Vibe coding today is no longer the solitary, late-night ritual of writing code for the love of it—headphones on, lo-fi beats in the background, hacking away in silence. It’s something else entirely: building apps with AI as your co-creator.

And it blows your mind when you first see it. An agent inside Cursor spits out app components from your prompt in minutes. Suddenly you have a product—UI, logic, features—that you can refine right there with new instructions.

A New Tool, a First Shock

The possibilities are staggering. You can spin up a landing page for a small business without hiring a freelancer. Great news for entrepreneurs, but a gut punch to the freelancers who used to make a living doing exactly that.

Even seasoned developers—often the fiercest critics of vibe coding—admit it makes refactoring components, updating apps, and shipping features faster and simpler. They don’t argue with that.

The term vibe coding itself landed in February 2025, when AI researcher and OpenAI co-founder Andrej Karpathy, formerly Tesla’s head of AI, posted on X about generating code from natural language and “vibes”—intuitive descriptions rather than strict syntax.

A Shift in Learning and Work

It’s fascinating to watch how quickly methods of learning and working flip when new tools arrive. When I was in my bootcamp, we were strictly warned not to use AI to build student apps. That ban actually helped me develop logic, structure, and an understanding of core concepts.

Now things look different. A friend recently told me he’s hooked on coding with AI and worries he’s losing both his skills and his job security. Meanwhile, my own bootcamp—which once forbade AI—now runs vibe coding workshops: how to craft prompts, how to squeeze the most out of LLMs.

Honestly, as someone working on my own project, I’m thrilled. By combining my dev skills with AI agents, I ship products in days instead of weeks, without pulling in colleagues for extra hands.

My humanities background helps too. Knowing how to work with words means I can craft precise prompts, cut down on wasted queries, and get exactly the result I want—even with extra details and quirks.

The lesson: vibe coding still demands programming logic and web development fundamentals. Your AI agent needs to understand what you’re asking for. And you need at least some communication skills to write good prompts—otherwise you’ll burn through tokens trying to explain yourself.

So, all the talk about anyone being able to build apps with vibe coding? It’s more hype than revolution. Plus, AI often “hallucinates,” spitting out buggy code that demands manual fixes, rewrites, and edits. Vibe coding isn’t magic—it’s a tool that speeds up development.

Juniors at a Crossroads

Here’s another twist in the story: the market is buzzing again about juniors. Job ads for entry-level roles are slowly creeping back. But the bar is higher now. Requirements include writing effective prompts for code generation and knowing how to review and polish AI-generated code. Prompt engineering has become a career edge—much like mastering TypeScript or React a few years ago.

AWS CEO Matt Garman pushed back hard against the idea that companies can just skip juniors:

That’s like, one of the dumbest things I’ve ever heard.

They’re probably the least expensive employees you have, they’re the ones leaning hardest into your AI tools. How’s that going to work when ten years from now you’ve got no one who’s actually learned anything?

GitHub CEO Thomas Dohmke made a similar point. Juniors, he argued, will remain essential precisely because they’re “AI natives”—a generation that grew up with these tools, using them in college or even earlier in school.

Zooming out, the World Economic Forum also notes a structural shift: more demand for architecture, verification, and security, and less for manual coding.

The takeaway: demand will rise for people who can orchestrate AI, verify its work, and refine the output. Cutting junior roles entirely isn’t just risky—it’s bad business.

Vibe Coding in Practice: How to Get Results, Not Chaos

Vibe coding is like a dialogue with a sharp colleague: you describe the task, the AI drafts the code, and you check and refine it. Sounds simple? It is—if you know the rules. Here’s a step-by-step guide to make vibe coding effective, so you end up with working code instead of a pile of bugs.

Five Rules for a Strong Prompt

• Give context. Name the environment: website, mobile app, or server.
  Example: “I need a form for a website in React” or “A Node.js API for processing orders.” The clearer the start, the less AI will hallucinate.

• Describe the result. Be specific. Instead of “make a form,” write: “A subscription form where the user enters an email. If valid, show ‘Thanks!’; if invalid, show ‘Incorrect email’.” Specifics are your best ally.

• Limit AI’s imagination. Don’t want it dragging in a heavy library or adding random animations? Say it directly: “No external libraries” or “Minimalist design only.”

• Ask for tests. Add: “Write a test that checks how the form handles valid and invalid emails.” This helps you catch bugs early and confirm the code works.

• Take small steps. Don’t ask for everything at once. First the form, then validation, then styles. It’s easier to stay in control and avoid drowning in rewrites.

Example prompt:
“Create a React component for an email subscription form. Use only built-in hooks, no external libraries. The form must validate the email and show success or error messages. Write a unit test for validation. Code should be readable and minimal.”

Four Habits That Save Time

• Check everything. Even if the code looks perfect, run it. AI often generates non-existent functions or forgets error handling.
• Save versions. Use Git or copy code into a separate file after each step. If AI derails, you can roll back.
• Log your prompts. Keep notes of what you asked and what AI delivered. It works like documentation—easy to return to a good solution or explain to a teammate how you got there.
• Don’t trust blindly. Ask yourself: “Would I understand this code a week from now? Is it maintainable?” If not, ask AI to simplify or rewrite it.

