Principles for Science-Based Entrepreneurship

I’m going to take the perspective of someone trying to build a business based on novel science (as opposed to the effects of science on a more, shall we say, mainstream business). This is hugely important for emerging markets, where generally the attention to science is still minuscule, and where the bridges between academia and entrepreneur, are almost completely absent.

Let me point out two of my recent experiences, and I’ll draw upon these areas to illustrate the principles below. One venture is a machine-learning, software company matching talent and opportunity around the world, Aspiring Minds []; the other is a medical device company facilitating a range of diagnoses for diabetes and helping patients and clinicians manage care Jana Care []. The first draws on recent advances in computer science, the latter on the life sciences. Their similarities and differences are instructive.

The first step for a science-based entrepreneurial endeavor is to develop the science in a cluster where other scientists are located. Problem solving is a lot easier in these milieus especially for a startup which will have a limited number of scientists on the rolls and will inevitably have to seek outside help. Having a good university or a cluster of established science-based companies around sure helps. For Jana Care, we do our science in Boston, a life-sciences hub, though the company started in Bangalore. The algorithms company Aspiring Minds also accesses talent in Boston and San Francisco, but it would be helped even more if it were located there (it’s based out of Delhi and Beijing). As another example, I imagine that if I were working on internet-security and cryptography, I might want access to the Tel Aviv startup culture.

There is, of course, the idea of the ‘death of distance’, a term popularized two decades ago by Frances Cairncross (editor of The Economist) to describe our ever-increasing ability to communicate digitally (and therefore remotely).  It’s true that it’s easier to communicate now of course; indeed, I’m a big believer in crowdsourcing insight by parceling out pieces of the problem for others to solve. At the end of the day, though, I think that nothing beats the convenience of proximity to fellow-science-travelers for those working on novel science.

Second, where you do the science need not be the place where you implement the science. I don’t mean to draw an overly stylized distinction between ‘doing’ and ‘implementing’ – as a colleague Eric von Hippel at MIT has frequently reminded us, “doing” and “using” feeds back strongly into R&D itself – but I’d encourage thinking about where the costs of experimenting with whatever you produce are lowest. That’s where you should try out the results of your science. Thus, our medical devices are deployed across parts of South and Southeast Asia before they’re deployed in Europe. Of course an important caveat to experimenting efficiently, especially for health related sciences, is that patient safety and well-being is paramount, so you can’t economize here!

Third, decide how much of the problem you want to solve. In Aspiring Minds, our algorithms to assess talent could, in principle, have been licensed to someone else to use. We need not have bothered with the rest: building a sales force, delivering millions of tests annually, etcetera.  In practice, we had to build the whole ecosystem to demonstrate its value. For life sciences though, it’s pretty commonplace to do the “science” up to a point. Thereafter, one hands off the results to an entity that’s better suited to further develop the solution to the problem and gets paid for it. Think, for example, of doing research to find new targets for drugs. A collection of scientists often characterize a target molecule, pass it on to a typically much larger pharma company, and often get paid handsomely. Sometimes, these drug targets can pass through multiple hands. Of course, a downside to doing only part of the science is that the scientist-entrepreneur doesn’t get to shape the destiny of what she created!

This principle is more general than in drug discovery; the question being, what part of the problem are you best suited to address? This requires cultivating an approach to partnering and being able to know when to stop working on a problem in favor of handing it off, and whether and how to redeploy resources.

Fourth, protect your intellectual property. Sometimes patents work, in some scientific fields and in some (geographic) jurisdictions. Otherwise, non-patent methods of protecting the technology are usually more effective (it needn’t be either-or). For example, the technology in question could be protected by embedding it in a product, or a business system, making it less useful as a stand-alone imitation.

Fifth, and the most important, be aware of the limits of the science. The limits might come because of your inability to take care of other ambient constraints – hence the partnering and the realism of how much of the solution to develop as mentioned above – but also might come because of how good you really are compared to best-in-class in the world. That just requires humility, open-mindedness, and a willingness to learn. Often, sadly, this is the attribute most in short supply!



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