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This is a great example of how open innovation can drive both great commercial outcomes and great societal outcomes. I would curious to understand more about how the economics work for a researcher working with CO-ADD. From the statistics described in the article, it seems like the collaboration that CO-ADD facilitates drives a lot of incremental value by increasing the likelihood of success and reducing the cycle time of R&D. In the system as described, who economically benefits from that added value? Incentives are key to driving behavior and if all of that incremental value goes to the researchers and research community that would seem like an amazing way to create a positive feedback mechanism. More participation drives better collaboration which creates incremental value.
It is interesting to think about how the rise of AVs will have ripple effects in other industries – what does a world with all AVs look like. The dynamics of several even outside of the traditional manufacturing companies will be dramatically changed, as will how we interact with cars generally. When car ownership drops dramatically as a result, how do manufacturers compete with each other. Who actually own cars in a world with all AV and who is accountable for when those cars get into some kind of accident: property rights and therefore liability are difficult to describe in a system where decisions are deterministic.
Very interesting article and thought provoking question. Particularly: “There is genuine fear in the media industry that Narrative Science, and startups like it, will eventually one day replace journalists altogether. Is this fear warranted?” I think in addressing this question it is important to think about the different types of activities that journalists do and which of those lend themselves to being completed by machines. While there are certain type of news output (financial data / performance, sports data) that is highly structured and likely able to be replaced by a machine, there is a large part of what journalists do that do not lend themselves to machine learning platforms, for example: building sources, gathering and interpretting information that is not in the public domain. Hopefully, by automating some of the less nuanced work we can free up journalists to do more impactful work.
This sounds like an interesting example where the cost structure of a technology seems to define its near-term use case. As described in the case, given the state of 3d printing, it is not the most economical way to mass produce shoes. The viability of the technology as a means of mass production will be dependent on how quickly they can get down the experience / cost curve. The heavy investment in the technology by ADIDAS today is a signal of their belief in what the future cost structure could become! It does, however, provide an interesting opportunity to produce customized shoes to consumers at a high price (compensating for the cost of manufacturing). This could be an interesting segment that they could address in the form of elite athletes and other high-end consumers.
Super interesting article and technology – we are at an exciting time in the development of regenerative medicine and therapies. As mentioned in the article, there is an interesting tension between control and speed as it comes to the innovation required to drive meaningful results in the business / industry – both from a profits standpoint and a patient outcomes standpoint. The article suggests moving to an open innovation paradigm, partnering with academic institutions and others. While this may increase the speed to market of an effective treatment / product, it seems like the value in this business is in the intellectual property. By moving to an open innovation paradigm, the company may ultimately lose control of the IP that makes the produce successful and therefore the ability to extract rents for that development.