University of Kwazulu-Natal

Technologists Are Embracing Vibe Coding As They Deploy More Ai

Bonisiwe Shabane
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technologists are embracing vibe coding as they deploy more ai

Earlier this spring, Alex Balazs hosted an internal hackathon at business software provider Intuit, where the chief technology officer challenged his global team of engineers to embrace artificial intelligence in their work as much... To give them the tools to do so, one week before the biannual meeting, Balazs approved the usage of “every AI coding tool that we’re aware of,” including Qodo, Windsurf, and Cursor. He quickly saw that thousands of Intuit’s engineers were using these tools, up from hundreds previously. “Our metrics are showing us that engineers love it,” says Balazs. Intuit is tracking how frequently the AI coding tools are being used, the steadiness of that usage over time, and productivity. Early indications are that efficiency gains can be as high as 40%.

Along the path to going all-in on AI coding assistant tools, Balazs finds himself enticed by a new concept called “vibe coding.” It is a term that was coined earlier this year by OpenAI... Balazs recently experimented with vibe coding when using Windsurf to create a tool that would allow him to import data from a competitor product directly into Intuit’s QuickBooks software. “I found myself making significantly more progress with my rusty coding skills than probably I would have otherwise,” says Balazs. Earlier this spring, Alex Balazs hosted an internal hackathon at business software provider Intuit, where the chief technology officer challenged his global team of engineers to embrace artificial intelligence in their work as much... To give them the tools to do so, one week before the biannual meeting, Balazs approved the usage of “every AI coding tool that we’re aware of,” including Qodo, Windsurf, and Cursor. He quickly saw that thousands of Intuit’s engineers were using these tools, up from hundreds previously.

“Our metrics are showing us that engineers love it,” says Balazs. Intuit is tracking how frequently the AI coding tools are being used, the steadiness of that usage over time, and productivity. Early indications are that efficiency gains can be as high as 40%. Along the path to going all-in on AI coding assistant tools, Balazs finds himself enticed by a new concept called “vibe coding.” It is a term that was coined earlier this year by OpenAI... Balazs recently experimented with vibe coding when using Windsurf to create a tool that would allow him to import data from a competitor product directly into Intuit’s QuickBooks software. “I found myself making significantly more progress with my rusty coding skills than probably I would have otherwise,” says Balazs.

Programming has always been a blend of logic, creativity, and problem-solving. But as technology evolves, so does the way we approach coding. Enter vibe coding—a new paradigm in software development where developers collaborate with AI to write, debug, and optimize code. This emerging trend, popularized by Andrej Karpathy, Tesla’s former AI director, is reshaping how we think about programming. Let’s dive into what vibe coding is, how it works, and why it’s poised to revolutionize the way we build software. Vibe coding is a technique where developers use AI-powered tools to assist in the coding process.

Instead of writing every line of code manually, programmers rely on AI to generate suggestions, complete functions, and even debug errors. The term “vibe” reflects the intuitive and collaborative nature of this approach—it’s less about rigid syntax and more about flowing with the AI to achieve the desired outcome. Imagine having a pair programmer who never gets tired, knows every programming language, and can instantly recall best practices from millions of open-source projects. That’s the essence of vibe coding. Tools like GitHub Copilot, ChatGPT, and Amazon CodeWhisperer are at the forefront of this movement, enabling developers to work smarter and faster. At its core, vibe coding relies on large language models (LLMs) trained on vast amounts of code and natural language data.

These models understand context, syntax, and even the intent behind your code. Here’s how the process typically unfolds: The AI-powered development movement, commonly known as “vibe coding”, has reached a critical inflection point. What began as experimental tooling for individual developers has now become a strategic imperative for major corporations. This edition examines the concrete evidence of this transformation through real-world implementations, quantified business outcomes, and emerging industry standards. The adoption of AI-powered development tools by enterprises has accelerated dramatically.

Visa has posted three generative AI engineer positions in Austin, explicitly requiring familiarity with Vibe coding tools as an essential qualification. This signals a fundamental shift in corporate hiring practices, where AI-assisted development skills are no longer optional but mandatory. Reddit is actively recruiting engineers to integrate AI coding tools and engage with the broader vibe-coding community to drive adoption across their platform. Meanwhile, DoorDash and cybersecurity leader Snyk have incorporated similar requirements into their job postings, with Snyk specifically seeking candidates with deep expertise in AI development tools. The most aggressive stance comes from Y Combinator startup Domu Technology, which has declared vibe coding experience as "non-negotiable" and requires that at least half of hired candidates' existing code portfolio be AI-generated. This represents a watershed moment in technology hiring practices.

The financial implications of this shift are substantial. Intuit's CTO, Alex Balazs, reports that engineers using AI coding tools demonstrate up to 40% faster coding performance. This productivity gain translates directly to reduced development costs and accelerated time-to-market. Enterprise application development is undergoing a profound transformation through the emergence of "vibe coding" — an intent-first, syntax-second approach that's redefining how organizations build software. Vibe coding democratizes development by prioritizing business outcomes over technical implementation details while dramatically accelerating delivery cycles. Organizations implementing vibe coding approaches are expected to experience faster development cycles for certain applications, marking a paradigm shift in how enterprises approach their digital initiatives.

