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Thanks for sharing! This open innovation project by Amazon seems to be super effective in certain domains of problem solving. Specifically, it works well with project with well-defined scope and the goal of making incremental changes. I would assume that this will be hard if you want to crowdsource the solution for drone delivery, for example. While I do think these competitions are useful, I am reserved about how scalable and sustainable it is for the broader innovation topics that Amazon is working on.

Very interesting read. My article is similar but focused on Bumble. My analysis is that Machines would definitely get better and more efficient at matching single people looking for love. However, that is contingent on the amount of users they have as well as the number of data they can collect per user. Given this, do you think Match.com will start consolidating different platforms so they can cross-match people and generate greater efficiency? In addition, once the algorithm has been significantly optimized, do you think the economics model of these apps will be different? (e.g., charge you a lot more per match given its accuracy instead of providing the service to its users for free and make money off subscription and advertisement).

Thanks for sharing. This is such a wonderful playbook for crowdsourcing product development ideas. I wonder, though, how sustainable is the practice? From surveying the users or conducting idea competition between its customers, how much incremental insights can the company extract every time? In a way, customers have no access to the sales and product data, which in a way allows them to only contribute based on their feelings and experiences and personal taste. Are they able to continuously produce ideas that can be visionary for the company? Also, how is this process different than other companies doing focus groups, surveys, and A/B testing to understand user preference and gather product innovation ideas?

From the article, it seems that currently ML can only provide incremental values to education (e.g., assistance with grading, detection of students’ attention, data analytics etc.). It seems like ML will not fundamentally change the industry of education as teachers and administrators will still be a an essential part of the industry, and the heavy operations side of education will not be changed in the short term.

Thanks Peter for sharing this. To your point of creating social values, I think Bytedance is creating social value in a way that it provides both news information and entertainment for its customers- which is essential needs of human being. However, the question is how much of those are healthy and how much would cross the line as being addictive. Another question would be how Bytedance can explore other ways to generate revenue than merely relying on the advertisement- otherwise their incentive would always be to get users to stay online as long as possible.

I enjoyed your article greatly, especially for the part where you compared Nike’s short term strategy with using 3D printing for prototyping versus long term strategy when 3D printing becomes common-place at home and how that impact would be. My assumption is that there will be some democratization of manufacturing in the long term- and in this case, product design will be more of Nike’s competitive advantage than their manufacturing ability.

This is a great perspective on how additive manufacturing can improve the production processes, which is such an important issue to Boeing to solve given the volatility of the industry and the past failing experiences of matching cost forecast with realized cost. However, I wonder, if there is a solid comparison between the costs of using 3D printing for manufacturing vs. the traditional way of doing it- is it going to substantially reduce the cost? What about the tooling cost of setting up 3D printing?