The Mythical 10X Programmer
Over 40 years ago, Fred Books published a book called “The Mythical Man-Month.” It was a collection of essays around the thesis that adding resources to a late project makes it later. One of the essays in the book, “The Surgical Team,” describes a way of structuring a team around one super-genius that everyone else helps do their important genius work. It’s a largely insane essay from an ancient era of computing (the ideal “surgical team” has two secretaries and a clerk to input all the code the super-genius wrote on paper onto punch cards). The relevant paragraph from the book:
Programming managers have long recognized wide productivity
variations between good programmers and poor ones. But the
actual measured magnitudes have astounded all of us. In one of
their studies, Sackman, Erikson, and Grant were measuring perfor-
mances of a group of experienced programmers. Within just this
group the ratios between best and worst performances averaged
about 10:1 on productivity measurements and an amazing 5:1 on
program speed and space measurements! In short the $20,000/year
programmer may well be 10 times as productive as the
$10,000/year one. The converse may be true, too. The data
showed no correlation whatsoever between experience and per-
formance. (I doubt if that is universally true.)
If you’ve managed programmers at any point after 1975, you may be wondering how on earth you would measure the performance of a programmer in a scientific study. It turns out that in 1968, they:
…found that the ratio of initial coding time between the best and worst programmers was about 20 to 1; the ratio of debugging times over 25 to 1; of program size 5 to 1; and of program execution speed about 10 to 1.
A few things stand out about this. To create directly comparable results, they used the exact same kind of horseshit Hacker Rank challenges that are near-universally accepted as having little to do with real-world development ability. The study says nothing about maintainability (where code spends most of its life), nor does it measure the kind of skills teams need in the modern era: Communication, adaptability, and collaboration. Lastly, the tools available to develop, debug, and run software (interactive debuggers, automated testing, JIT compilers, etc.) may well have obviated the “10X” advantage a number-cruncher programmer would have had in 1968.
Modern software developers are not interchangeable widgets that can be directly measured or compared. You bring your background, your specialties, your domain knowledge, your biases, and all of who you are to real-life software problems, and that’s awesome.
So why, 40 years later, does the myth persist? I posit a few reasons:
- In any given real-life scenario, some developers bring more to the table than others, and some do that often. Measuring that additional value is both impossible and pointless, but plenty of people think that such a way must exist, and that “10X” sounds like a good a way to describe those people as any. In reality, that advantage rarely translates across every stack, domain, etc., but it’s less impressive to say “I’m a 10X developer on Java backends for FinTech apps as long as I don’t have to do any database stuff, and ‘average developers’ who did it for a few years would be too.”
- Silicon Valley dev culture breeds the kind of narcissists that think of themselves as 10 times better than an imagined “average” developer (usually represented by some dummy they worked with once).
- It’s hard to undo 40 years of cowboy neck-beardery in dev culture. The industry is getting a much-needed influx of people from non-traditional backgrounds which is chipping away at the lone-super-genius-doing-everything story we tell ourselves, but it takes a long time.
- Despite a lot of lip service in recent years, soft skills are still undervalued and algorithmic skills are still overvalued. That’s changing for the better every year, but it’s also going to take a long time.
Some people are wrong on the internet. So?
This is dangerous for a few reasons. The biggest is that the dopamine and testosterone-infused “I’m better than you at everything” implicit in this 10X myth is hardly welcoming to anyone new to the industry, much less women, people of color, or anyone’s who’s already unsure if they belong. Someone who believes themselves to be a 10Xer is also an instant team liability. Such an inflated sense of worth is hardly conducive to sincere self-reflection, growth, or being willing to make and admit mistakes. Also, who wants to work with some asshole who thinks he or she (although honestly, probably he) is 10 times better than you?
Here’s a thought-experiment: Let’s say you can measure individual productivity. You’re on a team of seven, and everyone contributes “1”, so your team’s output is “7”. Now let’s say one of them is a 10Xer, so if everyone else’s output stays the same and the 10Xer contributes no tech-debt (quite the thought experiment indeed), your output is now “16”. There’s some risk to this though- 10Xers expect 10X money, and are self-described as almost impossibly rare. Someone throws a little more cash at your 10Xer, they leave, and now your team’s output is “6,” with a difficult hunt for a replacement ahead of you.
How else might we get to “16"?
You’d get a little more than “16” if everyone on the team brought out 15% more out of everyone else (1.15⁶ * 7≈ 16.19). This is the synergy effect. Not only is that far more realistic, it’s also more robust (losing a person knocks you to “12.6"). The upside is huge too- if the 10Xer picks up another X (which, again, is the supposed entire output of another developer), the team’s output goes to “17”- a little less than the gain if the 15% team makes each other 1% better.
Teams that consistently produce great results are not comprised of solo super-geniuses who have a bunch of secretaries and clerks doing the unglamorous work while they ponder the good and the true, and I’m not sure that was ever the case. Great teams bring out the best in each other. Keep your surgical teams and 10X myths- 15% is good enough for me.
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