The Future of Software Engineering in the Age of AI – Lessons from the Past

Introduction

The rise of AI-powered tools like ChatGPT and GitHub Copilot has sparked heated discussions about the future of software engineering. Some argue that AI is on the verge of replacing programmers entirely, whilst others believe it will simply change the nature of the job. But if history has taught us anything, it’s that technology doesn’t eliminate engineers—it shifts the skills that matter most.

I was reminded of this recently as I reflected on my own journey in software development. When I started programming around 2010, I worked with assembly for 8-bit microcontrollers. Writing low-level code was a painstaking process—manually moving bits and bytes, optimising for every instruction, and debugging at the hardware level. Then came C, which was a huge improvement, offering more readability and efficiency while still allowing deep control. I remember embedding bits of assembly inside C for performance-critical sections, trying to strike a balance between abstraction and control.

As my career progressed, I moved into web development and higher-level languages like C#, Ruby, and JavaScript. These languages sacrificed low-level control but made development exponentially faster and more accessible. Today, I can’t write assembly to save my life, but I also don’t need to—it’s no longer a core part of my daily work. However, a handful of engineers still specialise in it, particularly in firmware development, and their expertise is so rare that some command rates of $5,000 per day for months-long projects.

This evolution—from low-level programming to high-level abstractions—mirrors what’s happening now with AI. Just as high-level languages replaced the need for many assembly programmers, AI is automating repetitive coding tasks, shifting what it means to be a software engineer. The question isn’t whether AI will replace developers, but rather how the role of software engineers will evolve.

1. The Cycles of Abstraction in Software Development

Software development has always been shaped by cycles of abstraction. Each new wave of technology builds upon the previous one, making programming more accessible whilst shifting the skills that engineers need to stay relevant. AI is simply the latest step in this ongoing transformation.

In the early days of computing, programming meant working directly with machine code or assembly language. Every operation had to be meticulously planned and optimised, making software development a slow and highly specialised skill. Then came languages like C, which abstracted away some of that complexity while still offering control over memory and performance. Developers could write code more efficiently without manually managing every CPU instruction.

As the industry matured, higher-level languages like C#, Ruby, and JavaScript emerged, further simplifying development. These languages sacrificed low-level control but allowed engineers to build complex applications with far less effort. Frameworks, libraries, and cloud services took abstraction even further, enabling developers to focus more on solving business problems rather than managing system-level details.

The trade-off has always been the same: increased abstraction makes software development faster and more accessible but shifts the expertise required. A programmer who once had to optimise assembly instructions by hand now relies on a compiler to do the heavy lifting. A web developer no longer needs to worry about memory allocation in JavaScript. And just as few engineers today write assembly, future software engineers may rely on AI to handle much of the routine coding work.

This is where AI-driven development enters the picture. AI tools are beginning to abstract coding itself, generating boilerplate, refactoring code, and even suggesting architectural improvements. Just as high-level languages freed developers from the intricacies of memory management, AI is freeing engineers from some of the repetitive tasks of coding. The key question isn’t whether AI will replace programmers, but how it will redefine the skills they need—just as previous shifts in abstraction have done.

2. AI as the New “Higher-Level Language”

If high-level programming languages abstracted away low-level complexity, AI is now abstracting away much of the repetitive coding process itself. Tools like GitHub Copilot, ChatGPT, and Amazon Q are rapidly becoming the next layer of abstraction in software development, allowing engineers to focus more on problem-solving and less on writing boilerplate code.

Much like compilers and frameworks revolutionised programming by automating tedious tasks, AI-assisted coding tools are accelerating development. Need a function to parse JSON? Copilot can generate it in seconds. Struggling with an unfamiliar API? AI can provide context-aware suggestions. These tools drastically reduce the friction of writing code, making engineers more productive by eliminating repetitive work and speeding up the transition from idea to implementation.

However, AI is not replacing software engineers—it’s changing what they do. While AI excels at generating code snippets, refactoring functions, and even suggesting design patterns, it still struggles with deeper aspects of software engineering. Complex system design, performance optimisation, and debugging intricate logic remain human-driven tasks. AI can suggest a solution, but understanding trade-offs, debugging an obscure race condition, or architecting a scalable system requires experience and intuition that AI lacks.

