If you are interested in IT and on social media you've probably heard the label 10x engineer kicked around in the last couple of days. One of the first tweets on this topic was:
So according to this, the definition of a 10x engineer is:
- Hates meetings, because everything is obvious
- Works when there are no people around (late nights)
- Doesn't use default settings (e.g. white themes) and uses keyboard shortcuts
- Knows the software code in a project
- Doesn't specialize in a single programming language
- Converts "thought" into "code"
- Doesn't need to look up syntax definitions
- Likes learning new things (e.g. blockchain)
- Does things themselves rather than investing time to teach others
- Writes quality code
- Only moves jobs if the old one makes their life miserable
While some traits are highly beneficial for the IT sector, the internet has erupted with mixed opinions about how much the negative can or should be acceptable for the rare brilliance these people may bring to the company.
E.g. the following tweet is a sarcastic take on the topic:
As an R&D group heavily relying on solid programming skills to produce research tools that are robust and lead to the right answer, we also see the attraction in these mythical 10x engineers.
In the next sections I will summarize the traits and step through how they relate to research and in particular the Transformational Bioinformatics group.
10x engineers are solid programmers
(points 4, 6, 7, 10)
In research and everywhere else, writing good code is more than knowing the entire code-base and being able to modify it fast. In fact, we define "quality code" as code that others can easily, even intuitively, understand.
Therefore memorizing documentation is, according to Albert Einstein, unnecessary ("Never memorize something that you can look up."). In fact, language and libraries evolve so fast that staying on top of the latest syntax is impossible without consulting the documentation frequently. Running the risk of writing in outdated syntax does not make for quality code.
However, we agree that "thinking in code" is highly valuable especially in research. Here, we want to formulate hypotheses and test them experimentally to advance systematically. In our DevOps.com article we argue that "Infrastructure as Code" enables this, especially in the serverless space.
10x engineers get excited by new technology and dare to be different
(points 3, 5, 8)
The controversial but adopted "publish or perish" culture in research favours thinking along the "path less taken" as it has higher chances of uncovering new insights that are worth publishing.
The excitement for the "New" and the urge to learn and understand it is ingrained in research; be that technical topics, languages or scientific findings. As researchers we have learned very early on that being a fan-girl/boy about an idea/method has a lower pay-off than adapting your strategy with new evidence.
For example, our latest software tool, VARSCOT, aims to increase gene therapy safety for medical and biosecurity applications by incorporating huge volumes of information (the genome) in its calculations.
10x engineers prefer to be lone wolves
(points 1, 2, 9, 11)
While everyone hates unnecessary meetings, being a lone wolf rarely pays in research. In fact, science is a team sport. According to a recent Nature article, "honesty, openness and accountability come up again and again" in a survey asking researchers to list what makes a good scientist.
According to James Doty, a neurosurgeon and director of the Center for Compassion and Altruism Research and Education at Stanford University "sympathy lets creativity flourish".
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Are "brilliant jerks" accepted in research?
While the research community still has some issues to resolve, valuing team creativity and charting new frontiers in knowledge together will make it very hard for the 10x engineer clichés to take hold in academia.
(On mobile? View full layout to see the poll.)