David Zhou

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On November 13, 2018, David Zhou commented on Unlocking the Power of Open Innovation in Disaster Relief :

Kaleigh, you presented an outstanding use case for open innovation. I really like what you highlighted in exhibit 2 about utilizing both remote and local sources. Your open question is the biggest challenge I see for the IFRC moving forward. The data being collected is extremely powerful and I fear that it could be used by business who will use predatory pricing to gouge those in need after a disaster. I am very curious about how the Red Cross will handle this challenge moving forward.

Sean, I think you are spot on in highlighting the high costs of qualifying parts through the FAA. Because Danko Arlington is a first mover in the additive space within aerospace castings, I think it could capture a large piece of the market especially as it offers faster innovation at a lower start up cost. The challenge for Danko Arlington will be to drive down the cost curve so its per piece cost begins to get closer to traditional castings. This will strengthen their sales pitch to customers and allow them to entrench themselves in the market due to the high switch costs you mentioned.

On November 12, 2018, David Zhou commented on Sherwin-Williams: A Case Study of Open Innovation :

Rupert, I liked your choice of Sherwin-Williams–it is an unusual company to adopt open innovation. To address your first question, I don’t view Sherwin-Williams’ products as a commodity. Developing new paints requires significant materials research. I believe the company should focus on partnerships with other large companies or government entities when creating new products instead of crowd sourcing. You raised an excellent point about using consumers preferences to manufacture trendy colors earlier. There is huge market potential in being the first to market.

On November 12, 2018, David Zhou commented on Listen Up: Spotify, Machine Learning, and the Podcast Opportunity :

Meg, I agree with the point you brought up about recommendation engines stifling creativity and perpetuating bias. I believe it is imperative for Spotify to address this in the short term. Amazon’s machine learning algorithm for software engineering candidates preferred males due to the previous 10 years of data. I have that same concern for Spotify. If the company does not focus on diversity now, it risks biasing all future content especially as its library expands. Spotify is at a fork in the road and if it decides not to promote diverse programming, it could lose its connection with consumers.

On November 12, 2018, David Zhou commented on Will the Sport of Football Survive? VICIS Says Yes. :

Bueller, I worry about price of VICIS especially as it starts expanding into the youth segment. There are over one million kids in the US between the ages of 6-12 that play tackle football. A 3X price premium could slow adoption into that market. A challenge with additively manufacturing lower priced goods is that it takes huge volumes to offset the fixed costs of the machine, maintenance, and engineering staff required. Once R&D cycle is complete for VICIS, it could revert to traditional manufacturing as a cost savings.

On November 12, 2018, David Zhou commented on Who Defines Beauty: Humans or Meitu? :

Irene, I completely agree with your take that beautification should be a person choice and not a homogenized suggestion from a phone application. However, I struggle with how Meipai or any company could achieve this goal. The power of machine learning comes from aggregating data and identifying trends—like what Meitu discovered about Norwegians liking freckles. I think machine learning will struggle to identify an individual’s preferences apart from that of the group.