I struggle with answering many of the questions posed by this piece and the comments above. Mainly, how do you maintain a competitive advantage in an open source world? TOM Student and Jayne do a great job of providing me some clarity on this question. By crowdsourcing, Natura is also creating an authentic connection with its customers and in some ways is benefiting from network effects. The larger the crowd that they get to tap into, the better the ideas will be and the harder it will be for a competitor to replicate.
One thing I would like to add to the article and the comments above is that AM could drive huge potential weight savings on parts. This could happen by eliminating the fasteners, screws, bolts, or other means of putting traditionally manufactured pieces together. While I agree with July that the industry is slow to develop and requires intense quality control, the potential benefits of shaving a few ounces here and a few ounces there are enormous.
Great article Kelsey! I am a big believer in the revolutionary impact that AM has the potential to provide in the aviation, and specifically military aviation, space. I’m more bullish on the supply chain implications for forward deployed units than I am for the immediate potential of 3-D printing entire aircraft. The savings from not dragging a “buffer” of spare parts around the world could easily offset the cost of a 3-D printer and the research to make the parts. However, aviation is a wildly regulated space, and flight testing needs to be done for each part in addition to an aircraft as a whole when a new part is added. I’m unsure as to houw the FAA will react to the AM space.
Furthermore, I’m would propose that the Boeing acquisition likely did not have anything to do with the cancelling of the DARPA contract. My assertion is that the government is notoriously slow moving, and cancelling a contract is incredibly hard to do. It is generally a reaction to years worth of documented underperformance by a significant degree as opposed to anything that could have happened over a single year.
Very interesting article N. Fleming. Having gone through the borrowing process I find it incredible that this company can process loans in 24 hours while still reducing defaults by almost 75%. I’m curious as to what data sources have the highest correlation with default rates/ ability and willingness to repay. It will be interesting to see what data consumers are willing to share in order for lending decisions to be made.
I agree with your assertion that the algorithm will not fully be tested until we see how it performs through each phase of the economic cycle. On the other hand, I’m unsure about whether BankMobile should replicate Upstarts core competency in house. Why not leverage Upstart’s expertise while focusing on growing your business and extending your product offerings?
Fascinating post. Like Kombucha said, I think the honesty side of the equation is an incredibly interesting problem to solve. As we have discussed in class – when it comes to machine learning in general, the output is only as good as the data that is input. I wonder if facial recognition techniques are warranted, or if they could simply apply the types of algorithms they are already using, to filter the applications into low and high risk profiles. Then, LendingClub could use their own assets more efficiently by focusing resources on high risk files.
Additionally, I completely agree with your insight into the other lending spaces (beyond consumer loans) that this technology could be applied to.