The traditional enterprise development model, characterized by specialized teams, rigid processes and high technical barriers, is rapidly evolving into something more fluid and accessible. Vibe coding represents the convergence of Generative AI (GenAI) capabilities with human creativity, fundamentally changing how applications are conceptualized and built. With more and more organizations investing in AI-augmented development approaches, vibe coding is emerging as the predominant methodology. This shift isn't merely technological but a philosophical reorientation toward business-centric development. The true innovation of vibe coding isn't the AI component — it's the reframing of development as a business conversation rather than a technical exercise. This evolution is supported by enterprise-grade tools like GitHub Copilot Enterprise, Amazon Q Developer and specialized enterprise versions of coding assistants that integrate with existing development workflows while adhering to strict security requirements.

Unlike consumer-grade AI tools, these enterprise solutions incorporate governance guardrails and compliance features essential for regulated industries. Unlocking business value through accelerated development A new term is making waves in the software engineering world—“vibe coding.” It encapsulates a fast-rising trend where artificial intelligence, especially large language models (LLMs) like ChatGPT, takes on the heavy lifting of writing... Rather than requiring developers to painstakingly craft each function or debug every error line-by-line, vibe coding allows developers—and even non-coders—to describe what they want in natural language and get working code in return. The implications of this trend are enormous. From drastically increasing developer productivity to democratizing software creation for non-technical founders, vibe coding may be a glimpse into the future of software engineering.

But how far has the technology really come, and can it handle the complexities of enterprise-scale systems? Or is it still only suited for prototyping and simple projects? To understand what’s happening, let’s start by looking at one of the most powerful accelerators in the tech world: Y Combinator. Y Combinator (YC), one of the most prestigious startup incubators in the world, has backed giants like Airbnb, Stripe, DoorDash, and Dropbox. With over 5,000 companies funded and a combined valuation of over $600 billion, YC is an excellent bellwether for emerging tech trends. In its latest batch of startups, something remarkable happened: roughly 25% of the cohort reported using AI to write at least 95% of their code.

That’s not a minor assist—it’s nearly full codebase generation by artificial intelligence. In the rapidly evolving landscape of software development, one month can be enough to create a trend that makes big waves. In fact, only two months ago, Andrej Karpathy, a former head of AI at Tesla and an ex-researcher at OpenAI, defined “vibe coding” in a social media post. This approach to software development uses large language models (LLMs) to prioritize the developer’s vision and user experience, moving away from conventional coding practices. The code no longer matters. Vibe coding is less about writing code in the conventional sense and more about making the right requests to generative AI (aka a Forrester coding TuringBot) to produce the desired outcome based on the...

As cited in a YouTube video from Y Combinator (YC) titled “Vibe coding is the future,” a quarter of startups in YC’s current cohort have codebases that are almost entirely AI-generated (85% or more). The essence of vibe coding lies in its departure from meticulously reviewing TuringBot LLMs’ suggested code line by line. Instead, developers quickly accept the AI-generated code. And if something doesn’t work or fails to compile, they simply ask the LLM to regenerate it or fix the errors by prompting them back into the system. This method has gained traction for several reasons, notably the significant improvements in integrated development environments and agent platforms such as Cursor and Windsurf; voice-to-text tools like Superwhisper; and LLMs such as Claude 3.7... These advancements have made AI-generated code more reliable, efficient, and, importantly, more intuitive to use, keeping developers’ hands off the keyboard and eyes on the bigger picture.

The viral reaction to Karpathy’s concept of vibe coding, with close to 4 million instant views and countless developers identifying with the practice, underscores a broader shift in the software development paradigm. This shift aligns with Forrester’s insights on TuringBots, which predicted a surge in productivity through AI by 2028. The reality is outpacing expectations, however, with significant impacts occurring much sooner. Vibe coding won’t fade away. The advent of vibe coding and the proliferation of TuringBots are creating two distinct types of developers. On one side, developers will transform into product engineers who, while perhaps adept at traditional coding, excel in utilizing generative AI (genAI) tools to produce “apparently working” software based on domain expertise and some...

These developers focus on the outcome, continuously prompting AI to generate code and assessing its functionality with no understanding of the underlying technology and code. The philosophy is to just keep accepting code until it does what you want. Not only that, but they don’t spend hours fixing a bug or finding the problem, since they can ask a well-trained coder TuringBot to do that for them or can just ask it to... This approach may challenge our classical view of computer science skills, suggesting a shift toward developers who are more orchestrators of software development process steps than coding craftsmen. The concern of how we’ll develop good developers over the years is gone, because you’ll trust AI to do a good job. And if you want good developers, genAI will help those on the development trajectory learn faster.

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