This article was originally commissioned by Luca Rossi (paywalled) for refactoring.fm, on February 11th, 2025. Luca edited a version of it that emphasized the importance of building “10x engineering teams” . It was later picked up by IEEE Spectrum (!!!), who scrapped most of the teams content and published a different, shorter piece on March 13th.
This is my personal edit. It is not exactly identical to either of the versions that have been publicly released to date. It contains a lot of the source material for the talk I gave last week at #LDX3 in London, “In Praise of ‘Normal’ Engineers” (slides), and a couple weeks ago at CraftConf.
In Praise of “Normal” Engineers
Most of us have encountered a few engineers who seem practically magician-like, a class apart from the rest of us in their ability to reason about complex mental models, leap to non-obvious yet elegant solutions, or emit waves of high quality code at unreal velocity.
I have run into any number of these incredible beings over the course of my career. I think this is what explains the curious durability of the “10x engineer” meme. It may be based on flimsy, shoddy research, and the claims people have made to defend it have often been risible (e.g. “10x engineers have dark backgrounds, are rarely seen doing UI work, are poor mentors and interviewers”), or blatantly double down on stereotypes (“we look for young dudes in hoodies that remind us of Mark Zuckerberg”). But damn if it doesn’t resonate with experience. It just feels true.
The problem is not the idea that there are engineers who are 10x as productive as other engineers. I don’t have a problem with this statement; in fact, that much seems self-evidently true. The problems I do have are twofold.
Measuring productivity is fraught and imperfect
First: how are you measuring productivity? I have a problem with the implication that there is One True Metric of productivity that you can standardize and sort people by. Consider, for a moment, the sheer combinatorial magnitude of skills and experiences at play:
Are you working on microprocessors, IoT, database internals, web services, user experience, mobile apps, consulting, embedded systems, cryptography, animation, training models for gen AI… what?
Are you using golang, python, COBOL, lisp, perl, React, or brainfuck? What version, which libraries, which frameworks, what data models? What other software and build dependencies must you have mastered?
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