Vibe Coding’s Big Promise Meets Software’s Growing Debt

Software development has entered a strange new phase: people are writing code without always writing the code themselves. Vibe coding puts large language models at the center of that shift, promising speed and convenience while raising a harder question—what happens when nobody fully understands the system being built?

The idea has moved from an intriguing experiment to a serious part of the coding conversation. AI tools now help generate, review, debug, and complete software, but the risks are gaining shape too. Security researchers have already linked vulnerabilities to AI-generated code, turning technical debt into a problem that AI may expand even as it helps manage it.

Vibe Coding Turns Intent Into Software

Andrej Karpathy coined the term “vibe coding” in February 2025. He described a process where a person gives instructions to a large language model and lets the model generate some or all of the code for a project.

Karpathy wrote on X: “There’s a new kind of coding I call ‘vibe coding,’ where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” The phrase captured a new relationship with software, one built around describing what should happen instead of manually creating every line.

He also wrote, “It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good.” That improvement helps explain why the approach has attracted so much attention. When a model can produce useful code from ordinary instructions, the distance between an idea and a working project becomes much shorter.

Yet the label can hide an important distinction. Most professionals using AI for coding tasks are not abandoning engineering discipline and handing over every decision to a model. They use AI for autocomplete, code review, and debugging, blending generated work with human judgment.

That difference shows up in survey results. A survey of 1,100 professional programmers found that 72 percent used AI coding tools every day, and about 42 percent of their codebase was either AI-generated or AI-assisted. Stack Overflow’s 2025 developer survey found that 47.1 percent of respondents used AI tools daily, while 72 percent said vibe coding was not part of their development workflow. Another 5 percent said vibe coding was “emphatically” not how they do their job.

The Security Cost Is Starting to Surface

The gap between using AI assistance and fully trusting AI-generated code matters because software can carry hidden weaknesses. Researchers from the School of Cybersecurity and Privacy at Georgia Tech scanned 43,000 security advisories covering a three-month period at the start of the year and found 74 vulnerabilities traceable to AI-generated code.

Fourteen of those vulnerabilities were deemed critical. The researchers estimated that the real number of AI-generated vulnerabilities was likely five to 10 times higher, giving the debate a much larger scale than the confirmed findings alone suggest.

These numbers do not erase the value of AI coding tools, but they raise the cost of treating generated code as finished code. A model can produce a solution that looks complete while leaving behind security problems that appear only after deployment. The faster a team generates software, the more important review, testing, and maintenance become.

That pressure reaches senior developers in particular. Most senior developers are now using AI to automate tasks previously delegated to junior teammates, changing how teams divide work and how newer developers gain experience. The result is a productivity boost with a new question attached: if AI handles the early tasks, where do junior developers build the skills needed to judge complex systems?

Conversations around AI coding adventures include Benwiggy, a thread starter, and Lord Crappington, who shared an experience with GitHub Copilot. Those examples sit inside a much broader shift, where coding tools influence not only how software gets written but also how developers decide what deserves their attention.

Technical Debt Gets a New Shape

Technical debt has always followed software teams. Twenty years ago, a company’s entire business could be powered by a handful of tower servers in a basement. Those towers are now considered almost archaeological, even though they were the best solution at the time.

That history offers a useful lens for AI-generated code. Tools that feel powerful today may leave behind systems that future teams must replace, repair, or understand under pressure. The technology changes, but the pattern remains: a solution can make sense at the moment and still become difficult to maintain later.

AI is changing the picture of technical debt, including how it is managed. Generated code can help teams clear backlogs, improve existing software, and handle repetitive tasks. It can also add layers of code that no developer can explain with confidence if the team accepts output without careful review.

Shammy Narayanan, a Forbes Councils Member and Sr VP for Data, AI, and Architecture at Welldoc, sits within this enterprise conversation. The challenge for companies is not simply choosing whether to use AI. It is deciding where AI-generated work belongs, who checks it, and how teams preserve enough understanding to maintain the result.

Vibe coding will keep attracting attention because it makes software feel more accessible and more immediate. The professional workflow, however, is heading toward a more balanced model: AI generates and assists, while developers inspect, test, secure, and own the systems that reach users.

The next stage of software development will not be defined by whether AI writes code. It will be defined by whether teams can move faster without losing control of what that code does.

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