Wow – printable chocolate, real game changer. My one reservation is the cost-effectiveness of scaling. In evaluating the feasibility of scaling this into other product lines, I’d be curious to know more information about the costs associated with additive manufacturing. Additionally, what are the health risks associated with using 3D printing within factories (i.e. allergy sensitivity, diversity of products within the same production plant).
Very fascinating piece. My pushback to using additive printing in the cosmetic industry more broadly is the exact consideration you raised, namely the time between ideation/patent and product development. Given the trend nature of beauty and the incredibly unpredictable pace with which the cosmetic industry moves, the lead time required here is economically unfeasible. Perhaps one way Chanel can extend this product innovation is by creating new product lines – one that comes to mind is the emerging false lashes space (i.e. strip lashes) which is expected to grow at a 7% CAGR over the next 2 years.
I think the utilization of machine learning for AirBnB is very interesting. While I like the intention of using machine learning to analyze guest reviews, I think there is danger in using other guest reviews as training data. My concern is rooted in the fact that more often than not, extreme reviews (either positive or negative) are more likely to be posted vs the more accurate/average review. Therefore, I would not use crowdsourcing as a method of influencing algorithmic predictions. One interesting way AirBnB could also utilize machine learning is using learnt behaviors through user data to provide recommendations for actual trip destinations.
Great piece! I immediately thought of Lays when learning about open innovation as a method of product development. They’ve done an excellent job in taking advantage of crowdsourcing and using data to spot new viable markets for different product lines, which has obvious benefits for the broader PepsiCo family of brands. I think you raise a very important point regarding the issue with ownership and intellectual property, as is commonly a risk/issue when using open innovation to source ideas. One way to possibly address this issue is to invite creators to be featured on the bag or in commercials when promoting the new flavor. I think by having consumers more intimately involved in the R&D process across different products aside from chips, PepsiCo is essentially elongating customer lifetime value. One way to improve this partnership and perhaps the quality of ideas is to localize open innovation challenges according to different geographies. This will perhaps increase the likelihood of producing a flavor that bodes well within different regions.
Thanks for this insightful paper. The Gates Foundation, and others in the space including orgs like the Acumen Fund and Echoing Green, are all doing very interesting work using open innovation. However, the danger with relying solely on these Challenges — as with crowdsourcing more broadly — is the inability to control the quality and variety of ideas. Given that Gates and other foundations often have return thresholds of 5% as well as dedicated teams staffed across their main impact verticals – Gates being Global Health, Global Development, US, and Global Advocacy – I struggle to see how open innovation can effectively produce enough ideas to match each of their internal objectives. One major risk with crowdsourcing is the threat of special interest groups dominating the broader pool of ideas, which is directly in conflict with having specific impact goals.
Other potential concerns I have with your proposal:
1) Do foundations and other NGOs have the adequate resources to properly assess and investigate the ideas that come from the crowdsourcing method?
2) How do you inexpensively insure that participation, and thereby the probability of increased quality, will be high via the open innovation method?
Very interesting article. To your question around the viability of AI in the long-term, I am inclined to believe that given the risk and term associated with VC-backed projects and investments, complete automation is unlikely and highly risky. I think the use of algorithmic predictions for founders’ success is an interesting proposition and of great value however, as you alluded to, heavily depends on the reliability and quantum of data. To this end, I’d suggest looking into what other VCs that have utilized machine learning are doing to improve the quality and virility of their training data. For example, Hone Capital, which is a tech-focused VC, is utilizing AngelList to analyze over 30,000 deals from the past 10 years to feed to a machine-learning model. Based on this, they have established an assessment scorecard of 20 characteristics (whittled down from a list of 400) that are most indicative of success.