Open source lessons for synthetic biology
What bio can learn from the open source work of Tesla, Google, and Red Hat.
When building a biotech start-up, there is a certain inevitability to every conversation you will have. For investors, accelerators, academics, friends, baristas, the first two questions will be: “what do you want to do?” and “have you got a patent yet?”
Almost everything revolves around getting IP protection in place, and patent lawyer meetings are usually the first sign that your spin-off is on the way. But what if there was a way to avoid the patent dance, relying instead on implementation? It seems somewhat utopian, but there is a precedent in the technology world: open source.
What is open source? Essentially, any software in which the source code (the underlying program) is available to anyone else to modify, distribute, etc. This means that, unlike typical proprietary development processes, it lends itself to collaborative development between larger groups, often spread out across large distances. From humble beginnings, the open source movement has developed to the point of providing operating systems (e.g. Linux), Internet browsers (Firefox), 3D modelling software (Blender), monetary alternatives (Bitcoin), and even integrating automation systems for your home (OpenHab).
Money, money, money…
The obvious question is then, “OK, but how do they make money?” The answer to this lies not in attempting to profit from the software code itself, but rather from its implementation as well as the applications which are built on top of it. For the implementation side, take Red Hat Inc., a multinational software company in the S&P 500 with a market cap of $14.2 billion, who produce the extremely popular Red Hat Enterprise Linux distribution. Although open source and freely available, Red Hat makes its money by selling a thoroughly bug-tested operating system and then contracting to provide support for 10 years. Thus, businesses are not buying the code; they are buying a rapid response to any problems.