Great and thought-provoking article. I struggle a bit to see the incentives for a researcher to participate in CO-ADD (other than pure scientific pursuit). Are there scenarios in which a for-profit company encourages open innovation and there are incentives for all parties involved? Given the sheer size of the task that CO-ADD and others are faced with, I can see the value of collaboration but wonder if there is inefficiency due to potentially duplicative efforts of CO-ADD and for-profit companies.
I’m interested in the last question you raised. How defensible is Gantri’s business model? While 3D printing is an amazing deal for the small business / independent designer, who ends up consolidating power in the supply chain under this business model? Is it Gantri? If so, how does the company maintain that grip? I agree with your view that Gantri’s value-add is that of a marketplace, rather than its 3D printing process. That said, the company may want to consider ways to increase the value of its network to consumers and designers while increasing switching costs for both.
Great article and interesting topic! You mentioned the issue of competition, which I also see as an issue in the 3D printing / bioprinting industry. How can Organovo (or others) create their own monopolies in this space? What do you think of open sourcing “designs” and allowing other companies to produce bioink, while maintaining access to those designs via Organovo exclusive? Is that a potentially good strategy?
I actually think that in order to be even more valuable to consumers, Pinterest may need to gain deeper access into current users’ information. By doing this, the company would be able to create a database of profiles of users that could then be compared to profiles of potential new users. This information could be used to curate new users’ dashboard from day 1, increasing the likelihood of retention. The difficulty in this would be persuading users to grant Pinterest access to their data (e.g. age, location, etc.)
This is interesting. Though I think it’s possible for a machine learning algorithm to identify whether or not a person will be attracted to another, I’m skeptical about whether such an algorithm can predict long-term compatibility between partners. It would be difficult to collect the numerous data points necessary to make any conclusion related to the factors that predict such compatibility. Related to this, it would be interesting to see how Match Group’s revenue model could change if the company’s customers judged its value based on long-term success (i.e. staying together with their partner). Finally, suppose Match Group’s algorithm is widely successful and more accurate than human judgement. What then becomes the purpose of courtship and dating? Should we all just accept marriages arranged by a machine?