DSR Section I

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This article is truly fascinating, and the topic is fascinating and quite relevant! It does appear that ICON has an interesting solution to one dimension of the housing crisis — construction costs and scalability. Similar to the previous commenters, I’m a bit concerned about resident/consumer demand for houses that need to be somewhat uniform. Perhaps this technology could be used with a design feature that allows the houses to be designed in a more modular manner and pieced together to the liking of each resident. Alternatively, if ICON can build or acquire the capability to move their homes medium to long distances, they could distribute them over larger geographical areas and make them seem more unique to each consumer.

Zooming out a bit, there is some serious critique of the notion that increasing the housing stock will actually solve the housing crisis, at least in American cities. A recent Federal reserve study (https://www.forbes.com/sites/eriksherman/2018/08/03/additional-building-wont-make-city-housing-more-affordable-says-fed-study/#61b358f9218b) shows that the housing supply has little to no effect on increasing rents and other housing costs, relative to the distribution of high enough paying jobs and

I do think that the current state of the innovation proposal policy runs the risk of amplifying the voices of for-profit entities at the expense of other community members without the know-how or access to the current portal. I’d encourage an outbound strategy for gathering user feedback, distilling them internally into a strategy, and then creating a competitive RFP or separate procurement process for the development. This would help mitigate against undue sway or conflicts of interest.

Additionally, my experience in City government (shared during the design thinking case) suggests that internal capacity of MBTA staff will likely be a binding constraint on the ability to execute on ideas that are crowdsourced from the community. This is likely reflected in the 2.7% procurement rate – not sure what the best strategy would be for improving this capability internally.

On November 15, 2018, DSR Section I commented on Can Organovo Bio-print a Human Organ Before Money Runs Out? :

This is an incredibly interesting article and fascinating topic. I’m also curious about the potential legal and ethical pitfalls of bio-printing certain organs with genetic or other phenotypic improvements beyond a normal healthy human organ. Would this technology be potentially used by those with enough disposable income to replace their normal organs with new and improved versions?

Examples include cardio-vascular improvement for athletes or new livers for competitive drinkers. Could the use of bio-printed organs create a moral hazard problem, in that they actually reduce the consequences of certain risky behavior? In addition to liabilities, I’m curious whether regulation should also include some guidelines on the proper usage and other means of mitigating this kind of uptake.

On November 15, 2018, DSR Section I commented on Juul Labs: Transforming Vaping through Open Innovation :

Very interesting article – given that Juul is a consumer product distributed via retail convenience stores, I’m curious what your vision is for the specific channels for open innovation. Most other instances of open innovation seem to be other kinds of companies such as web platforms, professional services, etc.

Perhaps one way to do this would be a QR code on the back of the packaging that auto downloads a mobile app with ways that users can engage with the product and company (and be used to collect other user data, as well as an interface to control a “connected” Juul by changing dosage, flavors, child lock, etc).

On November 15, 2018, DSR Section I commented on Giving Credit Where its Due: Machine Learning’s Role in Lending :

Hey Abkarians – this is a super interesting article and very relevant to what you’re doing!

I have two topics that are interesting to explore. First, I’m curious how you think so-fi could expand to more severely underserved populations. My impression is that they do target the HENRY segment (high-earning, not rich yet). An interesting dimension here is the risk that measuring based on traditional educational attainment may truncate a massive and increasing part of the market as the education space undergoes its own rapid change (How would a so-fi look at a high-school dropout that is finishing a coding bootcamp or other untraditional degree?).

I’m also curious about the prospect of them expanding down to first-time student loans. It seems that the majority of their refi models are based on later-stage consumer features (educational attainment, utility payments, insurance claims, income, repayment behavior on the original student loan), so I’m curious how they would actually go about expanding into the market that you’re targeting!

On November 15, 2018, DSR Section I commented on Revolutionizing personal credit with machine learning :

Interesting article here! There are a few other trends that have implications for the future of a fintech startup like Avant. First, I’m interested in getting a bit more under the hood surrounding the actual data sources that they use in their machine learning algorithms in order to do proprietary credit underwriting. Based on my personal experience in the space (working with international banks as an alternative credit scoring vendor, similar model to “Amount”), several key datasources come to mind, including existing transaction and other account data at the consumer’s bank, their digital footprint from social media or their mobile network. With regard to existing bank transaction data, it is unclear how an Avant can maintain its unique value proposition as banks themselves acquire fintechs or develop their own data capabilities. An interesting extension of your essay would have explored financial data APIs and aggregators like Plaid.com and the role that they play in the ecosystem. With regard to seemingly nonfinancial data, it would have been interesting to look at the owners of social media and e-commerce data as potential players in the lending space within the next 5 years. One example is a former competitor of mine that build their entire credit scoring product on Facebook API data and whose entire business model was subsequently thrown into crisis mode when Facebook released a patent on social credit scoring and clawed back access to its graph API for such commercial purposes. Amazon and Google are reported to have similar designs. Another interesting development is regulatory change at the Federal level, where the OCC is working towards creating a new Fintech charter that would allow for non-accredited banks to work nationally for the first time. Loved your essay, its a really fascinating space!