To your question about how the company should think about licensing/leveraging/donating the technology to serve those in need, I think they’d have to monetize (instead of donate) given the large upfront investment this requires, and would envision a contracted licensing agreement with governments. I question though the feasibility of achieving profitability by going this route, given the limited budgets of municipal governments as well as the rate of adoption that they might face in working with governments. I do think there’s a broader affordable housing need that could be tackled, even if it’s more upmarket (and therefore serves less acute housing need) than the applications related to homelessness and natural disasters. To your second question, I think Contour Crafting will run into enough roadblocks and challenges as it is driving innovation for housing on Earth, and should focus almost entirely on that in the next 10 years; the iterative learning they will go through building processes that they can disseminate across the world will surely inform their approach more generally.
I actually wonder whether Hinge might go as far as to scan the content of the “personal interactions” that do happen over the app (e.g., keywords in messages, speech and reply patterns, etc.) to determine compatibility as another input into the machine learning. This of course would imply that they would want to encourage more connections in-app between people, and it’s debatable whether the best way to achieve this is to encourage their users to provide more data, or to get more users overall. Re: your point about this potentially further feeding people’s biases, I think they could leverage the huge amounts of data they have about people’s liking behavior on Hinge and present that in a clean, summarized way that would make people more aware of their preferences in aggregate and in turn think more deeply about their biases. Definitely a sensitive subject to broach, but could be something that users find valuable and informative.
This is a great example of the public sector embracing a form of innovation that has the potential to help underserved communities. The private sector is often not incentivized to solve these problems and the public sector often doesn’t have the resources to solve these problems, so it’s refreshing to see a city government recognize the power of crowdsourcing talent. To address the problem that you raise about furthering inequality, the city could take the open innovation one step further and have the public vote on high-potential app ideas. It could also back specific projects that are most consistent with the priorities that the city most wants to promote and signal to the public the types of projects that are especially welcome.
Very interesting application of 3D printing. I agree with your assessment that the greatest potential exists in the middle of the market, though I think it could go two ways. The iterative possibilities of designing custom jewelry might open up the higher end of the middle of the market given the higher level of “service.” Alternatively, if jewelry makers seek a value advantage, perhaps they will leverage the technology and pass on the cost savings to the consumer and make upper-middle market jewelry more accessible at greater scale.
I’d definitely be concerned about the problem you raise about whether a “machine learning investor” would be able to identify a successful investment that bears huge risk, without regressing to the mean. I also suspect that a large part of the venture investing decision is based on the quality of the team, which would be represented by qualitative factors that aren’t necessarily perceptible in an algorithm. Machine learning could identify patterns (e.g., Stanford computer science major = good), but it would be difficult to act on inputs such as someone’s trustworthiness, value system, etc. Regardless, this is a great case for the combined power of human and machine, and there definitely seems to be a place for the machine to outperform humans in making purely data-driven decisions without bias in the context of an investment decision.