(E25) DORA and the AI Capabilities Model: Nathen Harvey on Why AI Amplifies the Best and Worst of Your SDLC
In this episode of Developers Who Test, host Chris Harbert sits down with Nathen Harvey, who leads the DORA team at Google Cloud. Nathen has co-authored multiple reports on software delivery performance and was a contributor and editor for 97 Things Every Cloud Engineer Should Know.
The conversation starts with what people get wrong about DORA. Nathen explains that the famous four (now five) software delivery metrics are just the surface. Treating them as the whole picture is like stepping on a scale every day and expecting the number to change: the metrics tell you how you are doing, but it is the underlying capabilities and practices that actually move them. He walks through how Accelerate introduced DORA to most of the industry, why so many readers stop at the four keys on page 19, and how the capabilities model in the appendix is where the real value lives.
Chris and Nathen dig into a decade of findings: throughput and stability move together rather than in opposition, smaller batches lead to better outcomes, and trunk-based development is both one of the most effective and most controversial practices, including its surprising link to burnout on teams new to it. They talk about why alignment across practices matters, since you cannot adopt trunk-based development without also addressing test automation, test data management, and CI/CD, and why the goal should be to become an elite improver rather than an elite performer.
The second half focuses on DORA's new AI Capabilities Model, published in December 2025. With roughly 90 percent of respondents now using AI professionally, the differentiator is no longer whether you use AI but how. Nathen lays out the seven capabilities that amplify AI's benefits: a clear and communicated AI stance, a healthy data ecosystem, AI-accessible data, working in small batches, strong version control, user centricity, and a high-quality internal platform. The core 2025 finding is that AI is an amplifier across the SDLC: high-performing teams get faster, while teams with bottlenecks feel that pain even more acutely when they push ten times more change into an unscaled review or testing process.
They close on what this means in practice: AI is democratizing who can build software, so investing in platform guardrails, fast feedback, ephemeral environments, and high parallelism testing becomes more important than ever. Nathen points listeners to dora.dev and the dora.community to assess their own capabilities and start improving.
Key Topics:
- Common misconceptions and anti-patterns around the DORA metrics
- Why the four (now five) keys are only the surface of software delivery performance
- How Accelerate and the capabilities model fit together
- Throughput and stability improving together, not in tension
- Trunk-based development, smaller batches, and the burnout finding
- Alignment across test automation, test data, and CI/CD
- Becoming an elite improver instead of an elite performer
- Contextualizing findings and user centricity
- The new AI Capabilities Model and its seven capabilities
- AI as an amplifier of both strengths and bottlenecks across the SDLC
- Democratized building, platform guardrails, and the renewed importance of fast feedback