Programming has always been about making computers do what we want. However, over the years, the ways we use to instruct computers have changed dramatically. In the early days programmers worked directly with machine code, which a tedious and error-prone process that involved binary instructions. The low-level Assembly language soon followed, which provided mnemonics that made programming a bit more human-friendly, yet this still required deep technical expertise and a strong understanding of hardware. The 1960s and 1970s saw the rise of procedural languages like FORTRAN, COBOL, and C. These languages introduced higher-level abstractions, which allowed programmers to focus more on problem-solving than on the intricacies of hardware.
As software systems grew in complexity, new paradigms emerged to address the limitations of procedural programming. Some of the most prominent programming paradigms in this direction include:
- Functional Programming: Functional programming treats computation as the evaluation of mathematical functions. It puts emphasis on immutability and pure functions. Languages such as Lisp, Haskell, and Scala championed this approach. Functional programming made it easier to reason about code, while avoiding bugs related to mutable state. Most importantly, it also facilitated parallel processing and set the stage for more robust and scalable software.
- Object-Oriented Programming (OOP): The next major leap was Object-Oriented Programming. OOP organizes code around real-world entities (i.e., objects), which encapsulate data and behavior. This paradigm was initially introduced by languages like Simula and Smalltalk. However, it gained popularity with the emergence and wide adoption of C++, Java, and Python. These languages brought modularity, reusability, and a more intuitive mapping between software and the real world. OOP’s main concepts (e.g., encapsulation, inheritance, polymorphism) have become foundational for modern software development and are widely used in modern software development.
- Fourth-Generation Languages (4GL) and Visual Tools: As the demand for rapid application development grew, Fourth-Generation Languages (4GLs) emerged. 4GLs (e.g., SQL, dBase, LabVIEW) offered even higher levels of abstraction. They focused on specific domains like database management, report generation, and Graphical User Interface (GUI) development. Visual programming tools and environments allowed developers to design applications by dragging and dropping components, which lowered more the complexity of application development.
- Modern High-Level Languages: The trend toward higher-level abstractions continues today. Modern languages like Python, Ruby, and JavaScript are considered “higher-level” than older languages like C and C++. As a prominent example, Python abstracts away memory management, offers a simple syntax, and comes with powerful standard libraries. This allows developers to build complex applications with fewer lines of code and less cognitive overhead, when compared to legacy lower-level languages like C and C++. The latter require manual memory management and a deeper understanding of the underlying hardware, which makes them more error-prone and less accessible to junior developers.
The New Era: Intent Computing and Vibe Coding
During the last couple of years following the emergence of ChatGPT and Generative AI, we have entered a new era where programming is less about telling the computer how to do something, and more about telling it what you want. This is the essence of intent computing. Instead of specifying algorithms and control flow, developers express their intentions in natural language or high-level prompts. Modern AI-powered computers can interpret these intentions and generates the necessary code.
Intent-based computing is transforming the developer experience. It enables rapid prototyping, accelerates learning, and makes programming accessible to a broader audience. This shift is laying the groundwork for the rise of “vibe coding.”
Vibe coding is an emerging approach to software development where programmers use natural language prompts to generate working code through AI models. Rather than writing every line of code, developers and non-developers describe the desired outcome (i.e., the “vibe”). Based on the “vibe” the AI fills in the technical details. The term was popularized by AI researcher Andrej Karpathy, who described it as “programming by vibe: “You describe the idea or intent and let the AI fill in the technical details.” Vibe coding leverages large language models (LLMs) like ChatGPT, Claude, or Code Llama to translate plain English instructions into functional code.
The vibe coding process is iterative: users describe what they want, test the results, and refine their prompts until the application matches their vision. Specifically, the main vibe coding steps include:
- Describing your goal i.e., Articulating in plain language what you want the software to do.
- AI that generates code by interpreting your intent and producing code snippets or event complete applications.
- Testing and refining i.e., Reviewing the results, providing feedback, and iterating with further prompts.
- Deploying or integrating, since once satisfied, you can deploy the application or try to integrate it with other systems.
The Benefits of Vibe Coding
Vibe coding democratizes software development. Anyone with an idea can harness the power of AI to build prototypes, test concepts, and even launch simple applications. This requires no longer deep programming expertise. Some of the key benefits of vibe coding are found in the following areas:
- Rapid prototyping: With vibe coding it is possible to build MVPs (Minimum Viable Products) in days or even hours, not weeks.
- Lower barrier to entry: Non-developers can bring their ideas to life without learning complex syntax or frameworks.
- Enhanced creativity: Vibe coding’s focus on intent helps users to experiment and iterate quickly, which enables them to explore new ideas without technical constraints.
- Increased productivity: Developers can offload boilerplate coding to AI. In this way, they can free up time for higher-level design and problem-solving.
- Empowerment: Vibe coding gives anyone “developer powers,” which unlocks innovation across industries and communities.
Understanding The Limitations of Vibe Coding
Despite its promise, vibe coding is not a panacea. It has significant limitations, especially for projects that require robustness, security, and scalability. Some of the most prominent limitations concern:
- Security and Architecture: AI-generated code may contain bugs or security vulnerabilities that require human oversight. Vibe coding does not automatically enforce proper security architectures or best practices, which makes it risky for production systems without expert review.
- Integration and Maintainability: Projects built through vibe coding can be difficult to integrate with larger codebases or existing systems. The generated code may lack modularity, clear documentation, or adherence to established conventions. In most cases this complicates future maintenance and evolution.
- Design and User Experience: AI may over-engineer features, introduce unnecessary complexity, or even miss subtle aspects of good user experience design. Human judgment is still essential for refining and validating the final product.
- Scalability and Evolution: While vibe coding excels at building MVPs and prototypes, scaling these projects into robust, maintainable products remains challenging. The code may not be easily extensible, and evolving the application over time can become cumbersome.
Vibe Coding Today and Tomorrow
Overall vibe coding is revolutionizing how we build MVPs and validate ideas. Many startups and solo founders use vibe coding to test concepts, gather feedback, and iterate rapidly before investing in full-scale development. It’s a powerful tool for experimentation and learning. Nevertheless, it is not yet a replacement for traditional software engineering when it comes to building secure, scalable, and maintainable systems. Looking ahead, AI models are likely to become more sophisticated, which may waive some of the limitations of vibe coding. We may see a future where anyone can build production-grade software simply by describing what they want. This could give rise to a new era of creativity, empowerment, and innovation in programming. Nowadays vibe coding is about building MVPs and exploring what’s possible. However, in the medium and long term it may change the way we write code and the very definition of what it means to be a developer.