I’ve seen this first-hand. A non-engineering colleague recently used ChatGPT to generate a function for a specific task. It seemed to work at first glance, but when I tested it in a real-world scenario, I had to tweak it multiple times before it did what we actually needed. Eventually, it became clear that a better approach was to rewrite it from scratch using solid system design principles rather than patching up an AI-generated solution. AI can generate code, but making it truly robust, maintainable, and scalable still requires human oversight.

Much like how developers today rarely write raw SQL queries but still need to understand databases, the future engineer might write less manual code but will need to oversee AI-generated solutions, fine-tune architectures, and ensure maintainability. AI isn’t eliminating the need for engineers; it’s redefining the skills that will be most valuable—shifting the focus from syntax to system thinking.

3. The “Assembly Programmers” of the Future

Just as AI is redefining what it means to be a software engineer, it’s also creating a divide in the skills that will be most valuable in the future. Some developers will embrace AI-assisted development, leveraging it to write code faster, iterate quickly, and build applications with unprecedented speed. Others, however, will take a different path—becoming the “assembly programmers” of the AI era, specialising in the deeper mechanics that power these tools.

In the same way that assembly programmers didn’t disappear when higher-level languages became dominant, a new breed of engineers will emerge who deeply understand AI models, fine-tune them for specific applications, and optimise their performance. These engineers won’t just use AI—they’ll shape and refine it, working on things like model efficiency, prompt engineering, and AI-driven architecture.

And just like the firmware engineers who stuck with assembly and now command top-dollar for their expertise, these AI specialists will be in rare but high demand. Companies that rely heavily on AI won’t just need developers who can use tools like ChatGPT or Copilot—they’ll need experts who understand their inner workings, know how to optimise model outputs, and can troubleshoot where AI falls short.

We’re seeing the early stages of this shift already. While AI-generated code can speed up development, it still requires human oversight, careful design, and fine-tuning to fit real-world needs. The engineers who go beyond just using AI and instead learn to control, refine, and push its limits will be the ones commanding premium rates—just as firmware engineers, database optimisers, and performance specialists have done in the past.

As software engineering evolves, AI won’t erase the need for deep expertise—it will simply change where that expertise is applied.

4. The Evolution of Engineering Careers

The rapid integration of AI into the development process is not just transforming how we write code—it’s reshaping the very nature of software engineering careers. In this evolving landscape, engineers must adapt by embracing AI tools while continuing to sharpen their unique problem-solving abilities.

Those who eagerly integrate AI into their workflows will find themselves at a significant advantage. Rather than resisting the change, the best engineers are already learning how to leverage AI to accelerate development, reduce repetitive tasks, and enhance creativity in designing solutions. This shift demands a blend of traditional software skills with a new fluency in AI and data-driven decision-making.

Moreover, as AI becomes an indispensable part of the engineering toolkit, entirely new career paths are emerging. Roles such as AI supervisors, prompt engineers, and hybrid software/AI architects are starting to take shape. These positions focus on overseeing AI-driven processes, fine-tuning the interaction between human creativity and machine efficiency, and ensuring that the solutions built are both robust and scalable.

Ultimately, the evolution of engineering careers will favour those who see AI as an enabler rather than a threat. By combining tried-and-true problem-solving techniques with innovative AI strategies, engineers can unlock new levels of productivity and creativity, ensuring they remain indispensable in an increasingly automated world.

Conclusion

AI won’t replace software engineers—it will transform the role entirely. Just as historical shifts from assembly to high-level languages redefined our craft, AI is now reshaping how we build and maintain software. The key lesson from the past is that specialisation and deep expertise in niche areas will always be valuable. While AI handles repetitive tasks, human ingenuity in system design, troubleshooting, and architectural thinking could be highly sort after. The developers who resist change may find themselves struggling to keep up, while those who embrace AI as a tool will unlock new levels of productivity. The highest-paid engineers in the future won’t necessarily be the ones who write the most code—they’ll be the ones who understand how to leverage AI effectively.

The future belongs to those who adapt, harnessing AI as a tool to accelerate innovation rather than a threat to traditional skills. By embracing these changes, engineers can unlock new levels of productivity and creativity, ensuring that they remain at the forefront of technological progress. For now, I think the best engineers won’t be replaced by AI—they’ll be the ones leading the change. Stay curious, stay adaptable, and look to the future with confidence.