Becoming an AI-Native Engineer

Summary

I’m confident I’m not alone in saying I’ve spent a lot of time over the past couple of years thinking about AI as an engineer / developer and what it means for our field and for our careers. For a long time, software engineering felt like a safe bet. After all, how could we be automated away when we’re the ones writing the automation? Or so we thought. Like many, I’ve run the full gamut of emotions on the topic: excitement, uncertainty, fear, frustration, more excitement, more uncertainty, and so on. If there's an emotional response to AI to be had, I’ve probably felt it. But lately, one emotion has consistently stuck, and that’s excitement. It took a while to get here (and arguably even longer to learn to stay here) but I can now say with confidence: the rise of AI excites me more than anything else right now as an engineer. The opportunities seem endless. Things have started to click. I’ve always considered myself a fairly effective engineer by traditional metrics...shipping consistently with quality, solving challenging technical problems, mentoring others. But with AI, I’m producing more than ever before, with a level of speed, quality, and understanding I’ve never experienced. In many ways, it’s 10x’d me as an engineer. The next sections of this post are my take on what it takes to become an AI-native software engineer and build fluency with AI at every level. I’m not claiming to have mastered it all (far from it) but I wanted to put my thoughts down in the hope that they might help another engineer or two get off the emotional rollercoaster and land on excitement. This post is less about specific tools and frameworks (though I’ll sprinkle in a few I've found) and more about the mental models needed to approach AI effectively. As I keep learning and refining my thinking (and as the space continues to evolve), I’ll aim to share more. For now, think of this as a brain dump of random thoughts I have on how I currently view AI’s role in modern software engineering....