DEVOPSdigest

The rise of generative AI has sparked a wave of speculation about whether it might one day replace developers. While hype around AI capabilities has many people worried, the reality playing out on engineering teams today is quite different. Instead, AI is handling repetitive tasks and allowing developers to concentrate on solving complex problems. In fact, 84% of tech professionals say AI has already made their work easier, according to Pluralsight's 2025 AI Skills Report, which surveyed 1,200 executives and IT professionals across the U.S. and U.K.

This type of technology-driven shift isn't entirely new. Consider the calculator as an example. When these number-crunching tools became widely available, we didn't stop doing math, we just stopped doing it by hand. That change allowed us to work quicker and focus on higher-level thinking. AI is creating a similar dynamic in development. When boilerplate code and syntax are handled by a tool, developers have more time to architect better systems and think critically about the choices they're making. AI isn't replacing developers — it's transforming the way they work.

From Boilerplate to Breakthroughs

The true benefit of AI today is that it takes the repetitive, time-consuming tasks off developers' plates — the kind of routine work that disrupts flow — and gives them more time for tackling complex logic or system design. I'm talking about tasks like rewriting the same functions, copying code from documentation, or digging up snippets from older projects. AI is well-suited to automate this kind of work, and when used intentionally, it helps teams move faster and stay focused on the problems that actually require human insight, and ultimately add the most value to projects.

That said, it's not a hands-off process because quickly generated code still needs thoughtful review. It must be secured, validated, and understood in context. Large Language Models (LLMs) are helpful, but they don't make informed decisions because contrary to popular belief, these systems don't think like humans. Rather, they're logic boxes that use probability to make decisions. Discerning judgment still falls squarely on the developer, who must evaluate the LLM's output, understand its implications, and decide how to apply AI-generated code responsibly.

Speed Where It Matters Most

AI delivers the most value in the early stages of development, helping teams scaffold projects, spin up a basic function, or turn a rough idea into something testable. That kind of acceleration is meaningful. It speeds up experimentation and helps teams iterate more quickly.

Importantly, this isn't the first time productivity gains have come from abstraction. Moving from assembly to higher-level languages gave developers a massive boost. That shift meant sacrificing some fine-grain control, but it delivered major improvements in speed, clarity, and accessibility. The same was true with frameworks and APIs. AI is simply the next evolution. Instead of eliminating the need for developers, it's changing where they focus their efforts and enabling them to ship code faster.

The Developer Is Still the Decision Maker

These shifts also bring new responsibilities. Just as you wouldn't ask for a tool to write an email to your CEO and hit send without reading it, you shouldn't deploy AI-derived code without reviewing it first. AI coding assistants can provide a starting point, but the quality, safety, and performance of that code still depend on the developer. As OpenAI cofounder Andrej Karpathy noted during a 2025 keynote hosted by Y Combinator, "I'm still the bottleneck … I have to make sure this thing isn't introducing bugs." Even with the most advanced tools available, experienced engineers are still the final safeguard.

That's especially relevant for junior engineers. A growing number of leaders are asking how new developers will gain experience if AI handles the basics. It's a fair concern that teams may be tempted to prioritize short-term speed over long-term growth.

But it does not have to be a tradeoff. With the right support, such as mentorship, sandbox environments, skills assessments, and regular code reviews, AI can enhance learning despite changing the types of hands-on experience junior developers will get. AI provides a different kind of starting point, but developers still grow by actively engaging with code and feedback. 

What This Means for Engineering Leaders

For leaders, this is a tooling conversation and a cultural one. How AI gets adopted inside teams depends heavily on how it's introduced, discussed, and reinforced. Giving developers access to tools is just the beginning. It's just as important to normalize AI usage, reinforce high standards for review, and implement plans for ongoing upskilling as the technology evolves.

The most effective teams will treat AI as a collaborator rather than a crutch. And the most successful developers will be those who know when to leverage AI and when to rely on their own experience and judgment. This moment is about reinvestment, not replacement, giving developers the time and space to focus on the work that matters, and giving teams the capacity to move faster without cutting corners. It's a strategic shift that builds a long-term advantage, fosters innovation, and strengthens the overall effectiveness of engineering teams.