The first time I wrote a program, it was on a DEC PDP-11 in the early 1980s, a computer the size of a small wardrobe. Back then, writing software was an intricate dance of logic and intuition, an exercise in mental gymnastics that required a deep understanding of both the machine and the problem it was solving. Forty years later, the landscape of software development has been utterly transformed. And now, with the rise of artificial intelligence capable of generating code, the question looms large: if AI can do the coding, what’s left for humans?

This helps our engineers do more and move faster.

A recent article from MIT described what people building generative AI thought it was good for right now. And what they were fired up about was—coding. In the article, Jared Kaplan, chief scientist at Anthropic, says “That’s something that’s been very exciting for developers. It’s really understanding what’s wrong with code, debugging it.” The piece also reports the comments by Alphabet CEO Sundar Pichai who claimed in October last year, “Today, more than a quarter of all new code at Google is generated by AI, then reviewed and accepted by engineers. This helps our engineers do more and move faster.”

The Evolution of Software Development

In the early days, coding was a manual, laborious process. Debugging involved poring over reams of printed paper or staring at a monochrome monitor for hours. Every line of code was written, reviewed, and refined by hand. When graphical interfaces came along, they weren’t just tools—they were revelations, dramatically simplifying and accelerating development. This progression has continued relentlessly: high-level languages, integrated development environments (IDEs), and now AI-assisted tools like GitHub Copilot and ChatGPT are reducing the need for human coders to start from scratch.

These advancements are not new threats but natural evolutions in a field that thrives on automation. In the same way that compilers liberated us from handwriting machine code, AI liberates us from the mundane, repetitive aspects of coding. However, suggesting that this marks the end of human input misunderstands both the nature of coding and the essence of innovation.

Coding is a Means, Not the End

The act of coding is not and has never been, an end in itself. Coding is a tool, a means to solve problems and create value. Whether it’s where I started in designing systems to analyse vibration and sound in complex machinery or building algorithms to process terabytes of data, the real value lies in understanding the problem and devising a solution—not in the mechanics of writing the code.

AI can write code, but it cannot (yet!) understand context, nuance, or the broader implications of a problem. For example, in my years managing software teams at Prosig and later as a General Manager, some of the most critical decisions we made weren’t about the lines of code but about the strategic direction of the software: Who will use it? How will it integrate into their workflows? What unintended consequences might arise?

The Human Touch in Innovation

The key value of human developers isn’t their ability to write flawless syntax—it’s their capacity to empathize, imagine, and innovate. It’s the ability to sit with a client, listen to their frustrations, and envision a solution that transforms their experience. It’s the creativity to see connections between seemingly unrelated ideas and the courage to pursue them.

The ability to ask the right questions, challenge assumptions, and rethink the problem is where humans will always excel.

I’ve seen firsthand how innovation emerges at the intersection of disciplines and perspectives. At Prosig, where we developed solutions for sound and vibration analysis, our breakthroughs often came not from coding prowess but from understanding our clients’ challenges deeply. The ability to ask the right questions, challenge assumptions, and rethink the problem is where humans will always excel.

AI as a Partner, Not a Replacement

AI is not a competitor; it’s a collaborator. Just as the calculator didn’t render mathematicians obsolete, AI won’t replace software developers. Instead, it will allow us to focus on higher-order problems—on architecture instead of syntax, on strategy instead of implementation.

For businesses, this represents an incredible opportunity. Developers can now dedicate more time to understanding users, experimenting with new ideas, and refining the overall user experience. Leaders can leverage AI to streamline operations, reduce costs, and foster innovation in ways that were previously unimaginable.

The Risks of Over-Reliance on AI

That said, we must approach this new era with caution. Relying too heavily on AI without understanding the underlying code risks creating systems that are opaque, brittle, and difficult to maintain. There’s a real danger of losing the craftsmanship of software engineering—the meticulous attention to detail and the deep understanding of how systems interact.

As stewards of this technology, we must ensure it is used responsibly and ethically.

Furthermore, AI is only as good as its training data. It doesn’t understand ethics, context, or consequences. We’ve already seen examples of AI systems generating biased or even harmful outputs. Without human oversight, these risks will only multiply. As stewards of this technology, we must ensure it is used responsibly and ethically.

The Future of Developers

So, what’s the role of humans in a world where AI can code? It’s the same as it’s always been: to lead, to imagine, and to solve problems. AI can assist, but it can’t take responsibility. It can analyze data, but it can’t set goals. It can generate code, but it can’t understand what truly matters.

The developers of the future will be more like architects than builders. They’ll design systems, define requirements, and ensure quality. They’ll focus on the “why” and “what” rather than the “how.” And they’ll be more interdisciplinary than ever, combining technical skills with creativity, empathy, and strategic thinking.

Final Thoughts

All current AI systems need a “human in the loop”. The rise of AI in coding is not the end of human developers but the beginning of a new chapter. It’s an opportunity to refocus our energies on what truly matters: solving problems, creating value, and making the world a better place. As someone who has spent decades navigating the waves of technological change, I see this not as a threat but as a challenge—a challenge to adapt, to learn, and to lead.

As I often tell my teams, the question isn’t whether the tools will change. They always do. The question is how we will change with them. As Senaca wrote, “Luck is what happens when preparation meets opportunity.”

Chris Mason MBA

Advisor – Leadership, Innovation & Strategy

Chris Mason is a seasoned leader with over 40 years in the tech industry, recognized for his expertise in strategy, innovation, and team empowerment. As the former General Manager of an engineering software house, he guided the company and its teams through the peaks and troughs. And helped create some of the best vibration and acoustics analysis solutions in the world through strategic innovation and an inclusive approach to leadership.

Chris holds an Executive MBA from the University of Winchester, where his research on “Barriers to Open Innovation for Technology SMEs” reflected his passion for fostering collaboration and driving change. Beyond corporate roles, he is a trusted strategic advisor and non-executive director, helping startups and SMEs unlock potential through leadership development, digital/AI adoption and business strategy. His goal is to transform ideas into impactful realities, empowering teams and businesses to thrive in competitive landscapes.