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I would argue that the 10x engineer isn't a myth - the myth is that these 10x more productive employees are unique to engineering whereas they can really be found across industries.

My starting point is this study by biz school professors (unfortunately paywalled) about how power law distributions define employees in sales, scientific research, entertainment, and really much of skilled labor: http://onlinelibrary.wiley.com/doi/10.1111/peps.12054/abstra...

In effect, it's common for 20% of employees to be responsible for 80% of a company's productivity.

I'm a writer at Priceonomics, so you can read the blog post I wrote about the research and the topic here: http://priceonomics.com/whats-so-special-about-star-engineer.... I'd love to hear what HN thinks in the context of this thread.

It's a longread. I think the most important points are:

1) You find star performers in many industries

2) Star performers are not inherently more productive - context is important. They are talented but also benefit from the system supporting them. In one study, the performance of 10x Wall Street analysts crumbled when they switched employers if their team did not come with them.

3) If you get obsessed with 10x employees and A players - and lose sight of important points like the importance of team - you will become Enron. Really. Enron lived and breathed the A players motto until their idolizing of "talent" lost all connection to reality. You can find a link to a great Malcolm Gladwell article on the topic in my post.




Star performers are not inherently more productive - context is important. They are talented but also benefit from the system supporting them. In one study, the performance of 10x Wall Street analysts crumbled when they switched employers if their team did not come with them.

http://en.wikipedia.org/wiki/Alexey_Stakhanov

Stakhanov was held up as the >10x more productive coal miner; however, it's now generally understood that this was the result of a propaganda effort, and he had a team preparing and clearing for him.

This is a common risk in work-rate-measuring systems, how do you account for worker A spending time which saves the time of worker B? Especially in knowledge work, where the old guy/gal who does very little but carries all the oral history of the company in their head can be more important than anyone realises.


Its not about productivity. The net work achieved is totally by any means a wrong metric. What I mean to say is you can't measure human productivity in the same way you measure a machine's productivity.

But there are a few things about star performers that I see. They tend to pick up the toughest problems and work on them. Importance to small yet important details. Extreme importance to quality, relevance and impact of the problem has on the real work and business scenarios.

In most ways, it has nothing to do with education, talent or skill. These are really good habits cultivated and sustained over time.


I think it is a very good article, which would have been greatly improved by mixing in, First, Break All The Rules.

The key point from that being that it isn't so much that there are A players, and B players, it is that you only get true stars when the role fits the person. (Stated that way because it is easier to fit work to people than vice versa.)


Paints a great+balanced high-level picture picture of the phenomena - I noticed it was submitted a few weeks ago without much attention. I'd also add reading about TopCoder and IOI / various competitions - star performers in another, highly measurable, field. Sometimes, absolute skill differences at the top may be small but in a competition environment lead to huge result disparities in wins/losses (see sports or eSports - every small advantage matters). Though mosts aspects of engineering aren't highly competitive, I'd argue similar features such as need for creativity or logically difficult problems, produce the power-law-like distribution.

Rereading the Medium piece now draws me to its relative lack of citation especially in the moral arguments toward the end, with a lot of judgments on a shaky empirical ground.


I've seen it in my workplace. We're very particular about who we hire, but even so the best hires have been more than 10x as productive as the worst over the course of a year. It's definitely a real thing.




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