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This certainly is a complex question – “appropriate” means something different to everyone, and the global reach of YouTube means that they will need to consider how content will be received across geographies. Given some of the concerns raised by the Google memo, I’m curious about the formal and informal structures that the management team has implemented to separate the internal culture and values of the company from the judgment of content they may not agree with. YouTube requires on the input of many diverse creators, so alienating people who want to share or consume controversial opinions could foster the development of other video hosting platforms and drive people off of YouTube. It sounds like they are investing heavily in building a strong team to tackle this challenge, but will they be able to attract and retain a diverse enough group of talent to approach this problem without bias?
I think you make a great point that the economies of scale would potentially push hospitals towards outsourcing this work to third parties. It will be interesting to see how these companies will manage large manufacturing operations in the context of the sensitive nature of the products are that they are building. It will be very important to have strict and effective quality control to make sure that the surgical models are produced to specification, especially if they are being used to plan very complex and delicate surgeries. Also, the companies will need to manage inventory and information securely – the orders and physical models contain sensitive health data, and will need to be protected as such as they move through the supply chain. In the face of these uncertainties and the risk of malpractice suits that doctors in the US face, will the surgeons trust products that weren’t made under strict control in-house?
I am curious as to what those Futurecraft 4D shoes cost Adidas to make – while my initial reaction was that people are willing to pay a premium for this new technology, I realized that Adidas could be selling these at a loss to try to build hype around the general concept of 3D printed shoes. Unit costs will surely decline with increased volume, but I’m curious as to how low Adidas expects it to be. There definitely seems to be a market for $300 shoes, but 3D printing can’t play a central role in Adidas’ strategy until the production costs are low enough to sell at a more accessible price point. Allen Kim seems very keen on this technology, so I’m curious if he sees a world in which every Adidas product is 3D printed. If so, is it an imminent reality or a long term vision?
What excites me the most about the distribution of these large government data sets is the potential for truly additive public work to be done. There are many issues that are too small or obscure for the government to dedicate their attention to, so this data creates the opportunity for people who are passionate about specific issues to pitch in. We’ve seen motivated experts build a world-class free encyclopedia, and I like to think that we could see a similar trend in public administration as data science skills become more prevalent in our society. If the government can establish an efficient and accessible path for review and implementation of outside analyses, I think we would introduce a lot of innovation into public administration without the taxpayer spending that would be associated with funding a large government data science team.
It’s interesting to view personalization through the lens of the underlying characteristics that draw people to one product over another. I’m curious about the intended extent of the personalization – will the company seek to pair customers to a set of existing perfume options, or will they attempt to create truly unique blends for each person? I suspect that the intent would be the latter, but it seems like an incredibly complex problem to solve from the beginning. A rich set of data from each customer can be a good thing, but can also introduce uncertainty as more variables will make it harder to find and leverage the truly meaningful data relationships that will add value to your output. For this to work, you will also require a robust feedback loop from the customer – the learning won’t improve if you can’t capture both how much the customer likes the overall product and what specific attribute(s) of the product make them like it.