How Vibe Coding Is Reshaping Tech Industries
Democratizing Development
Non-programmers like New York Times journalist Kevin Roose have used vibe coding to create personalized tools. Roose’s "LunchBox Buddy"—an app analyzing fridge contents to suggest meals—showcases how amateurs can now build functional software. This accessibility fuels innovation: a hobbyist can prototype a mobile app in hours instead of months.
Corporate Adoption and Productivity
Companies report 40-60% faster development cycles by offloading repetitive tasks to AI. GitHub Copilot and Replit’s tools automate boilerplate code, letting engineers focus on architecture. IBM’s guidelines recommend vibe coding for rapid prototyping, advising developers to "describe requirements in specific, goal-oriented prompts".
The Voice-to-Code Revolution
Emerging tools like SuperWhisper allow programmers to dictate features verbally. Neurodivergent developers, in particular, benefit from this shift. As one engineer with dyslexia shared: "Speaking my ideas instead of wrestling with syntax lets me contribute equally".
The Double-Edged Sword: Pros and Cons
Advantages
- Speed: Startups validate ideas faster. A 2025 Stanford study found AI-generated prototypes reduce time-to-market by 68%.
- Lower Barriers: Graphic designers now build custom CMS tools; marketers automate data analysis without Python expertise.
- Creative Focus: Developers spend less time debugging semicolons and more on user experience.
Risks and Real-World Blunders
- Security Gaps: In March 2025, a vibe-coded payment gateway approved $2M in fraudulent transactions due to inadequate input validation.
- Technical Debt: A Reddit user shared how their AI-generated React app became unmaintainable: "The code was a black box—we had to rewrite everything from scratch".
- Ethical Concerns: AI models sometimes plagiarize open-source code. A 2025 lawsuit alleged a startup’s "original" AI-generated app copied 80% of a GitHub repository.
The Future: Where Do We Go From Here?
Short-Term Trends (2025-2027)
1. AI Code Security Standards Emerge
- Organizations like OWASP and NIST will release frameworks to audit AI-generated code for vulnerabilities like hallucinated functions or poisoned libraries. Tools like GitHub Copilot will integrate automated security scanners to flag unsafe patterns in real time.
2. Prompt Engineering Becomes Core to Developer Education
- Coding bootcamps will teach "secure prompt engineering" to minimize AI-generated vulnerabilities. For example, learners will master prompts like, "Generate a Python function to hash passwords using bcrypt, with input validation and error handling" to reduce insecure outputs.
3. Cybersecurity Teams Battle AI-Powered Botnets
- AI agents will autonomously exploit vulnerabilities in IoT devices (e.g., routers) to launch coordinated attacks. Defenders will counter with AI-driven threat-hunting tools to detect anomalies in real time.
4. Regulations Target Shadow AI in Development
- The EU’s AI Act will mandate audits for AI coding tools used in critical systems. Companies will ban unauthorized tools like ChatGPT for code generation to prevent data leaks
Long-Term Predictions
1. Domain-Specific LLMs with Built-In Security
Healthcare and finance industries will adopt specialized LLMs trained on secure coding patterns (e.g., HIPAA-compliant data handling). These models will reject prompts that could generate vulnerable code.
2. AI Literacy as a Cybersecurity Requirement
Junior developers will need certifications in "AI-safe coding" to enter the field. Employers will prioritize candidates who can debug AI outputs and validate code against OWASP Top 10 risks.
3. AI Agents vs. AI Defenders Arms Race
Threat actors will deploy multi-agent AI systems to automate phishing, exploit discovery, and data exfiltration. Defenders will respond with autonomous AI "guardrails" that patch vulnerabilities before deployment.
4. Universities Phase Out Traditional Coding Courses
Computer science programs will replace introductory Java/Python classes with courses on AI collaboration, secure prompt design, and computational ethics.
Expert Opinions
Proponents: Simon Willison argues vibe coding empowers "everyone to automate life’s tedium". He built 80+ experimental tools this way, including a voice-controlled home automation system.
