There’s been a vibe shift in tech. Every job posting mentions AI, every startup pitch deck has “AI-powered” on slide two, and every developer is wondering if they’re about to be automated out of existence. So maybe it’s fitting that the latest development trend is literally called vibe coding, the process of using large language models to generate code based on simple natural language prompts. 

In the 6 months since vibe coding exploded across social media, it’s gone from a somewhat gimmicky hack to a development approach that’s reshaping how software gets built. Indie developers are launching apps faster than ever, product managers are building their own prototypes, and even enterprise teams are quietly using it to speed up mundane tasks. The practice has become so mainstream that Meta reportedly allows vibe coding during job interviews, according to a recent article in WIRED. 

Vibe coding is doing something significant: it’s collapsing the distance between having an idea and building it. Where coding once required months of syntax mastery before you could create anything meaningful, AI tools like Claude, Lovable, and v0 let you jump straight to the building part.  

This democratization of building is personal for us at Codecademy. We were founded on the belief that everyone should learn how to code and be able to build something. Vibe coding gets us much closer to that vision. People are building solutions to their own problems in real-time: automating repetitive tasks, creating custom tools for their teams, and testing business ideas that previously would have required months of development. The barrier between having an idea and testing it has never been lower. 

Instant gratification comes with a catch, though. Without basic software knowledge, you can’t tell if the AI’s code actually works or why it fails. AI will happily generate broken, inefficient, or completely wrong code if your prompts lack precision. Even when the code appears to work, making specific changes becomes frustratingly difficult — you can keep regenerating, but you might never get exactly what you want. 

In our opinion, there’s a time and a place for vibe coding. Our new course Intro to Vibe Coding, teaches you best practices and prompting tips, plus gives you a chance to ideate your first vibe coding project with an AI assistant. Read on to learn what vibe coding actually is, when it works (and when it doesn’t), and how to get started the right way. 

What is vibe coding? 

Andrej Karpathy is the AI educator who popularized vibe coding in a post on X in February. He described a type of coding where “you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”  

In practical terms, vibe coding means describing what you want to build in natural language and letting an AI tool generate the actual code. For example, “Make me a to-do app with a dark mode toggle.” Instead of writing functions and debugging syntax errors, you iterate through conversations, refining your prompts until the AI produces something that works. (It’s the kind of movement that makes computer science professors cringe and startup founders curious.) 

The tools enabling vibe coding combine two existing technologies: AI code generation and live preview environments. What feels revolutionary — prompting an AI and immediately seeing a working app — is really just the integration of AI assistance with browser-based development environments that provide instant feedback.

This explains why vibe coding appeals more to newcomers than seasoned professionals. Experienced developers already have fast, local feedback loops in their development environments, so tools like Cursor in agent mode or Copilot can provide similar prompt-to-working-code experiences without the browser constraints. In many ways, vibe coding serves as training wheels for software engineering — lowering barriers for new projects and new developers who haven’t yet set up sophisticated local tooling.

The appeal may vary by experience level, but the adoption numbers don’t lie. In the 2025 Stack Overflow Developer Survey, 51% of professional developers said they use AI tools in the development process daily. This rapid adoption raises an obvious question: is this transformation good for software development? 

Is vibe coding bad?  

Despite widespread AI use, the majority of developers are not vibe coding, according to the Stack Overflow Developer Survey. Codecademy shared a LinkedIn poll recently asking whether vibe coding in interviews is the norm and 73% of respondents said no.   

Vibe coding has two main problems: security vulnerabilities and a complexity ceiling. AI-generated code often contains security flaws or uses outdated packages, making it risky for sensitive applications. It also breaks down as projects get complex. While perfect for simple prototypes and single-use apps, vibe coding struggles with applications that need long-term maintenance, high reliability, or complex component interactions. When it comes to building the mission-critical systems that keep businesses running, vibe coding falls short.  

These aren’t trivial concerns. When AI-generated code fails in production or introduces security vulnerabilities, the consequences can be severe. And the fear that vibe coding creates a generation of developers who can’t debug, optimize, or understand their code feels increasingly valid as more people rely on AI to do their thinking for them.  

Some people are finding a middle ground. Masha Rose, a Codecademy learner, describes her evolution perfectly: “I used to copy code from my overlord ChatGPT, paste it, watch it break… then politely tell it its own code had an error,” she wrote on LinkedIn. After learning Python basics, “now when Claude or ChatGPT throws me some code, I don’t just paste and pray. I get it. I see the sneaky syntax bugs,” she says. Her experience shows that even basic coding knowledge transforms how effectively you can use AI tools. 

Critics also argue that vibe coding isn’t “real” coding, and they may have a point. Coding typically refers to the act of writing syntax in a programming language, while programming is the broader process of solving problems and creating software solutions. Vibe coding might not be traditional coding, but it’s definitely programming. This distinction misses the bigger picture about how programming has always evolved. “The most advanced coding has largely remained unchanged for decades,” says Judah Anthony, Senior Director of Consumer Engineering at Codecademy. “However, each generation adds layers of abstractions to hide away the details of the machine, so we can stay focused on the logic of the problem.”  

Think about it: Early languages like C and C++ made code work across different machines. Later, languages like C# and Go handled memory for you. Then dynamic languages made it easier by managing data types behind the scenes. Visual programming tools like Scratch introduced block-based coding, while no-code and low-code platforms let people build without traditional syntax. Now, vibe coding goes a step further by letting you focus on what you want the code to do, rather than how to write it. 

Despite the valid worries about skill gaps and security risks, this progression serves a larger purpose. “Code has always been on a spectrum,” Judah says. “Vibe coding simply pulls forward that entry point, bringing it closer to everyday knowledge and thus more accessible to everyone.” This democratization means a marketing manager can build a landing page tester, a teacher can create a grade calculator, or an entrepreneur can prototype their MVP — all without formal coding skills. 

How to start vibe coding 

“Vibe coding will reduce what you need to know to get results, but those who know will always have an advantage over those who don’t,” Judah says.  

It’s worth remembering the full context of Karpathy’s original description. He ended that viral post by calling vibe coding “not too bad for throwaway weekend projects, but still quite amusing.” Three key words there: it produces “bad” code that’s inherently “throwaway,” but it’s undeniably “amusing.”  

Karpathy wasn’t advocating for vibe coding as the future of software development; he was describing a tool that’s perfect for experimentation and learning, not production systems. The reason vibe coding works best for throwaway projects becomes clear when you consider that English (or any natural language) isn’t a great programming language. The more specific you want to be, the more you need simple, structured, unambiguous instructions. Try to make English do that, and you’re essentially reinventing programming languages. 

But here’s what vibe coding does brilliantly: it lowers the barrier to entry for new creators who can approach programming with fresh eyes and no preconceptions, and creates a new kind of collaboration between human creativity and AI capability. Instead of needing to master syntax and frameworks, you bring your domain knowledge, problem-solving instincts, and creative ideas, while the AI handles the technical implementation you don’t know. The trick is learning effective prompting techniques before you dive in, which is why we built Intro to Vibe Coding to teach you how to communicate clearly with AI tools, break down complex requests, and iterate on results that work.  

The real test comes when you hit your first wall. When you go from that rewarding “aha” moment to a troubling “uh oh” moment — when prompting alone can’t solve your problem — that’s when foundational coding knowledge becomes invaluable. Understanding how software works helps you debug AI-generated code, optimize performance, and build systems that scale. Vibe coding may have changed the game, but it hasn’t changed the rules: the developers who thrive are still the ones who know what they’re building, not just how to ask for it.