Vibe Coding: Giving Good or Bad Vibes?
What is Vibe Coding?
Vibe Coding, a term coined by OpenAIco-founder Andrej Karpathy, is a new style of programming where developers use natural language (human language) to instruct AI systems to write, edit, or debug code.
Instead of traditional programming, where you write syntax line-by-line, you now “vibe” with an AI, collaborating in real time to bring software ideas to life.
For example, asking an AI assistant to build a dashboard showing customer rates of unsubscribing to a service by region; with vibe coding, the assistant will generate, manage, and even analyze the necessary code.
Your role is describe what you want in plain language. This approach shifts the focus from knowing how to code to knowing what to build. It motivates not just developers, but also designers, product managers, and entrepreneurs to quickly turn ideas into prototypes or fully functional tools—without needing deep technical expertise. It’s a blend of improvisation, rapid feedback, and creative problem-solving, where the human sets the vision and the AI handles the challenges to the vision.
Examples of Vibe coding
There are a slew of things that vibe coding entails. Here are some specific examples of vibe coding software and their abilities. The list is ever changing and companies keep adding capabilities so it is best to do your own research for your use cases:
- Build a complete web app (Replit)
- Auto-suggest functions while typing with (GitHub Copilot)
- Generate a complete react app (ie. Social Media) from a sentence (Bolt.new)
- Debug broken code (Claude AI)
- Add features to an existing project (Cursor)
Vibe coding isn’t just a futuristic concept, it’s a practical and accessible way to build software faster and smarter. Whether you’re prototyping a full app, fixing bugs, or adding powerful features with a sentence, unlocking new levels of creativity in many, regardless of skill.
How Vibe Coding Works
Vibe coding relies on powerful large language models (LLMs) like GPT-4, Claude, and specialized tools like GitHub Copilot, which functions as an AI programmer.
These systems are trained on massive datasets of code and documentation, enabling them to understand both natural language and programming syntax (the structured rules that govern how code must be written for computers to process it).
This allows developers and even non-developers to generate working code simply by describing what they want the software to do in plain language.
The typical vibe coding process follows four steps:
- First, prompt the AI with a description of your desired functionality.
- Second, the AI generates the corresponding code.
- Third, you refine the output by giving feedback or tweaking the code manually.
- Finally, you test the result, either on your own or as part of a larger system.
This dramatically speeds up development and shifts the focus from manual coding to higher-level tasks like user experience, system design, and creative problem-solving. It also opens up coding to a broader audience, lowering the technical barrier for prototyping digital products. However, as the AI handles more of the technical heavy lifting, the need for strong testing, review, and quality control becomes even more critical.
Tools for Vibe Coding
Several platforms now support vibe coding, each offering unique capabilities that make software development more accessible, faster, and more intuitive.
Cursor, for example, is an AI-first integrated development environment (IDE) built on top of Visual Studio Code.
It seamlessly incorporates AI models like Claude to help write, refactor, and understand code across multiple files, making it especially powerful for solo developers or small teams. Claude, from Anthropic, serves as a general-purpose AI assistant, but in the coding context, it excels at generating clean, readable code and explaining complex logic in simple terms, making it a favorite among both technical and non-technical users.
GitHub Copilot, integrated into popular code editors like VS Code and JetBrains, acts like an always-on pair programmer, suggesting lines or entire blocks of code as you type. It’s particularly helpful for boilerplate, repetitive tasks, or when exploring new frameworks and APIs. Then there’s Bolt, a browser-based tool that turns natural-language app ideas into deployable code, often in minutes, making it ideal for fast prototyping and MVP development.
Other notable tools include Replit, which provides a full cloud-based development environment with built-in AI support, and V0 by Vercel, which generates polished UI components from plain English descriptions.
These tools lower the technical barrier by allowing users to interact with software development through conversation, not syntax. The result is a shift in how we build—less time spent on the mechanics of code, and more on creativity, iteration, and user experience.
Benefits and Risks of Vibe Coding
There are many advantages to using vibe coding over standard programming, but with that also comes some disadvantages too. Here’s a list of pros and cons to vibe coding:
Benefit:
- Ultra-fast prototyping: You can build simple apps or games in a few hours instead of weeks.
- Low technical barrier: People with no coding background can build functional apps and plain language prompts.
- Democratization of development: More people, including non-technical founders, designers, and indie hackers, can create digital products.
- Accelerates creativity: You can experiment freely without worrying about complex syntax or setup.
- Reduces boilerplate: Skips repetitive coding tasks, letting users focus on ideas and structure.
- Speed to market: Great for testing ideas quickly and validating concepts without deeper development.
- Empowers technical developers: Experienced coders can work faster, delegate routine tasks to AI, focusing on higher-level thinking.
- Increased accessibility: Helps young people and others explore software development in an approachable way.
- Industry shift: Success will begin to depend more on creativity and product sense, not just raw coding labor.
- MVP-friendly: Ideal for creating minimum viable products and weekend projects.
Risks:
- Security vulnerabilities: AI-generated code may have hidden security flaws, especially when users don’t understand what’s being created.
- Technical debt: Code created quickly can be messy, making it hard to scale or maintain.