Skeptics: Gary Marcus warns, "Vibe coding without oversight is like letting a self-driving car navigate Manhattan during a parade".
Conclusion: Vibes Can’t Replace Vigilance
Vibe coding is neither a panacea nor a pariah—it’s a tool. For weekend projects or brainstorming sessions, it’s revolutionary. However, as the industry learns from early missteps, best practices are emerging:
- Validate Relentlessly: Treat AI code like an intern’s work—test every edge case.
- Hybridize: Blend AI speed with human oversight, especially for sensitive systems.
- Stay Curious: Use vibe coding to explore new languages but deepen your understanding through traditional study.
As we stand at this crossroads, one truth endures: Technology should enhance human capability, not replace critical thinking. Whether vibe coding becomes a footnote or a new chapter in software history depends on how wisely we wield it.
Top comments (6)
nor, one must add, useful.
Yes yes, I know, people have built real applications with it and all that, but consider: a) it's all plagiarism and b) it's a bubble.
Not a single useful thing has ever been vibe-coded with a profitable AI built on ethically acquired training data. No, seriously, not a single time has that happened. Saying AI can build software for you is like saying a coat hanger can build you a new car. No, you can just use it to steal a car. Except coat hangers are at least cheap to make, while AI is not.
So an alternative development from what you propose, in a sane world, would look like this:
The AI bubble continues to grow for a while, causing more and more harm to society and using up more and more resources as investors continue to expect someone to find a profitable use case any moment now.
Eventually, legislation catches up and more and more places introduce restrictions on the sourcing of training data, making it difficult if not impossible to sell AI that has been trained on data scraped without consent.
At some point, the bubble bursts. Investors lose faith that a use will be found that can earn them more money than they spent on development. Losses are cut and AI dies out just like any other tech hype.
By that time, the next snake oil bubble will likely already be hyping up the entire tech sector and the cycle will repeat.
Thanks for such a thoughtful comment! You raise good and important questions about data ethics, plagiarism, and the danger of an AI bubble. It’s not possible to ignore that AI coding tools have issues, particularly around training data and overhype, but there are already some practical uses, like accelerating prototyping and making coding more accessible. I do, however, agree that improved regulation and ethics are essential, and the trajectory of vibe coding will really hinge on how we deal with these problems. It’s not ideal, but it’s not entirely worthless either. I appreciate your skepticism in it — it's necessary in order to keep the discussion unbiased!
Vibe coder are NOT coders; calling a vibe coder a coder is like calling someone who randomly connects electrical cables until the house DOESN'T burn down, an electrician.
This is the new "coding boot camp" - job security cleaning up the mess they leave behind for people who ARE real coders.
You bring up a valid point concerning code quality, and the dangers of a half-learned systems. Vibe coding can result in sloppy outputs if misused, as early no-code tools did. However, a lot of developers (as we can see in the r/ClaudeAI threads) replace their need for time with AI while still overall going through the code. The trick is not to just throw the tool away, but to put guardrails around it — like requiring mandatory code reviews or security scans — so vibe coding enhances skills and doesn’t replace them. Even old-school coders have something to gain from AI doing the boilerplate, the grunt work, so they can work on complex logic. It's not so much labels as it is responsible use.
“Democratizing development”? I get the intention. I disagree with how you phrased it. Should we start calling people who diagnose themselves with ChatGPT “vibe doctors” too? Because sure, it sounds noble to “democratize” medicine — until the model hallucinates, as it often does, and someone pays the price. Just like people felt they didn't need to drive anymore when self-driving cars started becoming a thing. How many people died or were seriously injured since then? I love the technology, I hate the rush for money based on the hype of the moment. Maybe that was actually "vibe driving" and we are only naming it now. But the intention — again, I really get it. I understand the idea that people can now build little things for themselves. That’s fair. After all, "democratize" comes from the Greek "give power to the people", or something like that, right? Give people the same opportunities, the same rights, the same voice. But it's still not the right word. With the advent of the internet, was there ever something more democratic than being a developer, with the vast knowledge available out there to learn how to read code? How to write code? But I won't focus on the misuse of that word. I'll go further than that.
