New study reveals what really drives revenue per engineer
Bill Doerrfeld | April 10, 2025
My latest post on LeadDev explores the factors that contribute to higher revenue per engineer.
New benchmarking from DX reveals how areas like R&D spend, org size, and growth rate move the needle.
The factors correlated with higher RpE include:
- Investing more in research and development — especially in vendor tools and services
- Having a startup mindset — companies founded after 2010 show higher RpE
- Maintaining steady year-over-year revenue growth — not scaling wildly, but not stagnating either
Engineering has long been a black box, so it’s refreshing to see data on what actually contributes to higher performance.
Check out my full analysis on LeadDev!
Featured image credit: @towfiqu999999
Sign up to the DirectorPlus newsletter for a new story in your inbox each month featuring on-the-ground insights from some of the industry's top engineering leaders. Or, if you would like to be featured in the series, please read this.
AI researchers are calling the next class of models large action models (LAMs). For The New Stack, I explored what LAMs are, what examples are emerging in the market, and what experts think.
My article for LeadDev explores specific prompting techniques proven to sharpen your AI-assisted software development.
Nordic APIs, the API-specific blog I edit, was recently ranked the top API blog online by FeedSpot. After ten years managing this presence, I reflect a bit on the journey thus far.
Developers are realizing that being productive with AI coding assistants takes a lot more than just asking nicely. There's real craft to it.
AI coding is the easy part. Now it's time to focus on DevOps to get it into production. In a recent interview for LeadDev's DirectorPlus, Honeycomb's CTO, Charity Majors, shares expert tips on how to accomplish this.
My latest for The New Stack explores new research from Ai2, DataDecide, which helps LLM developers make better decisions around training data.
My APISEC|CON talk covers the hype around agentic AI and MCP, and delves into inherent flaws in MCP architectures and suggests mitigations.
AI coding assistants are a productivity dream in some cases — and a debugging nightmare in others. So, where’s the line?
Semantic caching is like typical caching, but for AI. It could eliminate a lot of redundant API calls to LLMs, reducing costs and improving performance.
For CIO.com, leading executives shared with me how they're actively utilizing agentic AI to enhance core business workflows.
