Development Trends 2025: AI Code Generation Will Be the Most Productive AI Use Case

This post is part of G2's 2025 digital trends series. Read more about G2’s perspective on digital transformation trends in an introduction from Tim Sanders, VP, research insights, and additional coverage on trends identified by G2’s analysts.

AI code generation has its drawbacks, but not for long

As buyers search for value in the wake of AI hype, AI code generation seems more promising than ever—in theory. 

The use case for AI code assistants is clear and immediately relevant. Ideally, its buyers should see sustained improvements in development time. However, since both quality and security are at stake, solutions so far require extensive code review and vetting before reaching production, canceling out productivity gains. 

But wait! 2025 will see code assistants stand out as drivers of real business value amid AI noise, impressing on first-pass outputs and requiring less manual intervention.

G2 reviewers say code generation has had the shortest ROI among AI categories in the past six months, and it’s only getting faster

When it comes to gaining productivity from AI code generators, there is crucial tension between automation and code quality. It comes down to the nature of programming as a task. 

If an AI writing assistant generates copy for a human writer to edit, the time savings are relatively straightforward—editing text simply doesn’t take as long as both writing and editing. 

In the case of AI in software development, time saved writing code easily becomes time lost in the debugging and security review phase. Developers are used to fixing one bug only to break something else; it’s a fact of life with programming. However, working with AI-generated code means software engineers have less understanding of how that code works in the first place. 

The solution to this problem is the same solution to most software problems — the tools must improve. That’s it! Thanks for reading!

Okay, I’ll keep going. We’re not at the point where an AI code generator can reliably fix its own mistakes at scale (AI debugging, anyone?). But, tools that produce higher quality code from the onset will relieve the time spent on review. And according to G2 data, we’re already moving in that direction. 

In the past six months, G2 reviewers who answered their estimated ROI have said that AI code generation is the fastest compared to other G2 AI categories. In fact, the category’s current ROI has shrunk by more than half compared to the previous six months (back then, its average ROI was 12.7 months). None of the other AI categories experienced a drop in ROI that drastic between the two periods. These ROI improvements indicate advancements in technology, which in turn are saving people more time. 

AI in software development: Looking ahead

AI in software development is still reckoning with its drawbacks. These tools haven’t fully solved their reliability problem, leading to more time spent on code review compared to traditional development. However, based on how fast the ROI on these solutions has become and how quickly ROI is shrinking for the category, buyers can consider them a safe bet for straightforward productivity improvements in 2025. At a time when buyers are looking for real value among all the AI hype, AI code generation will be as good as it gets.

Will AI code generation replace low code? Learn more to find out!

Edited by Supanna Das