Very interesting concept. This type of entertainment seems very fitting with many of the preferences of today’s generation. A few potential competitors that come to mind include Escape the Room, Improv Comedy, Drunk Shakespeare. These forms of entertainment are either immersive (Escape the Room) or look for audience input from time to time (Improv). These competitors, however, maintain control over the majority of the experience and script. My concern with crowd-sourcing content and create open source scripts is that this approach could actually seriously impact quality and negatively impact the brand or concept. Are there mechanisms that Secret Cinema could put in place in order to facilitate this type of engagement while still curating or controlling quality?
At the end of your post, you pose the idea of requiring local governments to use crowd-sourced solutions. While I think that great ideas can certainly be uncovered through crowd-sourcing, I also fear that there is a risk of it becoming the next sexy, innovative approach to public policy that isn’t necessarily the most efficient use of government time or funding. For example, social entrepreneurship became a big focus area roughly a decade ago and numerous government entities created programs that direct funding to social enterprises. As a result, sometimes funding is directed to small social enterprise when the issue would really be better addressed through investment in infrastructure development etc. Crowd-sourcing worked really well in the situation in Mexico City because the issue was well defined and the solution could clearly benefit from the aggregated input of numerous citizens. Hearing the perspectives and ideas of constituents is certainly useful, but I’m not sure that it is something we should look to as a silver bullet or a new requirement within the government.
Very interesting post. Thanks for sharing! Similar to BlueSky, I would like to better understand the cost breakdown of 3D construction vs. traditional construction in various contexts and for various projects. My hunch is that, additive manufacturing is currently only cost effective in a very select set of use cases due to high labor costs (as in the case of the Dutch projects) or unique design requirements. I am specifically curious to better understand the potential for this technology in emerging markets – Is this really a cost effective method for infrastructure development or is this another “leap frog technology” that people get excited about but which realistically has prohibitive up-front investment in technology and talent?
As mentioned in the comments above, I’m concerned that a fully customized shoe to customers may not gain significant traction with customers. Many customers are prompted to buy shoes by seeing something that we like as opposed to going out to look for something that is similar to a customized idea we had in mind. I therefore think there is value in having thoughtful and attractive designs that prompt customers. Additive manufacturing may, however, have significant benefits in the production of insoles or add-ons that improve the comfort and performance of shoes.
In your post you suggest that “they could refine the attractiveness of different categories of investments or perhaps build in data points around qualitative features, like characteristics of its management teams or organizational design.” I’m skeptical that these data points or metrics can be effectively incorporated into an investing algorithm. The premise of Helio seems to be that early sales data, social media mentions etc. are leading indicators of success for small consumer brands and can be tracked to move quickly on up-and-coming brands. Those metrics seem to have a pretty direct link to potential for success across all companies. With qualitative features, however, there are numerous models for success. As we’ve learned in LEAD, no single management style or organization design is a leading indicator of future growth or success.
Is it ethical to test the plan on a person in order to find out? As in many industries, replacing human decision-making with machine learning poses serious risks. A potential path forward is to design systems and tools that are focused on assisting, rather than replacing, the decisions of experienced medical practitioners. To start, perhaps a hospital could could deploy a system alongside doctors and allow it to gather data and learn without revealing recommendations to doctors. Once a more robust technology has been developed, its recommendations could be leveraged alongside other data sources as inputs to help inform or speed up decision-making.