Raj Sethi, senior vice president and go-to-market leader for software development lifecycle (SDLC) at GlobalLogic, pushes back on the narrative that generative AI will eliminate the need for junior developers. He argues that while AI can generate boilerplate code, tests and scaffolding, modern software delivery still depends on human judgment, critical thinking and a deep understanding of how systems are designed, built and evolved over time.

Sethi notes that recent layoffs and hiring slowdowns are less about AI replacing humans and more about organizations recalibrating after over-hiring and then scrambling to understand how AI actually fits into their productivity models. Despite headlines about “60–70% of code written by GenAI,” he points out that teams still need people to design prompts, review what AI produces, validate correctness, and reason about architecture, algorithms and performance. If organizations skip that layer of human oversight—especially at the junior level—they risk creating an unsustainable wave of technical debt.

He also challenges how junior talent is being prepared and evaluated. In an era where learning resources, courses and tools are democratized, Sethi urges aspiring developers to treat education as continuous, not transactional. Degrees alone aren’t enough; portfolios, open source contributions and visible problem-solving matter more than ever. He encourages students to focus on fundamentals—math, algorithms, linguistics, systems thinking—and to use AI as a collaborator rather than a crutch.

For DevOps and software delivery teams, the future isn’t about replacing junior engineers with AI, but about elevating their role. Junior developers will spend less time on repetitive plumbing and more time reviewing AI-generated output, refining specs, improving quality and learning how to orchestrate systems. In that world, the organizations that invest in growing their next generation of engineers—not just in buying tools—will be the ones that actually benefit from AI at scale.