5 metrics in DX for measuring the impact of AI on developer productivity
Discover five essential DX metrics for measuring AI across four key dimensions: speed, effectiveness, quality, and impact.
Kali Watkins
Product Marketing
Research & Insights
Building better software faster: Leading in the age of AI
Learn how to measure AI's real impact on developer productivity with proven frameworks. Get actionable strategies to move beyond hype and drive measurable improvements in your engineering organization.
Laura Tacho
CTO
How Faire's platform team built an AI code review agent
A closer look at how Faire is putting AI to work beyond just code generation.
Justin Reock
Deputy CTO
Unpacking METR’s findings: Does AI slow developers down?
A new study found developers were slower with AI coding tools than without. In this candid interview, METR study participant Quentin Anthony reveals why, and shares tips for using AI more effectively.
Kali Watkins
Product Marketing
How to introduce AI impact metrics into an engineering organization
Engineering leaders are under pressure to measure the impact of AI tools, but doing it wrong can backfire. This guide shares four best practices for introducing AI metrics in a thoughtful way.
Laura Tacho
CTO
Where does the time go?
If developers are saving time with AI, where does the time go?
Laura Tacho
CTO
Measuring AI in software teams: Trends we’re beginning to see
Early trends on how AI is reshaping software development, and how leaders can measure what matters to drive real impact.
Abi Noda
CEO
Data collection methodologies for measuring AI impact
There are three complementary data collection methods that, together, offer a well-rounded approach to measuring the impact of AI tooling.
Laura Tacho
CTO
METR’s study on how AI affects developer productivity
In this study, developers were 19% slower when using AI.
Abi Noda
CEO
AI: a complete paradigm shift, or just another dev tool?
A straightforward answer: it's both.
Laura Tacho
CTO
Takeaways: How to measure engineering productivity in the AI era
DX CEO and CTO Abi Noda and Laura Tacho discuss how to measure AI’s impact in engineering, track AI-generated code, safeguard quality, and more.
Brook Perry
Marketing
Using DX as a Director of Engineering at Adyen
A Director of Engineering at Adyen uses DX to align with Product, influence platform roadmaps, and drive data-driven decisions across teams.
Kali Watkins
Product Marketing
How can you measure AI's impact on your organization?
Despite AI's impact on how developers work, the core objectives remain the same: delivering working software that solves real problems
Laura Tacho
CTO
Introducing the AI Measurement Framework
A research-based approach for measuring the impact of AI in your organization.
Laura Tacho
CTO
How Snowflake’s DevProd team operates
The communication channels, rhythms, and principles that made Snowflake’s DevProd team one of the most trusted teams in the company.
Kali Watkins
Product Marketing
AI acceptance rate: Easy to measure, easy to misuse
The question is no longer does AI work; it’s how well, and for whom, and where is the most value being created?
Laura Tacho
CTO
How to communicate the reality of AI-assisted engineering in today’s hype cycle
Show real use cases, outline the cost of enablement, and tell a story with data.
Laura Tacho
CTO
Setting targets for developer productivity metrics
Principles for setting goals around developer productivity metrics while avoiding common pitfalls.
Laura Tacho
CTO
Findings from Microsoft’s 3-week study on Copilot use
After three weeks of using Copilot, developers felt more positive about the tool and AI overall, but they also emphasized the ongoing need to carefully validate AI-generated code.
Abi Noda
CEO
How Google is accelerating code migrations with AI
Google cuts code migration time in half by automating tasks with AI.
Abi Noda
CEO