do we still enjoy software engineering in the age of AI?
Short summary
A manager and engineer at a security firm discuss how AI tools have stripped away the satisfaction of deep technical work—replacing problem-solving with prompt management and eroding the sense of ownership over code. They explore whether the tradeoff of faster output for lost engagement is worth it, and what might make the role meaningful again.
- Engineers report losing satisfaction because AI handles complexity they once owned, shifting their role from critical thinking toward management-style coordination rather than hands-on technical work.
- The manager acknowledges the real cost: after 15 years mastering a skill, watching AI surpass you at it is demoralizing, but the solution isn't rejecting AI—it's using it to tackle harder problems instead of just faster ones.
- Post-holiday malaise and team disorganization compound the issue, but the underlying problem is that engineers feel disconnected from their own work and the codebase direction when development moves too fast.
Full summary
A software engineer reached out to their manager expressing a real problem: AI tools have stripped away much of the satisfaction from coding. Before, they could take pride in understanding every line they shipped, feel ownership over their work, and gradually deepen their knowledge of the codebase. Now they're using AI to generate code faster, but they've lost that sense of mastery and connection to what they're building. They don't fully understand the output anymore, can't predict what reviewers will catch, and feel increasingly out of touch with the overall direction of their team's work. The engineer also noted that AI tools frustrate them in practical ways—they ignore stored context, can't learn from corrections, and change so rapidly that investing time in optimization feels pointless.
The manager's response cuts to something harder to fix than just tooling complaints. They admit that AI has forced engineers into a role that looks more like middle management—coordinating and directing rather than deeply problem-solving. Both of them spent fifteen years building expertise in their craft, and they're both grappling with the fact that AI is now better at their core skills than they are. The manager frames the shift as a forced evolution: if you want fast outcomes, you have to become good at orchestrating AI rather than writing code yourself. But they acknowledge this isn't what drew most people to engineering in the first place.
The conversation ends with practical moves forward. The manager suggests showcasing team members' contributions even as velocity increases, so people can still feel pride in shipping things. More importantly, they propose shifting the ambition level entirely—instead of using AI to do the same work faster, use it to tackle problems that were previously impossible. The manager's personal strategy has been learning to excel with AI tools rather than competing against them, while also pursuing things outside work (baking, in their case) where they can still be the expert. The underlying message: the job isn't disappearing, but what makes it satisfying has fundamentally changed.