Innovation is one of the key criteria by which grant applications and academic publications are judged. To fulfill the innovation criterion, academics are incentivized to distinguish themselves from others and to pursue novel research. In the academic sciences, novelty comes in the form of new discoveries, whereas in academic engineering it comes in the form of proofs of concept. ^5
This novelty prioritization manifests in two problematic ways. First, it is one of the reasons that Academia is not a good environment for systems engineering. Second, it leads to many promising technologies failing to reach their potential like baby dinosaurs that are fossilized right as they’re hatching.::Need better analogy::
It’s impossible to count^1 the number of times that someone embedded in the academic way of thinking^2 has dismissed a project because ‘there was nothing new about it.’ This completely serious dismissal has included proposals to build a useful system that had never been built before, but each of its components had been. If your experience building things is anything like mine, you realize that there is a huge gap between “it should work” and “it does work.” And unlike putting together lego sets, there is a lot of creative and sometimes research-level work to be done in order to assemble a set of working components into a single system.
People rarely get grants or academic accolades for doing the work to turn a one-off prototype into a technology that is actually useful. And for context, most academic prototypes are not simply the full system but custom-built by hand. They’re a duct-taped mess that requires the person who built it to poke it in just the right place and then connect a wire after waiting exactly 3.2 seconds (or the chemical, biological, software equivalent.) While demoing the prototype system that laid the groundwork for modern personal computing, Douglas Engelbart
would spend the entire presentation listening to the murmur of impending disaster: “Bill, we’ve lost XYZ!” or “Bill, we’re not going to be ready in time!”^3 Turning a prototype into something that people who didn’t make it themselves can use requires increased consistency, robustness, generalization, and legibility, all of which can end up requiring additional conceptual leaps. Note that some of the things Engelbart demoed in 1968 didn’t become widespread until twenty years later. Prototypes are also fraught with tacit knowledge^4 so if their original creators don’t have an incentive to help, the chances of them becoming something more decrease.
The charitable view is that academic rejection of building systems and going beyond prototypes is simply specialization and division of labor — they’re important activities but not academia’s institutional job.
^5:From A new structure for scalable research
^1: Partially because it has happened so many times and partially because I have a terrible memory
^2: It’s worthwhile to point out that the novelty-prioritizing way of thinking bleeds out into many domains but its origin is squarely in academic culture
^3:<sidenote> From The Dream Machine by M. Mitchell Waldrop
^4: See Tacit Knowledge, Trust, and the Q of Sapphire for an amazing exposition on this point.