- Black-box logic: It’s easy to lose track of what the AI has built, especially if you don’t review each file manually.
- Over-reliance: Beginners might skip learning the “why” behind code, leading to a lack of true programming understanding creating subpar results.
- Scalability issues: Quickly built projects might not perform well under pressure or handle large user bases.
- Low code quality: Without best practices, the resulting code can be difficult to read, debug, or improve.
- Poor maintainability: Working with other developers becomes difficult due to lack of structure and documentation.
- Unpredictable outputs: The results may work now but break later, especially without tests or structured architecture.
- Flood of low-quality products: Easier access may lead to a rise in gimmicky apps, cluttering the digital space.
- Missing foundational skills: Non-coders may struggle with the last bit of a project, the part that requires deep knowledge to finish or fix.
There’s also specific examples of issues that have arisen because of vibe coding’s new-found fame and vulnerabilities.
With over 25% of Y Combinator startups and Google’s new code now being written by AI, this unlocks incredible speed, allowing teams to go from idea to working prototype in hours.
However, this also raises key risks. Code is often deployed without human overview, increasing the chance of bugs, security flaws, or technical debt.
Unlike legacy code that has proven itself over time, new AI-generated code hasn’t earned that reputation. Developers may struggle to review or test code they didn’t write, making quality maintenance harder.
At the same time, the volume of code is growing fast, adding pressure to manage and secure it. While engineers can navigate this shift, it may limit learning opportunities for new devs who miss hands-on coding experience.
To adapt, businesses should upskill engineering leaders, establish clear policies for AI use, and rethink how teams build and review software responsibly so that all mentioned risk will be negated.
Vibe Coding as a Force Multiplier for Great Engineers
Vibe coding is an excellent tool for rapid prototyping—it allows ideas to be brought to life quickly and with minimal friction.
However, its true power lies not just in prototyping, but in serving as a springboard for more robust development.
The best results come when a minimum viable product or prototype is first created using vibe coding, and then reviewed and refined by experienced programmers who can optimize performance, structure, and scalability.
The real promise of this technology isn’t in replacing engineers, but in amplifying them. It empowers already exceptional developers to work faster, think bigger, and achieve disproportionately impactful results with the same—or even less—effort.
It’s not about skipping steps, it’s about supercharging the people who know how to build.
The True Costs of Vibe Coding
The cost of vibe coding can vary widely depending on the tools and AI models you use. Services like GPT-4 charge based on token usage—averaging around $0.03 per 1,000 tokens—while subscription-based tools like GitHub Copilot typically run about $10 per user per month. More advanced platforms, such as Cursor, may offer tiered pricing based on features, collaboration tools, or usage limits. Many of these services also include free access tiers, making them especially appealing for solo developers, hobbyists, or early-stage startups looking to prototype quickly and affordably.
At this stage, vibe coding significantly reduces the upfront investment typically required for software development.
But as projects grow in complexity or involve multiple collaborators, the cumulative cost of API calls, AI processing, and premium platform features can escalate quickly.
This introduces a real risk: as a few dominant platforms consolidate control over the most effective vibe coding tools and models, pricing power may concentrate in their hands—potentially leading to monopolistic dynamics that lock developers into expensive ecosystems.
While the technology opens exciting doors for innovation and access, it’s important to remain vigilant about long-term cost structures and platform dependency.
Overall, if used wisely, vibe coding is still a powerful force for democratizing software development and accelerating progress—but its benefits must be balanced against the risks of centralization and rising operational costs.
Redefining Who Builds—and How
The future of vibe coding points toward a fundamental shift in how digital products are conceived, built, and who gets to build them. By abstracting away much of the traditional complexity of programming, it opens the door for designers, entrepreneurs, and domain experts to take a more active role in product creation—lowering barriers that have historically limited innovation to those with deep technical skills. This democratization, however, comes with risks: poorly structured or insecure code, overreliance on automation, and a potential devaluation of deep engineering expertise. Still, when paired responsibly with expert oversight and thoughtful design, vibe coding has the potential to dramatically accelerate innovation and make high-quality software more accessible than ever. Ultimately, it should be a net positive for humanity—empowering more people to bring meaningful ideas to life, faster and with greater impact.
Developers, do you “vibe code”? What’s your stance on this form of software development?
Share your thoughts to GAME PILL or Mike Sorrenti
#VibeCoding #AIProgramming #FutureOfCoding #CodeWithAI #ClaudeAI #GitHubCopilot #AIcreations #OpenAI #AI #ArtificialIntelligence
Sources:
https://www.analyticsvidhya.com/blog/2025/02/vibe-coding-the-future/?utm_source=chatgpt.com
https://www.geeksforgeeks.org/what-is-vibe-coding/?utm_source=chatgpt.com
https://zencoder.ai/blog/vibe-coding-risks
https://www.forbes.com/sites/nishatalagala/2025/03/30/what-is-vibe-coding-and-why-should-you-care/
https://arstechnica.com/ai/2024/10/google-ceo-says-over-25-of-new-google-code-is-generated-by-ai/
https://www.pixelmatters.com/blog/benefits-risks-vibe-coding
