More than code: what AI gives back to engineers

Today's tech blogger: Chris Menz, Stream Technical Architect and UBS Distinguished Engineer

There is a moment most engineers remember: the first time they wrote code that made a machine do exactly what they intended. Maybe just a small program, a simple script, something that worked. The outcome on the screen may have been trivial, but the realization was not: software could translate human thought into deterministic action and once that clicked, something changed.

This is when, for many of us, engineering became more than a technical skill. And today, as AI tools reshape how we work, that feeling is coming back – stronger and at a much larger scale. Our work has always been more than code, especially early in a career, there is this unique sense of momentum when you build, you learn, you experiment. AI is reigniting that mindset, lowering barriers between ideas and execution and renewing the curiosity that sits at the heart of engineering.

As we gain expertise over time, the work deepens. We start to understand that good engineering is not only about making something run. It is about making something that other people can work with, build on and trust. We learn to think about the team of people around us, not just the problem in front of us.

AI tools are extending that tradition. Engineers now have more opportunity to spend time on the work that really matters: design decisions, architecture and the questions that require real experience to answer well. The repetitive parts move faster and the judgment-driven parts get more attention.

There is sometimes a common concern that AI might make engineering less meaningful. In practice, the opposite seems to be happening.

When a tool can handle the parts of the work that were always a little tedious, engineers get more time for what truly defines the craft: reading a codebase carefully, spotting a design problem early, thinking through how a system will behave under pressure and asking how something should be built with quality in mind - not just whether it works.

Many engineers who have embraced these tools often describe a similar experience. They feel as though they have regained something they didn’t realize they were missing: space to think, room to explore and test a direction quickly before committing to it. Instead of spending more time on execution, they can spend more time on judgment, exploration and design.

The craft is not disappearing. If anything, it is sharpening.

The engineers I find most exciting to work with today are the ones who treat these tools as partners in their own development. They are not just simply using AI to complete tasks more quickly. They are using it to challenge their assumptions, deepen their understanding of complex problems and explore ideas they might not have pursued on their own. In doing so, they are accelerating their growth in ways that were difficult to imagine only a few years ago.

And that growth matters because engineering has always been a profession shaped by continuous learning.

One of the most exciting shifts is how quickly engineers are growing when they use AI tools with genuine curiosity. These tools provide exposure to a wider range of problems, patterns and approaches than any single team or codebase could offer on its own. What once took years can now happen in months – for those willing to explore. Engineers can ask questions they might have hesitated to ask a colleague. They can explore a direction, see how it plays out, and understand why it did or didn’t work, all within the same afternoon. The path to becoming a strong engineer is getting faster without becoming shallower. The difference, as always, is curiosity.

What makes this moment exciting is that the things that drew many of us to engineering in the first place are still very much alive. AI hasn’t taken that away - it has amplified it.

The spark is still the same, just bigger.

By reducing some of the friction between ideas and execution, it creates more time to think, more opportunities to learn and more time to focus on the parts of the work that require human judgment at its best. So yes, the spark is still the same – the difference is just that there are now more ways to follow it, more opportunities to learn from it and more possibilities to see where it leads. And that is what makes this such a compelling moment to be an engineer.

And perhaps most importantly, none of this happens in isolation. The best engineers do not build alone – they learn from others, challenge ideas together and turn expertise into collective progress.

The more engineers learn, the more effective they become at collaborating, and one of the most powerful shifts AI brings is the way it reduces friction between people. Product people, data people, engineers – people who often speak different “languages” – are finding it easier to collaborate.

The result? AI doesn't just help engineers understand code better – it helps them understand people better: how product managers think, what designers optimize for, what business stakeholders need and how different teams frame problems.

Today, ideas move faster from concept to implementation, with less translation needed along the way. An idea that once required multiple rounds of discussion to become a detailed technical specification can now move more directly from the person who had it to the person who builds it. 

When the communication barrier shrinks, the best ideas surface more quickly.

I’ve watched teams where people from very different backgrounds sat down together around a problem and came out with something none of them would have built alone. For me, the future of engineering is not about individual engineers becoming more productive in isolation. It is about making it easier for people to learn from one another, understand one another and build together.