University of Kwazulu-Natal

The Future Of Code How Ai Is Transforming Software Development Forbes

Bonisiwe Shabane
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the future of code how ai is transforming software development forbes

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. 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. 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. 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.

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... Peter Guagenti is president at Tabnine.

Peter is an accomplished entrepreneur, and has been working in AI business tools for 10+ years. Software development just isn’t what it used to be. The role of the developer is more challenging today than it was before the pandemic, and it's exponentially more difficult than at the start of the mobile era 10-plus years ago. Why? The world’s seemingly insatiable appetite for software. To put it into numbers, Google Play adds about 1,270 new applications per day, and the average enterprise has over 1,000 applications in use at any given time.

The burden of creating, maintaining and modernizing all of this code is only increasing. The combination of technological advancements and fierce competition between businesses has created the need to build more applications faster. Devs are tasked with creating and maintaining these apps—including contending with their increasing complexity—while simultaneously dealing with a growing pool of technical debt that has accumulated over time. The final piece of the puzzle contributing to this challenging new environment is a talent shortage. According to one study, "the shortage of developers in the US will exceed 1.2 million." This talent shortage is so severe that "the US economy [is] at risk of an unrealized [GDP] output of... 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. The current state of AI presents ethical challenges that need to be tackled.

For example, there are issues related to data privacy and risk-based algorithms. As AI emerges into decision making frameworks, it’s important to guarantee fairness, transparency and accountability to uphold public confidence and encourage innovation through responsible AI. In the past decade, software development has evolved rapidly, but nothing compares to the seismic shift happening right now—thanks to Artificial Intelligence (AI). From writing code to debugging and deploying applications, AI is streamlining the entire software development lifecycle. Let’s explore how AI is changing the way we code, and what the future holds for developers. Tools like GitHub Copilot, Amazon CodeWhisperer, and ChatGPT are already helping developers write code faster.

These AI assistants suggest entire functions, write documentation, and even detect bugs before you run the code. Platforms like Snyk and DeepCode use machine learning to scan your code and alert you of vulnerabilities in real-time. Cloud platforms like AWS, Azure, and Google Cloud are now integrating AI-driven CI/CD tools for intelligent deployment.

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