AI’s biggest impact isn’t cranking out more code: Why DevEx matters

Developer experience (DevEx) is more than ping pong tables and free snacks. It’s the foundation for building great software. As AI reshapes the way we code, the real opportunity lies not in writing code faster, but in creating environments where developers can do their best work. In the latest episode of the PurePerformance podcast, hosts Andi Grabner and Brian Wilson interview Laura Tacho, CTO at DX, for a candid discussion on DevEx and AI’s true potential in software engineering. Hint: It’s not about writing code faster.

The impact of DevEx

Tacho reminds us that excellent DevEx is about removing friction from daily work. But measuring DevEx isn’t simple. It’s often about trying to measure an absence. Enter the DX Core 4 framework, co-authored by Tacho and leading software researchers. It focuses on four actionable metrics:

• Speed: How quickly teams can deliver, measured by merge frequency.
• DevEx: Engineers’ self-reported experiences of focus and feedback.
• Quality: Change Failure Rate, or how often production deployments go wrong.
• Impact: The ratio of time spent on innovation versus maintenance.

The major takeaway: Most engineers aren’t great at selling the business impact of improved tooling and better DevEx. But when you improve DevEx, the other three metrics will rise as well. Whether it’s reducing build waits, making information easier to find, or reducing the number of meetings, reducing developer pain points unleashes business value.

Use AI to tackle friction

That tees up Tacho’s other major insight: AI’s biggest impact won’t be in merely cranking out more code—after all, typing speed has never been the bottleneck in software development. The majority of developers’ time is spent on tasks other than coding, like meetings. There’s room for AI to make a bigger difference in developer productivity by speeding up prototyping and tightening feedback loops, which can reduce the amount of time developers spend in meetings discussing what to build, or wasting time building the wrong things.

She says the real return on investment comes from using AI to improve the essentials. “Back to basics is a great headline for what makes companies successful with AI,” Tacho says.

“When we look at what makes agentic workflows work really well, it’s clearly defined services, really robust testing, the ability to detect anomalies in the system and fix them quickly, production debugging, observability. All of these things are core components to developer experience.”

— Laura Tacho, CTO at DX

Rather than just bolting AI onto existing processes, AI presents the opportunity to fundamentally rethink the software development lifecycle from first principles to get these basics right. Tacho points out that the boom in AI and agents is an opportunity to “make a lot of the investments that we’ve been wanting and advocating for, but maybe couldn’t secure a budget for.”

Our perspective: We’re already starting to see the first glimpses of AI boosting DevEx and enabling new workflows, as MCP integrations make it easy for developers to run tests, file pull requests, observe application performance, and more without ever leaving the comfort of their IDE or terminal.