Devin AI: Will It Really Replace Developers?

The world's first AI software engineer

In March 2024, when Cognition AI unveiled "Devin, the world's first AI software engineer," the IT world erupted.

In the demo video, Devin autonomously read GitHub issues, wrote code, debugged, and submitted PRs. "Developers are done" was the immediate reaction.

On Twitter, people posted "I'm quitting my dev studies." A student deciding between computer science programs asked "Is it even worth it anymore?" and got thousands of replies.

Media rushed to report that "the day AI replaces programmers has arrived."

Two years later, where does Devin stand? My assessment: "Impressive, but nowhere near replacement."

Actual performance

Looking at external benchmark results after Devin's release, its success rate on SWE-bench -- a test involving real GitHub issue resolution -- was initially about 13.86%.

After improvements, it reached roughly 25% by late 2025.

How should we read 25%? Autonomously solving 1 in 4 issues is undeniably impressive. But flip it: 3 out of 4 issues remain unsolved.

From hands-on experience, the issues Devin handles well are mostly pattern-based tasks.

Library version upgrades, simple bug fixes, type error resolution -- that sort of thing. Complex issues requiring system design or business context understanding were almost never solved.

It was completely helpless when an issue required understanding "why this feature should behave this way." It understands code syntax but not business meaning.

A wave of similar tools

After Devin, similar AI coding agents sprouted everywhere. GitHub Copilot Workspace, Amazon Q Developer Agent, and dozens of open-source projects.

These agents share a common trait: stunning productivity on simple repetitive tasks, but they actually consume more time on complex ones.

The paradox of spending more time reviewing and fixing AI-written code than it would have taken to write it yourself occurs frequently.

Once, I had an AI agent refactor an API endpoint. It produced working code, but completely ignored the existing error-handling patterns and implemented something entirely different. I ended up rewriting it from scratch.

But in practice

People say "AI is replacing developers," but in reality, what AI is replacing is "coding tasks," not "developers."

Think about what software development actually entails. Understanding requirements, designing systems, making technical decisions, communicating with the team, responding to operational issues, incorporating user feedback.

Research suggests that the time spent "writing code" accounts for only about 30% of the total.

AI making that 30% more efficient is a completely different story from replacing developers. Much like how calculators didn't eliminate mathematicians.

How the developer role will evolve

In my view, the advancement of AI coding agents is shifting the developer's role from "coder" to "orchestrator."

In the past, the ability to implement algorithms directly was the core skill. Going forward, the ability to give AI correct instructions, evaluate AI output, and coordinate the entire system becomes more important.

This is an opportunity, not a threat. Freed from repetitive boilerplate code, you can spend time on more creative, higher-value work.

That said, for developers who "only know how to write code," this is genuinely a crisis.

Coding can be replaced. But the ability to define problems and design solutions is territory AI hasn't yet touched. That's where to focus.