AI Makes Coding Faster, But Who Is Responsible For What It Produces?

Roman Eloshvili is the Founder and CEO of XData Group, a B2B software development company with a focus on the European banking sector.

When AI coding tools come up in conversations, it’s pretty common for people to focus on how much faster they can write up software. It’s not hard to see why: AI can generate large amounts of functional code in just a fraction of the time it would take a human developer to do the same manually. But the more important question, I think, is what happens after that.​

Writing code is not the same as putting reliable software into production. While the first has become relatively easy to automate, the second still requires human judgment and verification.​

The main risk of using AI in this kind of work is not necessarily that it will generate genuinely bad code—in many cases, the code works perfectly well. The problem is context: Even when correct in isolation, that code can still be wrong for the system around it.​

An AI model may generate code lines according to immediate prompts, which is technically accurate—but without looking at the broader architecture. It can, for example, potentially duplicate functionality that already exists elsewhere, creating maintenance problems that need to be solved months down the line. ​

And because the code itself gets generated so quickly, it can sometimes reach production before the team has even fully understood what has actually been done. That leaves a lot of room for risks, and it’s the major reason why code generation can’t be left entirely to artificial intelligence (AI).​

Testing Cannot Have Shortcuts​

If AI writes the vast majority of the codebase, which I’ve found it increasingly does these days, the testing process should not become AI-generated as well. And there should be no cut corners, especially where AI-made code receives less scrutiny than human-written code. The discipline of these checks has to stay rigorous in order to efficiently root out issues.​