Gabriel Araujo

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Hi Joaquin! Thanks for all the work put into this – the article is great!

My main concern with this model is scalability. While this open-innovation model seems to reduce the cost of developing and designing a new model, I’m under the impression those designs will tend not to be cost-conscious. While the collective power might be great at having cool designs/concepts, I tend to be a little more skeptical of people’s incentives to keep the bill of materials at a reasonable cost. As a result, I’m under the impression those models would actually have a higher cost/unit and, therefore, only be suitable for a niche market.

What do you think? Do you think this innovation model helps or prevents the company from scaling its production?

Great article! I think this technology has the potential to disrupt the construction and real estate market. Due to significant cost reductions, I believe it could be especially valuable for the affordable housing market.
On the legislation factor, I would argue for the company to try to get buy-in from the government and the engineering boards before it goes on. Differently from ridesharing apps, construction companies usually have projects that depend on public financing or are contracted by the government itself. In this context, ignoring current legislation could generate backlash from the government and prevent 3D printing adoption.

On November 15, 2018, Gabriel Araujo commented on Machine learning as a tool to predict future earning power :

Hey David! I really like this idea of a market for “personal equity”!

I have a two question/comments. The first is on the algorithm and the second on the business model itself.

1) I understood that you are trying to look at historical data to try to find correlation between that data and realized income. Nevertheless, I would guess that the historical level of debt may actually influence someone’s career path (if that person needs to repay, will actually search for a high income job vs. his/her “life aspiration”). On the other hand, people that have “outstanding personal equity ” might go on more “risky” careers, since they do not have to repay school and will have to share their income anyway. How are you guys thinking about that potential bias in the data? Do you see any way to “correct it”?

2) On the business model, I imagine that a decisive moment for the company comes when the first successful “personal equity” agreements start to bear results and show nice return/low default rates for investors. Given the long term nature of the “loans”, how do you see AlmaPact iterating it’s models and being able to scale it’s “personal equity” agreements? As importantly, how to follow and evaluate the probabilities of repayment over time in the first few years of AlmaPact, before actual results start to show? I feel that would be crucial to prevent a “big surprise” when repayment period begins.

On November 15, 2018, Gabriel Araujo commented on Solidifying the impact of open data innovation in the government – NYC311 :

This is a very interesting concept!
One aspect that comes to the top of my mind when analyzing this article is how can NYC 311 initiative generate high quality sustainable products for the society, instead of one-off analysis only? In my opinion, this will only happen when there is an incentive for individuals and companies to come up with solutions. For example, is there a way to allow startups to build apps in areas such as transportation and security, helping some of the city problems while also making a profit?

Fascinating topic, even though I think it might still take a while for this idea to become feasible economically – I imagine investment/fixed costs of these machines would be pretty high. Maybe one potential near term application could be pharmaceutical R&D – since those already incur huge investments, 3D printing might be able to turn the process more efficiently. What do you think?

To your questions:
– Quality control and responsibility: I might be completely off, but I tend to think those risks are very similar to those a pharmaceutical company faces today. Therefore, I think there might be ways to reduce or accommodate those risks.

– Usage of the machine by different brands: Due to the high investment I mentioned earlier, I would assume that for this idea to work on hospitals/care centers in rural areas one single machine would have to print a wide variety of medicines from different companies. As one of the comments mentioned earlier, it would be important to achieve a revenue sharing model that could protect patents while still providing a financial benefit for the 3D printing company.

On November 15, 2018, Gabriel Araujo commented on Ant Financial – Pioneering China Fintech with Machine Learning :

I found the topic to be very interesting! Specifically, it opened my eyes to the huge opportunities Ant Financial has on the B2B space. By coupling Alibaba’s data on consumption with Ant Financial’s data on transactions, the company now has close to full visibility on consuming habits of a big part of the Chinese population. This vision is incredibly valuable to all consumer brands that are trying to reach their target market, and how well this data is used could be potentially the differentiation between Alibaba and Tencent on the e-commerce space.

On your questions, I would argue that Ant Financial is probably looking to expand internationally through acquisition. By acquiring local companies’ userbase and adding new services, they would be able to create value in both developed and non-developed markets. In a non-developed digital transaction market for example, they could acquire a messaging or financial education app and transform that into a mobile wallet. On the other hand, in a developed market, they could acquire an already popular wallet and introduce new services such as loans and insurances. Either way, I believe it’s critical for Ant to use it’s capitalization to move quickly in attacking the market.