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The Evolution of Software Development: Navigating the AI Powered Landscape
The folks at DevOps BCN invited me to have a talk (hosted by InnoIT) about the future of software engineering in our rapidly changing landscape. I've written a some lines about it... here they are.
We're witnessing a fundamental shift in how software gets built. The tools, processes, and skills that defined the industry for decades are being rapidly transformed by AI-powered development. Having worked with teams across different tech eras, I've been reflecting on what this means for our craft and how we should adapt.
The Changing Face of Software Development
Remember when Stack Overflow was the go-to solution for every coding challenge? When we'd spend hours scanning documentation and experimenting with different approaches? Today's workflow looks radically different:
Developers now start with a conversation rather than a search query. Instead of manually assembling solutions from documentation fragments, we prompt AI systems to generate initial code. Our role is increasingly focused on defining problems clearly, evaluating AI-generated solutions, and refining them into production-ready code.
This shift has profound implications. The mechanical aspects of coding are becoming commoditized, while the truly human elements – problem definition, systems thinking, and quality assessment – are becoming more valuable than ever.
The Looming Technical Debt Crisis
Every technological revolution creates its own form of technical debt. The rapid proliferation of AI-generated code presents a unique challenge: code that works but lacks cohesion, consistency, and context awareness.
This isn't merely academic. We're seeing a massive growth in generative AI projects and public code contributions. Each line represents a potential future maintenance burden if we don't develop proper conventions and standards.
For those who've experienced the legacy maintenance challenges of earlier eras (think COBOL in banking systems), the parallels are striking. Today's auto-generated codebases are becoming tomorrow's legacy burden – but at an unprecedented scale and pace.
Why Context Matters More Than Ever
In a world where code generation is increasingly automated, context becomes the crucial differentiator. AI models can produce syntactically correct code, but they struggle with:
- Understanding the broader ecosystem in which code operates
- Maintaining consistency with established patterns
- Optimizing for long-term maintainability rather than short-term functionality
This is where human expertise remains irreplaceable. By providing rich context and establishing stronger conventions, we can guide AI tools toward producing more sustainable code.
The industry has been through similar evolutions before. Linux distributions, packaging systems, containerization – all represented efforts to standardize and provide context. Today's challenge is to develop similar conventions for the AI generation era.
Finding the Balance: Standardization vs. Innovation
The tension between standardization and innovation has always existed in software development. Too much standardization leads to stagnation; too little results in chaos.
In the AI era, this balance is even more critical. We need enough standardization to make AI-generated code maintainable and secure, without stifling the creative problem-solving that drives the field forward.
The sweet spot is what I call "sustainable progress" – an approach that establishes clear patterns and conventions while leaving room for innovative solutions when appropriate. This balance allows us to leverage AI's capabilities without becoming constrained by its limitations.
The Evolution of API Design
The history of API design reflects our ongoing search for better ways to connect systems. From SOAP to REST to GraphQL, each transition attempted to solve the problems of its predecessor.
Today, we're seeing the emergence of MCP and other similar initiatives such as Google's A2A – approaches designed specifically for the era of AI-to-AI and AI-to-human interactions. These protocols emphasize:
- Rich context sharing beyond simple data transfer
- Function calling capabilities
- Standardized formats that work across different systems
- Interfaces that are natural for both humans and machines
- Security as a foundational principle, not an afterthought
This evolution isn't just technical - it reflects a deeper shift in how we think about system integration in an AI-powered world.
Rethinking Technical Interviews
The traditional technical interview – focused on algorithm memorization and syntax recall – made sense in an era where those skills differentiated great engineers from average ones. In today's landscape, they measure increasingly irrelevant capabilities.
When anyone can generate code on demand, what are we really testing? The more valuable skills involve:
- Collaborative problem definition and solution evaluation
- System design with real-world constraints
- The ability to assess existing codebases and identify potential issues
- Effective tool use when faced with unfamiliar problems
Progressive companies are already adapting their hiring processes to focus on these higher-level skills, creating opportunities for engineers who excel at systems thinking rather than just coding mechanics.
The Future is Bright (and Full of Work)
For newcomers to the field, today's rapid changes may seem intimidating. But they actually represent tremendous opportunity for those who understand where the industry is heading.
Software engineering is becoming more about actual engineering – applying principles to solve problems – and less about the mechanical aspects of code production. People who know how to "make things" and combine technologies effectively will thrive.
Similarly, those with strong product sense will find increasing demand for their skills. The ability to define what should be built becomes even more valuable when the "how" is partially automated.
And for experienced engineers? Don't worry – our jobs are safe. The massive increase in software production means equally massive growth in the need for maintenance, optimization, and integration. There's more than enough technical debt being generated to keep us all employed for decades to come.
Looking Ahead: Batch is Back
One fascinating trend is the resurgence of batch computing paradigms. After years of focusing on real-time systems, the computational demands of AI are driving a return to batch processing.
Unfortunately, many cloud providers haven't fully adapted to this shift, still focusing on selling high-margin products designed for previous computing models. The most interesting innovation is coming from newer players who understand the changing landscape.
Conclusion
The AI revolution isn't eliminating the need for software engineers – it's changing what we do and how we do it. By focusing on the uniquely human aspects of software development and developing better standards and conventions, we can harness these powerful new tools while avoiding their pitfalls.
The engineers who thrive in this new landscape won't be those who cling to old ways of working, but those who recognize which parts of the craft remain essential and which are being transformed. Both newcomers and veterans have a place in this future – as long as we're willing to adapt.
I would like to thank David Acacio, David Castañeda and the other folks at DevOps BCN for having me.