University of Kwazulu-Natal

Code Disrupted The Ai Transformation Of Software Development Forbes

Bonisiwe Shabane
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code disrupted the ai transformation of software development forbes

Asaf Wiener, CEO and Co-Founder, Mate Security. Software development has fundamentally changed in the past 18 months. AI-assisted coding and engineering went from novel and exploratory to widely adopted across enterprise teams. We're seeing it fundamentally reset core engineering domains, from code review and testing to deployment and documentation, by eliminating repetitive manual tasks and toil that traditionally consumed developer time. Traditional software engineering follows a predictable sequence: plan, code, review, test, deploy, monitor. Each step requires human coordination, handoffs between team members and significant time investment in process management rather than actual problem-solving.

AI-native engineering breaks this linear model. Instead of sequential handoffs, we now have continuous human-AI collaboration loops that reduce coordination overhead while improving code quality and delivery speed. The traditional engineering workflow often revolved around coordination overhead: weekly planning meetings to align priorities, daily standups to surface blockers, code review sessions that could stretch for days and architecture discussions mixed with status... This framework worked when humans handled every aspect of the development pipeline. Rupesh Dabbir is a Software Engineering Manager at Google with over a decade of experience building highly scalable systems in the cloud. The emergence of artificial intelligence (AI) is transforming the software engineering domain in ways we haven't seen in the past few years.

What was once entirely dependent on human creativity and problem-solving is now being enhanced—and, in some cases, even automated by a plethora of AI tools growing every hour. Although this shift brings challenges, it also opens up opportunities for engineers to rethink their roles and adapt to the changing technology landscape. As AI becomes deeply integrated with how software engineers write code, it's essential to understand how developers can take advantage of AI and thrive in the new technology era. Software engineering roles are increasingly moving to AI-assisted programming roles, using tools like GitHub Copilot and Cursor that not only make coding more efficient but also save time for developers to focus on core... This paradigm shift can enhance collaboration and increase efficiency. However, this also presents concerns about job displacement and the need for reskilling, making it crucial for software engineers to invest in education that helps them upskill in AI.

Will AI replace human jobs? This is difficult to say, but the integration of AI into software engineering will likely create new opportunities that require a partnership between machines and humans, who can harness AI's ability to solve problems... Software engineering involves much more than just inserting code snippets. It demands creativity and collaboration among multiple stakeholders (e.g., the user experience team, product team and technical program managers) to address complex problems and deliver innovations that meet customer needs. Ultimately, the product being built should apply to real customer use cases. 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. Jyoti Shah is a Director of Applications Development, a GenAI tech leader, mentor, innovation advocate and Women In Tech advisor at ADP. When I first started leading enterprise software projects, the first step was always the same: a whiteboard full of sticky notes, half-finished user stories and weeks of questions before anyone wrote a single line...

That process is very different now that AI is at the table with us. I don't mean a prototype for a lab in the future. I mean real, production-ready systems that help my teams turn business goals into working software faster, smarter and with insights we didn't expect. Over the last few years, I've used AI to change the path from user story to deployment. It has changed how we deliver value, speed up release cycles and get more out of our investments. It starts right at the whiteboard.

During discovery, I feed raw user stories into a GenAI model trained on our domain language. The AI instantly clarifies vague ideas, fills in missing acceptance criteria and maps dependencies. A note like, "As a manager, I want better dashboards" becomes, "As a regional sales manager, I want AI-generated dashboards showing weekly revenue, churn and forecast accuracy." That kind of precision saves entire sprints... As more organizations awaken to the power generative AI holds for building software, this guide can light the way. Few practices stand to benefit from today’s generative AI boom more than software development. Prompting GenAI systems to create code reduces repetitive processes and accelerates production cycles, freeing developers to focus on new, higher value projects.

The upside is likely a big reason why 78% of developers surveyed by Stack Overflow said they were using AI-assisted programming tools to save time on routine tasks. Excitement aside, developers face learning curves while using GenAI to create code. Fortunately, Dell and NVIDIA have created this eBook, which follows a day in the life of a software developer whose team is tasked with conceptualizing a proof-of-concept (PoC). When Sam, an early career programmer, arrives at the office Monday morning she opens Jira and learns that her IT leadership team has requested a PoC sketch for a mobile shopping application. If software development were music, the past decade has been a jam session: developers riffing on code, improvising solutions … and occasionally hitting a sour note. But with AI increasingly stepping in as a conductor, 2026 promises a full orchestral performance.

Generative AI isn’t about just adding a few instruments – it’s rewriting the score and changing how the entire ensemble plays together. In Forrester’s Developer Survey, 2025, using AI and genAI in the software development lifecycle (SDLC) bubbled up as a top priority (alongside using more cloud-native technologies and improving software security). At the same time, adoption rates varied across the SDLC. Coding and testing were the top use cases for leveraging AI (48% and 47%, respectively). Lagging behind were priorities such as finding development insights, at 33% of respondents. The question is: How do you maximize the music?

Are you ready to swap your solo for a symphony? Here’s what will happen in 2026: AI isn’t just changing the tempo; it’s redefining the entire composition of software development. Leaders who embrace this shift will unlock faster delivery, better quality, and more creative innovation. Those who cling to old rhythms risk falling out of tune. Ready to hear the full symphony?

Get complimentary resources on the Predictions 2026 hub and download the predictions guide for security and technology leaders here. Code generation has emerged as a top use case for generative AI. Is your organization ready for it? Generative AI’s ability to create text and image-based collateral for marketing, product design and other business functions is well known. Yet that’s not the use case drawing the most buzz of late. That distinction goes to software development, where AI “copilots” have captivated the coding world.

Organizations are using GenAI copilots that generate or edit code to streamline routine software tasks such as testing, debugging and language translation. GenAI coding has caught on at NVIDIA, the chipmaker whose GPUs have an outsized influence on the market. “We use generative AI for coding quite extensively here at NVIDIA now,” said NVIDIA CEO Jensen Huang during the company’s second quarter earnings call. As AI copilot use grows, it requires organizations to reconsider how to build software holistically, including educational initiatives aimed at reskilling and upskilling developers. In the meantime, individuals and businesses alike are raving about the productivity boost these GenAI tools provide. In the rapidly evolving landscape of software development, one month can be enough to create a trend that makes big waves.

In fact, only a month 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.

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