Where Vibe Coding Breaks

AI isn’t magic. Here’s where it can trip you up:

• Hallucinated code. AI invents functions or outdated methods, especially with newer frameworks.
• Scaling issues. Code that works in a demo might collapse under real traffic or with a live database.
• Tool lock-in. If you rely on one tool (say, Cursor), switching to another can throw you off.
• Security. A careless prompt might leak sensitive data—like passwords or API keys—straight into your code. Always check.

💡 Pro tip: To avoid bugs, add to your prompt: “Check the code for vulnerabilities and make sure it doesn’t use deprecated methods.”

How to Tell If Code Is Good

Good AI-generated code should:

• Pass tests and not crash on basic scenarios (like invalid email input).
• Be readable: you should understand it a month later without a headache.
• Avoid bloat: no unnecessary libraries or 100 lines for a simple task.
• Run smoothly: check performance, especially on the frontend.

Example check: If you got a form, test it with a valid email, an empty field, and random symbols. Make sure error messages appear correctly and the page doesn’t lag.

Think Before You Ship

Vibe coding isn’t a replacement for logic, it’s an accelerator. It works if you’re attentive, know the basics of programming, and can formulate tasks clearly. Without that, AI will hand you pretty but useless code. Think, test, refine—and vibe coding turns into a super-tool.

The Hidden Pitfalls of Vibe Coding

Vibe coding is a powerful tool, but it comes with traps. Ignore them, and instead of faster development you’ll get chaos. Here are five of the biggest risks—and how to avoid them.

• AI hallucinations. AI often sounds confident while making things up: fake functions, methods, or libraries. It might suggest useMagicHelper() out of nowhere, or code that runs in a demo but breaks in production because of a framework mismatch.
  Fix: check the code immediately. In your prompt, point to the exact docs (e.g., “use React 18, no external libraries”). Split tasks into steps: base functionality first, improvements later. Always add tests to catch bugs.

• Unreadable code. AI code can look like an intern’s first attempt: messy styles, weird variable names, extra lines. A month later, you won’t even understand it yourself, and your team will say: “Easier to rewrite.”
  Fix: set clear rules: “Variable names in camelCase, functions no longer than 20 lines.” Save prompts in a file (say, prompts.md). That file becomes documentation: a record of what was generated and why.

• Security problems. AI doesn’t care about protection. It can generate code with XSS holes, SQL injections, or even slip your API key into the output if you mentioned it in the prompt.
  Fix: never put secrets into prompts—use proper storage. Ask explicitly: “Make sure the code is safe against XSS.” And run security scanners in your CI/CD pipeline.

• Tool dependency. Maybe you’re used to Cursor. But if tomorrow you have to switch to another model, your whole workflow can collapse. Different models parse prompts differently. It’s cloud lock-in all over again—migration is painful.
  Fix: create reusable prompt templates. Log your prompts and outputs. Plan ahead for tool migration if one shuts down or goes paywalled.

• Legal risks. AI can “slip in” fragments that break licenses. You think you wrote the code, but it’s lifted from an open-source project with strict terms.
  Fix: run license checks with tools like Dependabot or Snyk. In prompts, specify: “Use only MIT-compatible code.” Always review before committing.

Can AI Replace Developers—and When?

For now, the answer is no. Not fully. Vibe coding handles routine brilliantly: building a CRUD app in an hour, fixing a bug, writing unit tests is much faster. But full replacement is another story.

Today, AI is the copilot. It drafts templates, finds bugs, speeds up testing. GitHub’s numbers are clear: developers using Copilot complete tasks 55% faster, especially in JavaScript and Python. But the pilot’s seat—the architect’s role—remains human. AI doesn’t understand business context, can’t take responsibility for a product, and doesn’t run infrastructure. As GitHub CEO Thomas Dohmke put it:

AI is a tool, not a replacement for critical thinking or domain expertise. It can generate code, but it doesn’t understand the ‘why’ behind your product.

Some researchers predict that in 10–15 years we’ll see truly autonomous AI agents able to plan architecture, write and test code, release updates, and monitor production systems without human help. McKinsey’s 2023 report projected that by 2030, up to 30% of developer tasks could be automated by generative AI. Bolder forecasts, like Jason Kwon’s (xAI, 2025), suggest:

By 2040, we could see AI agents capable of end-to-end software development for simple applications, but human oversight will remain critical for complex systems.

Even then, these scenarios mostly cover niche cases: prototypes, internal tools, simple landing pages. In big products—banking systems, healthcare platforms, SaaS with millions of users—humans aren’t going anywhere. Security, legal compliance (GDPR, HIPAA, CCPA), and integration with legacy systems keep people in the loop.

The World Economic Forum’s 2025 Future of Jobs report lays out the balance: AI will create 11 million new IT jobs by 2030, while eliminating around 9 million—mostly through automating routine tasks. Which means demand shifts toward “AI orchestrators”: people who can manage AI, verify its output, and weave it into complex systems. Or as the report puts it:

The future of coding isn’t about replacing developers; it’s about augmenting them. Developers who can orchestrate AI tools will be the most valuable.

The Bottom Line

Vibe coding is a powerful accelerator, but its magic still depends on thinking. It melts the ore, but it takes a craftsman’s hand to shape the metal. For now, AI may run the mines—but the miner is still on stage.