In the world of software development, improving efficiency without compromising code quality is the goal. Generative AI is the latest developer tool used to improve software and shorten time-to-market for new features.
Both experienced and novice coders can now increase productivity using GenAI platforms to write new programs using natural language queries, a new approach called vibe coding.
With vibe coding, virtually anyone can create functional software code using natural language prompts. Vibe coding speeds software development, automating the creation of new applications that can be tested and refined.
Vibe coding also makes it easier for those with limited programming experience to develop new apps. But just because inexperienced developers can now write AI-generated software, should they?
Less experienced developers are using vibe coding to shorten development time, but that can lead to other problems. For example, AI-generated code may overlook security concerns, integration issues, or performance bottlenecks.
It takes an experienced programmer to provide the technical context needed to create robust software using AI coding. Novice programmers can use AI to write software, but they will leave gaps because they don’t know what they don’t know.
Experience Is Required
To illustrate, consider the recent Tea app hack. Tea Dating Advice was launched in 2023 as a women-only site to give members access to background checks on men they date.
In a data breach, a hacker accessed 72,000 images stored in an archived data system, including 13,000 photos used for IDs and 59,000 user-submitted images. The data breach was the result of archiving the images in a misconfigured Firebase storage bucket.
AI coding models are security-aware in the right context. Instructing an AI coding system to store images in a public Firebase data repository is not inherently wrong and may be a reasonable choice for scale and performance.
When the developer instructs a vibe coding platform that the data is sensitive and subject to privacy laws, the AI may recommend a more secure solution.
It requires an experienced developer to provide the right prompts. An expert programmer understands the tradeoffs between security, performance, and architecture, filling in the gaps in an app created using vibe coding.
The Evolution of Vibe Coding
Andrej Karpathy coined the term “vibe coding” in early 2025 to describe how AI can be used to write software. The idea is that, rather than coding line by line, vibe coding creates, refines, and debugs the application, freeing developers for more strategic tasks.
There are two ways to apply vibe coding:
Pure vibe coding: Some developers rely solely on AI as an exploratory form of application development, enabling rapid ideation and experimentation.
AI-assisted coding: AI can also serve as a collaborator. The developer guides the AI to write the code, then reviews and tests it to understand how the code functions.
New AI-powered code generators like Replit, Claude, and Cursor have hit the market to aid developers. These code generators are built on large language models (LLMs) for coding and trained using massive amounts of code. Developers simply describe what they want to do, and the code generator creates an application to be refined and tested.
Vibe coding is fostering a new type of development culture where you code first and refine later. However, vibe coding still needs the human in the loop to provide creativity and out-of-the-box thinking.
The Limitations of Vibe Coding
While AI coding can be a great productivity tool, it still has limitations, even in the hands of seasoned programmers. Some of the real-world shortcomings include:
Inability to deal with technical complexity: Like all AI, vibe coding engines draw from existing data, which works well with standard frameworks. Sophisticated applications may have requirements that are unique or complex.
Poor debugging: AI-generated code can be hard to test and debug since it’s dynamic and lacks architectural structure.
Code quality and performance: Vibe coding is great for prototyping and testing, but AI-generated code still needs to be optimized and maintained. For example, vibe coding can be problematic for distributed applications that use a structured level architecture and sophisticated optimization strategies.
Maintenance challenges: Vibe coding threatens to add to technical debt. All software requires regular maintenance. To maintain AI-generated code, programmers must understand its structure and underlying logic. With vibe coding, it’s difficult to know what’s going on under the hood.
Security issues: Apps written using AI may leave vulnerabilities. Security issues can go undetected if the code isn’t carefully reviewed.
Applying Vibe Coding in Practice
Despite these limitations, vibe coding offers several advantages, such as fast prototyping using natural language instructions. This problem-first approach is a departure from conventional coding, allowing developers to focus on functionality rather than the tech stack.
Vibe coding can also reduce risk while maximizing impact. Businesses can experiment with ideas and gather feedback with minimal investment. Once new concepts have been validated using vibe coding, additional resources can be committed for commercial development.
And vibe coding increases productivity. Now developers can apply multimodal programming, combining voice, visual, and text to create new apps. AI is driving new trends such as voice-driven and visual programming interfaces that are more intuitive and increase flexibility.
Just because vibe coding simplifies software development, is it responsible to release code no one truly understands? Competitive pressure and the desire to shorten time to market encourage shortcuts.
It’s tempting to deploy AI-written software without a human in the loop to apply proper analysis and debugging, which increases security risks and undermines software maintenance.
The easier it is to write new applications, the more code is created. The sheer volume of code can become an issue, especially in maintaining a knowledge base. Widespread adoption of vibe coding may also have lasting consequences for education and coding skills.
Alternative Development Strategies
When you take a step back, you realize that vibe coding is just another means of abstracting machine code – something developers have done since the birth of software. There are other ways to gain the same efficiencies without sacrificing control over the code.
No-code platforms are appealing to businesses looking to develop complex applications with little developer expertise. No-code makes it easy to visualize workflows, assembling proven code modules to deliver the desired functionality.
While no-code tools lack the total freedom of vibe coding, they put developers in control, allowing them to create using tested building blocks, shortening development time without sacrificing code quality.
Applications built using no-code solutions offer agility and flexibility and are generally easier to maintain. They also eliminate technical debt, relying on the no-code provider to take responsibility for machine functions.
No-code users can create applications, adapt workflows, and add functions without writing a single line of code.
App development is evolving, and businesses can weigh the pros and cons of vibe coding versus no-code solutions. Vibe coding relies on AI, which means all the limitations that go with it, such as failing to ask the right questions or incomplete responses.
No-code solutions offer more control and are built on the work of experienced developers, albeit with limitations of functionality and scalability. No matter how you choose to approach application development, it pays to have experienced developers behind the wheel.
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