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.
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.