AI is reshaping software engineering into two increasingly distinct paths: product engineers focused on what to build and why, and foundational engineers focused on the deep systems, infrastructure, security, and scale that make it possible.
As AI handles more of the "how," engineers will have to decide where they want to create the most impact.
AI can accelerate engineering, but you can't build trust by assuming everyone understands everything it generates.
Strong test suites, CI checks, AI and human reviews, and experienced engineers create the guardrails that keep AI-assisted development moving in the right direction, especially for junior teammates still building judgment.
Great leaders don't control every task. They create the space for people to think, challenge, and solve problems creatively.
Leadership is about serving your team, being a sounding board, and helping people find the best solutions rather than using the role to control them.
The question isn't whether teams should use AI anymore. It's how to use it well.
The biggest wins come from moving idea to output dramatically faster, while keeping human judgment, clear standards, and discipline in the loop so speed doesn't turn into chaos.
Being a great engineer doesn't automatically make you a great leader, and wanting the promotion certainly doesn't either.
Great leadership comes from genuinely understanding people, building strong teams, and having enough experience to recognize when to step in, pull the handbrake, and steer the team toward a better outcome.
AI is helping teams move faster, but the pressure to constantly keep up can quietly turn productivity into burnout.
As AI changes how we work, leaders need to rethink how they measure performance, and how they protect the balance between output and sustainability.