MVM's Profile

I agree with some of the sentiments above that developing a consumer product-focused VC arm may prove very difficult just based on the competitiveness of that industry and the challenge of building that capability out organically, since it would likely require building out a new team. I think in technology VC, where Foundry is playing, it may be slightly easier to determine whether a startup’s technology has the potential to be disruptive or how it could benefit Unilever than it might be to determine which consumer startup brands have the potential to connect with consumers and explode. I also think this would force Unilever to walk a pretty thin line between supporting its existing brands as well as its startup businesses, which would presumably be in direct competition with Unilever’s existing brands.

Very interesting use of open innovation. I think this was a great opportunity for VHO to slim down their potential targets for the hyperloop by ruling out markets that were less commercially attractive, but I would definitely be careful with how closely I allow the partners to be involved in the next implementation phase. Ultimately when implementing these solutions VHO are the experts and will need to make decisions in the best interest of the company. I could envision clashes with their partners if situations arise in which they need to alter or go back on aspects of the partners’ commercialization plan. I would make sure that the partners are looped in with the projects but might want to consider keeping them at arms length during implementation.

Interesting application and one that I think makes a lot of sense given the potential cost savings down the road with added scale and improved printing technology. One thing that I would want to really think about if I were Adidas is whether I want to pursue this as strictly an internal cost savings strategy or additionally as a way to provide consumers increased customization. While customization seems like a positive on the surface I think it gives Adidas less control over its own brand. In a market where design is so important I think I would want to retain control over how my products look and feel.

I am curious as to how the eventual rise of 3D-printed car parts will affect the entire automotive supply chain. Companies like Ford generally have a long list of suppliers that provide the multitude of parts that go into an automobile, and I wonder whether the improvement in R&D and prototype time from 3D printing might influence Ford and others to bring more of these parts in-house. Without Ford and others pushing to bring more part manufacturing in-house I think it could take a while for this technology to trickle into the supply chain, since the upfront investment required for part suppliers (often much smaller companies) to completely revolutionize their manufacturing operations could severely slow the adoption of these printing systems.

I agree with the comment above and think this is a good example of a situation where a machine-human combination would be more effective than a machine on its own. I think the issue of getting past prior biases in the human recruiting process is a challenging one, and I wonder if the value here is more in the machine drawing out interesting correlations rather than actually making the hiring decisions (or slimming the field) on its own. If the machine is able to provide visibility into what correlations are driving its specific recommendations then 1) the machine may bring to light important indicators of future performance that were previously unidentified by humans; and 2) a human could then make judgements on whether or not those correlations are valid criteria for making a hiring decision.

Interesting article. I think one piece that Hinge may be missing in order to better refine their machine learning capabilities and improve their services is soliciting user feedback based on actual meet-ups. Exchanges of phone numbers is a good proxy for facilitating a strong match, but it isn’t until two people meet up that they can determine whether the match was actually a good one. I think Hinge’s machine learning engine could use the feedback received from actual meet-ups to correlate which combination of attributes or preferences indicate stronger matches and improve the app’s predictive abilities over time.