There are plenty of one-off tools and silly automations out there where “vibe coding” might get the job done. And yeah, that’s cool in a way — someone solves their own problem without having to study for years. That’s real empowerment, to a degree. Many times it also mistakenly empowers people to think they can effortless innovate. But it doesn’t scale. It breaks. It hallucinates. And it fuels this thing I call “vibe delusion”: people who don’t really know what they’re doing, building “vibe apps” for “vibe users,” and charging for a service they can’t support. When something goes wrong — and it will — they won’t even know where to start looking. The person using it loses. The person building it panics. And the person profiting from it? They were never involved in the code to begin with.
But there’s a fine line between solving something for yourself and thinking you’ve become a builder of actual software. That line gets crossed fast. It's an illusion to think you've wrote something you can't read. As a kid, what did you learn first: to read, or to write? And that’s the core issue. You must know how to read and interpret what's written when you need to debug, for example. Debugging is a creative skill. You need to explore, guess, ask, test, but most of all, from my experience, you need to understand the system. But how can you, when you didn't even write it? How long of a context window do you think it requires for an LLM to try to work on a long spew of error logs while indexing your entire codebase? LLMs won't go through that whole debugging process intuitively. They don’t explore. They don’t debug. They don’t think. They are heavily trained to predict what’s most likely to come next in a sequence of tokens, based on patterns learned from massive amounts of data.
This isn't reasoning. We can't implement reason when we can't define it with complete precision. That’s approximation. If you’re happy with the margin of error in the output, and you actually understand what LLM benchmark tests measure — and how that race is going — let me take it a step further. These models aren’t optimizing for truth, understanding, or originality. They’re optimizing for probability. That’s it. You can layer benchmarks on top of that all day, but you’re still measuring how well a machine fakes fluency — not how well it understands anything. Ask ChatGPT. Ask Claude, Grok, Le Chat by Mistral, Copilot, Meta Llama — or whatever else is out there — if creativity can be formulated into a probabilistic function based on a pattern of tokens. They’ll give you an answer, maybe even one that sounds smart. But it won’t be theirs. It’ll be a statistical echo of something someone else once said, a pattern that feels right. Because creativity isn’t prediction. It’s not completion. It’s what happens when knowledge, emotion, memory, intuition, and experience crash together inside a human brain — a live system, constantly reshaped by everything it feels and remembers. No model knows what that’s like. And that’s the difference.
I'm 32 now, but I've spent most of my 20s working with machine learning infrastructure at Meta. I still use AI daily. But I correct its logic constantly. It speeds me up, sure — but it doesn’t understand anything. And that’s the difference. So no — this isn’t some revolution in development. It’s a shortcut for the “creator syndrome.” And this time, it’s not the LLM hallucinating — it’s the human. And calling it “democratization” makes it sound like progress when all it really does is reinforce the idea that you don't need to study, you can just delegate knowledge and enjoy a "vibe life", until no one remembers how things work — or worse, they don’t think they need to.
Next up: "vibe butt cleaning". Democratizing the craft of cleaning your butt.
You make good points about the dangers of reliance on AI coding tools. The “vibe delusion” you’re describing, where users misinterpret AI-generated code for actual understanding, is a legitimate cause of worry, particularly when apps are crashing or the bugs need fixing. As you observed, debugging requires systemwide understanding that LLMs can’t really replicate, as they optimize for probability, not logic. But the article's case for "democratization" isn't about taking developers' jobs but giving non-experts the ability to prototype ideas while they learn the basics. The trick is to marry the speed of AI with education — using it as a tutor, and not a crutch. Your Meta experience epitomizes exactly that: AI speeds work without substituting for critical thinking. The future is in tools that supplement skills, not shortcuts that undercut them. Thanks for contributing the important perspective!