I think we do specialization of labor wrong a lot. This makes me think there is a lot to be gained from a sort of back-to-basics approach, and I feel like knowledge work in general and research in particular are good candidates.
The classic example of specialization of labor is comparing the output of a single blacksmith in the production of pins with 1⁄10 the output of a factory where 10 men work producing pins, but each is responsible for 1-3 tasks in the pin-making process. Each man gets very good at his appointed tasks, and as a consequence the per-worker output is 4800 pins per day, and the lone metalworker’s output is perhaps 20 pins per day.
Imagine for a moment how research is done: there is a Primary Investigator who runs a lab, who works with grad students and postdocs. Normally when the PI has a research project, he will assign the work to his different assistants with so-and-so gathering the samples, and so-and-so running them through the equipment, and so-and-so preparing some preliminary analysis. In this way the labor is divided; Adam Smith called it division of labor; why don’t I think it counts?
Because they aren’t specializing. The pitch with the pin factory is that each worker gets very good at their 1-3 pin-making tasks. The division of labor is ad-hoc in the research case; in fact the odds are good that the opportunity to get very good is effectively avoided, because each assistant is expected to be competent in each of the relevant tasks.
This is because a scientist is a blacksmith-of-abstractions, and the grad students and postdocs are the apprentices. The point is only mostly to produce research; the other point is to turn the assistants into future PIs themselves.
This is how “artisanal” small research labs work, but larger research groups usually have more specialisation—especially in industry, which happens to have almost all the large-scale research groups. People might specialise in statistics and data analysis, experimental design, lab work, research software engineering, etc. Bell Labs did not operate on the PI model, for example; nor does DARPA or Google or …
I’d be interested in hearing more about how Google runs things, which I have no knowledge of; I note that Bell Labs and DARPA are the go-to examples of standout production, and one of them is defunct now. This also scans with (what I understand to be) the usual economic finding that large firms produce more innovation on average than small ones.
I have other examples from industry I’ve been thinking about where the problem seems to be dividing labor at the wrong level (or maybe not applying it completely at every level?). The examples I have in mind here are pharmaceutical companies, who underwent a long series of mergers starting in the 80s.
The interesting detail here is that they did do specialization very similar to the pin example, but they didn’t really account for research being a much broader problem than pin-making. Prior to the big downsizing of labs in pharma and chemicals, the story went that all these labs during mergers had been glommed together and ideas would just be put in one end and then either a working product came out the other end or not; there was no feedback loop at the level of the product or idea.
This looks to me like a case where small university labs are only specialized at the meta level, and the failing industrial labs are only specialized at the object level. It feels to me like if there were a graceful way to describe the notion that specialization has dimensionality, we’d be able to innovate better.
Specialization of Labor in Research
I think we do specialization of labor wrong a lot. This makes me think there is a lot to be gained from a sort of back-to-basics approach, and I feel like knowledge work in general and research in particular are good candidates.
The classic example of specialization of labor is comparing the output of a single blacksmith in the production of pins with 1⁄10 the output of a factory where 10 men work producing pins, but each is responsible for 1-3 tasks in the pin-making process. Each man gets very good at his appointed tasks, and as a consequence the per-worker output is 4800 pins per day, and the lone metalworker’s output is perhaps 20 pins per day.
Imagine for a moment how research is done: there is a Primary Investigator who runs a lab, who works with grad students and postdocs. Normally when the PI has a research project, he will assign the work to his different assistants with so-and-so gathering the samples, and so-and-so running them through the equipment, and so-and-so preparing some preliminary analysis. In this way the labor is divided; Adam Smith called it division of labor; why don’t I think it counts?
Because they aren’t specializing. The pitch with the pin factory is that each worker gets very good at their 1-3 pin-making tasks. The division of labor is ad-hoc in the research case; in fact the odds are good that the opportunity to get very good is effectively avoided, because each assistant is expected to be competent in each of the relevant tasks.
This is because a scientist is a blacksmith-of-abstractions, and the grad students and postdocs are the apprentices. The point is only mostly to produce research; the other point is to turn the assistants into future PIs themselves.
This is how “artisanal” small research labs work, but larger research groups usually have more specialisation—especially in industry, which happens to have almost all the large-scale research groups. People might specialise in statistics and data analysis, experimental design, lab work, research software engineering, etc. Bell Labs did not operate on the PI model, for example; nor does DARPA or Google or …
I’d be interested in hearing more about how Google runs things, which I have no knowledge of; I note that Bell Labs and DARPA are the go-to examples of standout production, and one of them is defunct now. This also scans with (what I understand to be) the usual economic finding that large firms produce more innovation on average than small ones.
I have other examples from industry I’ve been thinking about where the problem seems to be dividing labor at the wrong level (or maybe not applying it completely at every level?). The examples I have in mind here are pharmaceutical companies, who underwent a long series of mergers starting in the 80s.
The interesting detail here is that they did do specialization very similar to the pin example, but they didn’t really account for research being a much broader problem than pin-making. Prior to the big downsizing of labs in pharma and chemicals, the story went that all these labs during mergers had been glommed together and ideas would just be put in one end and then either a working product came out the other end or not; there was no feedback loop at the level of the product or idea.
This looks to me like a case where small university labs are only specialized at the meta level, and the failing industrial labs are only specialized at the object level. It feels to me like if there were a graceful way to describe the notion that specialization has dimensionality, we’d be able to innovate better.