Navigating the shift from Hardware to Software in the AI Era

Engineering is essentially the art of building stable systems, whether you're dealing with circuits or complex software architectures. For the past two years, I've been deep in the weeds of electrical and electronics engineering here in Turkey, but lately, it feels like my internal logic is pointing me toward a total rewrite: I'm switching my major to computer engineering next year. This isn't just a casual change; it's a massive pivot driven by a personal mandate to build a stable, high-performance career that honors the legacy my father left me.

But here is the real question: how do you calibrate your compass when the entire industry landscape is shifting like tectonic plates?

Transitioning from the physical constraints of hardware to the abstraction layers of software feels like moving from building a house to building a cloud—the rules are different, and the speed is terrifying. I’m staring at this massive, swirling fog of new frameworks and AI models, wondering if I'm even building on a solid foundation. If the industry is being re-architected by AI in real-time, is there even a "stable" role left to aim for, or are we all just trying to debug a system that's changing its own source code every week?

I’ve been digging into some deep-dive perspectives on where this tech evolution is actually heading, specifically looking at how the professional playbook might change:

https://ai-2040.com/?choices=plan-a-root#playbook-insider-pov

I don't want to just drift through the transition; I want to understand the underlying mechanics of how this new AI-driven era connects to traditional engineering principles. Has anyone else gone through a complete "system reboot" regarding their career path? How do you filter through the noise to find the technologies that actually matter under the hood, rather than just chasing the latest hype cycle? I'd love to hear how you managed to find your footing when the map you were using suddenly became obsolete.