What Is Vibe Coding? And What Does It Mean for the Future of Programmers?

AI tools can now generate scheduling applications, simple games, and small business websites from natural-language instructions, sometimes without users writing a single line of code. What began as an experimental approach is prompting wider discussion about how software is developed—and what role programmers will play in that process.

Computer scientist Andrej Karpathy named this practice “vibe coding” in February 2025. He described a hands-off style of AI-assisted development in which the person directing the work focuses on the desired result rather than examining the generated code line by line. The term is relatively new, but it reflects a broader shift toward intent-driven software development, in which natural-language instructions can guide AI systems in building and modifying applications.

For anyone considering a computer science degree, vibe coding raises an important question: what becomes of the programmer’s role when a machine can produce the code?

What Vibe Coding Changes

Vibe coding is one approach within the broader field of AI-assisted software development, but the distinction matters. At one end of the spectrum, programmers use AI assistants to suggest lines of code, functions, or solutions to specific problems while remaining directly involved in writing and reviewing the code. Further along, developers can delegate larger tasks to AI agents, such as building a database interface, generating tests, or developing parts of an application, while continuing to review the results and maintain oversight.

Vibe coding, as Karpathy originally described it, is different. The person directing the process focuses almost entirely on what the software does rather than how it is built. They run the application, identify problems, and ask the AI to make changes, often without reading the generated code. This lack of code-level inspection distinguishes vibe coding from more structured forms of AI-assisted and agentic software development.

The approach makes software creation more accessible. Someone with an idea but little programming experience can produce a working prototype, while an experienced developer may use AI tools to accelerate implementation. However, faster code generation does not necessarily mean teams can deliver complete, reliable software at the same pace.

A 2026 National Bureau of Economic Research (NBER) working paper offers useful context. Researchers analyzed data from more than 500,000 GitHub developers, combined with AI usage telemetry, to examine the productivity effects of successive generations of AI coding tools. They found that autocomplete tools increased coding activity, measured by commits, by 30%; interactive coding agents increased it by 180%; and autonomous coding agents increased it by 240%. However, these gains diminished further along the production process: the 240% increase associated with autonomous agents translated into an 80% increase in the number of projects and a 30% increase in actual releases.

The study examined AI coding tools broadly, not vibe coding specifically. Its findings suggest that increases in coding activity do not automatically translate into equivalent increases in completed software. The researchers interpret this pattern as evidence of strong complementarity between AI and human effort: AI can accelerate individual tasks, but human contributions remain important to moving work through the broader production process.

Chief Scientist at Thoughtworks Martin Fowler has written that vibe coding can be useful for disposable software built for personal use or a small group of collaborators. However, he warns that serious problems can arise when developers stop examining the generated code. These include incorrect logic, exposed credentials, security vulnerabilities, and poorly structured code that becomes difficult to maintain or modify.

The Changing Role of the Programmer

Predictions that AI will simply replace programmers overlook a more significant shift: the nature of programming work is changing. Routine implementation, boilerplate code, and some testing and debugging tasks can increasingly be delegated to AI systems. In more advanced workflows, developers direct AI agents, evaluate their output, and decide what needs to be corrected or improved.

Thoughtworks describes this emerging approach as supervisory engineering: the work of directing AI agents, evaluating their output, and ensuring that the resulting components function together as intended. Rather than removing the need for engineering expertise, this approach changes where that expertise is applied.

The ability to assess an AI-generated solution depends on understanding the problem it is meant to solve. Developers still need to judge whether an architecture is appropriate, recognize a poorly designed database or an insecure authentication process, and determine whether the resulting software behaves as intended. They must also identify when an AI-generated solution is incomplete, inefficient, or unsuitable for its intended context.

This shift raises particular questions about the development of entry-level programmers. Traditionally, junior developers have built experience by handling smaller coding tasks, debugging problems, and learning from more experienced colleagues. If AI systems take on a growing share of these routine tasks, organizations and computer science programs may need to rethink how early-career developers acquire practical experience. The challenge is not simply teaching people to use AI tools, but ensuring they develop the technical judgment to evaluate the work those tools produce.

Preparing students for this evolving landscape is an important consideration for University of the People’s Computer Science program. The curriculum establishes a foundation in programming, data structures, databases, operating systems, networking, and software engineering. Students also develop skills in designing computing systems and writing, debugging, and testing programming solutions. Alongside these technical foundations, the program emphasizes critical thinking about computer ethics, privacy, and security. Together, these areas of study help students understand how software works, assess technical decisions, and consider the implications of the systems they develop.

Vibe coding will continue to make software faster and more accessible to produce. But generating code is not the same as building software that is reliable, secure, and fit for purpose. As AI takes on more implementation tasks, the ability to understand, evaluate, and take responsibility for the resulting systems may become an increasingly important part of what it means to be a computer scientist.