This article is super interesting and brings to light a use case of open innovation that I had not considered before. I agree with the previous comments about the limited long-term viability of any one algorithm to provide consistently outperform the market. I wonder what processes Quantopian has in place to refresh ideas to take advantage of short-term market inefficiencies? How often do they reevaluate their portfolio? Is there a time constraint on holding algorithms? I am also not clear on the reason an author would use Quantopian rather than employing the algorithm themselves, given the low barrier to market entry. Maybe they provide computing power or other benefits from their platform. Fascinating topic, thanks again for sharing!
Thank you for sharing the story of Back of House. It is a fascinating idea and brand, and the current positioning of the company is interesting in the context of machine learning. Your article brings up the pivotal challenges with machine learning: What’s the value? Is it a worthwhile investment for a business? In terms of a clothing company, if it’s true that people care more about their influencer’s clothing than their own style, the benefit of machine learning seems clear. It can highlight purchasing trends and preferences and make recommendations through ads to support future purchases. I am not convinced that machine learning removes the need for a designer though, especially in the progressive, high-fashion sector in which Back of House competes.
Awesome post! Well-written and clearly captures the important ways in which Netflix employs machine learning. The “choose-your-own-ending” idea is particularly interesting as it combines machine learning with open innovation, though I wonder how cost intensive such an endeavor would be for Netflix. In addition to Netflix’s move into content creation, I wonder how they can use machine learning to better advertise their new content? In my experience, and as your essay outlines, it can be tedious and time-consuming to sift through Netflix offerings to find a new show. I know they address this through content suggestion, but there are still so many options. It seems that most interest in shows is generated from word-of-mouth. If they partnered with social media platforms, for example, they could generate ads for shows based on your search results or influencer preferences.
This is a super interesting article that gets at some of the underlying big-beer tensions we read about in Idle Hands. It seems like any new beer, regardless of the quality, suffers from an association with AB-InBev. As you mentioned in the article, the company needs to determine the root causes underlying this association and the main drivers causing consumer preferences to shift. How can open innovation help AB-InBev with these problems? Maybe it’s something as simple as letting consumers vote for the next line of AB-InBev beers. Promote a campaign of three options in the fall and reveal the winner at the Super Bowl? A contest like this drives interest, but also shows trends in preference if the beers are differentiated enough. Another idea is having ZX Ventures invest in something like the Untapped App, which allows you to find, rate, and track beers that you’ve consumed. It’s similar to Vivino for wine and could provide data on what beers consumers like and where they consume (retail, restaurants, concerts, etc).
Your article is well-written and effectively highlights the ways in which Nike has developed additive manufacturing and secured a competitive advantage through the technology. The important question that you pose regarding unforeseen consequences is challenging to even consider because of additive’s relatively limited scope in most people’s daily lives. Is there a day in the future where homes have their own 3D printers, and instead of Nike physically manufacturing a shoe and customers ordering it from a store or website, customers buy a license to the shoe data and print it at home? In such a case, what happens to the many jobs involved in that shoe’s development? This situation seems far off, but it’s an important thought experiment nonetheless. I think it shows that additive is pushing the manufacturing industry in a direction more focused on technical skills and computer science. In the short term, if companies like Nike want to maintain their competitive advantage, they need to focus on educating and training their workforce in the technical aspects of 3D printing. GE recently opened an Additive Training Center near Cincinnati and holds several “manufacturing boot camps” each year to train its engineers in additive processes. This might be a training model Nike can develop as well.
Your article does an excellent job of contrasting the additive developments at BMW with the challenges additive faces within the broader manufacturing industry. It is particularly interesting that you found a case where additive manufacturing, which has historically been cost prohibitive, reduces the time and cost of fabricating a single tool. The challenge going forward is to scale those cost savings at the unit level and make additive feasible for more standardized products. The advances at BMW are exciting, especially printing the i8’s top cover, and it seems like their processes will certainly add to additive’s future viability.