The AI Opportunity for Software Engineers
Some reasons to be optimistic about AI
The rise of AI has sparked intense debate about the future of software engineering. Some predict the decline of the profession, arguing that AI will automate developers out of existence. I don’t buy it. AI will write a lot of code for us, but that doesn’t eliminate engineering—it makes it more important.
History proves it.
Every major advance that made coding easier—high-level languages, frameworks, cloud computing all led to more software, not less. AI will follow the same pattern: by lowering the barrier to entry, it will flood the world with more software, more systems, and more complexity. And that means we need more engineers, not fewer.
How Engineers Should Prepare for This Future?
So, if that’s true, what should you do to prepare for the future? Spoiler. I’ll bet the things you’re doing today already help.
Architecture
As anyone who knows software knows writing code is easy, building systems is hard. Imagine an AI generating code across a large enterprise system. Small, unrelated changes might cause cascading failures because the AI doesn’t truly understand the architecture, it’s just predicting tokens. Without strong architectural patterns, we’ll see even more systems that break in unpredictable ways.
As engineers, we’ll have to get better at building architectures that localize the impact of change. And the good news is, that’s mostly what we’ve been doing anyway!
Correctness
AI makes it faster to write code, but correctness isn’t just about writing quickly. It’s about writing the right thing. And correctness is often ambiguous! If stakeholders knew exactly what they needed, we’d have been automated away decades ago (4GLs didn’t do it, low-code didn’t do it, and AI won’t either). Engineers bring deep domain understanding, the ability to ask the right questions, and the skill to verify assumptions.
As an engineer, you have a whole bunch of tools to understand correctness, ranging from the human side before you start writing a feature (acceptance criteria, behaviour-driven development), through to automated tests that validate your understanding of the code. More good news! We do most of these already.
Delivery
AI-generated code doesn’t reduce the need for fast, reliable shipping—it amplifies it. Faster code generation increases the importance of Continuous Integration, fast deployment pipelines, and robust observability. If AI helps you write a feature in 10 seconds, but it takes weeks to get it into production, you’ve gained nothing.
AI will increase demand here and challenge us to get faster and faster cycle times. You guessed it, that’s good news too!
Product
AI can generate code, but it can’t distinguish between a useful feature and a gimmick. It takes engineers with technical empathy to guide decisions, like preventing an AI from over-engineering a simple tool or creating something nobody wants. The ability to bridge the gap between stakeholders and technology will be more critical than ever.
As technology becomes more accessible, the ability to understand and guide stakeholder decisions increases in importance. This requires developing "technical empathy" – the ability to translate between technical and business domains while maintaining the trust of both sides. Engineers must learn to listen deeply to stakeholder needs, reading between the lines to understand unstated requirements and constraints.
And of course, being able to push back against the “isn’t it just …?” style narratives become even more important when we have to talk in terms of the long-term changes to the system as a whole rather than the short-term code writing.
Conclusion
The AI revolution isn't a threat to engineering - it’s the exact opposite; it’s a multiplier. The engineers who thrive won’t be the ones who just write code; they’ll be the ones who architect resilient systems, define correctness, streamline delivery and shape the products of the future. Instead of fearing AI, use it. Embrace it. Master it! Be the one who leads the change.