Great paper – I agree that better underwriting can generate a sustainable advantage for an insurance company. I wonder about the social implications of putting trackers and cameras in every car. Would the resulting underwriting become more discriminatory (like the pre-ACA U.S. health insurance system where insurers were denying patients based on pre-existing conditions)? Are there other concerns with a private corporation having a comprehensive record of every driver’s activity data?
Very interesting ideas, and such a fundamental problem for our healthcare system. I would argue that the U.S. government already has a framework for crowdsourcing solutions to problems like this – they do it all the time in military procurement as well as some civilian agencies. The military routinely puts out specifications for the function of the technology that it would like to procure and then solicits bids for many different approaches.
This structure is increasingly being applied to social problems like the opioid epidemic with something called a “social impact bond” where the government agrees to pay a fixed price for a given social outcome.
Very interesting article, and I think this is a super important problem. The benefits of open innovation here are clear: crowdsourced ideas generate more variance (which is valuable as we’ve discussed in TOM) and come from people with an external perspective. Going forward, I wonder whether the NFL could continue to use this principle of open innovation to apply to other areas of player safety. For example, could the NFL crowd-source potential rules changes and the testing of those changes? Or could the NFL ask for high school and college teams to submit head impact data on all of their players to further study the effects of playing football on players before they even get to the pros?
I love the concept of democratizing access to machine learning tools. One of the concerns about machine learning is that it will increase income inequality as the largest, most successful corporations are able to deploy these tools and further out compete small businesses. For this reason, I think tools that make AI easily accessible and usable by small businesses (like Einstein) are extremely important. There are still obviously kinks to work out (especially demonstrating ROI for customers!) but the potential of this technology is very high.
This is a very interesting essay. As a runner I personally would love to have a shoe that is tailored to my running style. I do, however, agree with the thought behind your question at the end of the essay: I am skeptical that additive manufacturing will ever be able to produce shoes with the same cost basis as more traditional manufacturing processes. Additionally, I would be curious whether scientists have a good enough understanding of biomechanics to truly personalize shoes for each individual. It seems to me that many of these “customized” products are really just 2-3 different SKUs.
This is a very interesting and well-written essay. I completely agree that 3-d printing provides an important avenue to allow companies to iterate quickly on manufacturing concepts. This flexibility allows them to both test things that wouldn’t otherwise be possible, and test them much more quickly than they could if they were relying on other forms of fabrication.
The one question I would raise, however is whether 3-D printing is viable at scale. In your essay, you imply that Centimo could use 3-D printing to fabricate all of its products and product lines. I would be very curious to better understand the unit economics of manufacturing in this way. I would guess (though I don’t know) that 3-d printing does not generate the same economies of scale as traditional production mechanisms. If this is true, 3-D printing would primarily remain a tool for internal iteration on a concept, rather than a technique for mass-production.