Thanks, Tasnia. Really interesting! I think the point you raise around retaining and supporting innovators is exactly right. I can imagine, for example, that some of the really successful innovators with large product growth will want to break out on their own to start their own companies and capture more of the value themselves. It seems this could lead to a cycle of one-time innovations, with some of the biggest hit innovators leaving the platform after the product takes off. I’d also be wary of this being a fad. We are at a particular moment in time where much of our generation values customization, being different, and being first movers to new and exciting brands. This is certainly the case in beauty, as evidenced by the rapid growth of companies like Glossier which were fundamentally born out of social media. I wonder though whether a company like Volition, with such a broad array of messages and visions, will be able to hold on to a customer after the novelty of up-voting and individual design has worn off. Glossier, though similarly born from the internet, has the benefit of being a highly cohesive brand with a strong message that consumers resonate with. I’m personally skeptical that Volition will be able to achieve the same. Time will tell! Thanks again – really insightful.
Thanks, Melina! Really interesting read. To your point, the algorithm itself might be quite scalable as the company expands, but it is the other factors of the business that could prove more tricky. Yes, there will be variations in preferences in different markets, but a good machine learning algorithm should be able to take a series of inputs and develop a similar pattern. The potentially more challenging issues could be things like business development, partner management (for the restaurants for example), and even regulatory hurdles depending on the market. Maintaining a lean development team, as you suggest, and potentially focusing additional resources on building strategic partnerships and momentum in new markets would likely be a wise growth strategy.
Thanks, Alec. This is really interesting! As a patient, I am encouraged by all the potential safety benefits that computer vision offers. I can see how the privacy issues present a significant short term concern, but have to imagine that in the medium term people will get used to the technology and will positively weigh the safety benefits against the privacy costs. I do wonder though, much as with autonomous vehicles and the app (Natural Cycles) that I wrote about, what the potential reaction to a failure in judgement on the part of the algorithm would be. This raises a number of questions about the future that I’m sure Stanford is thinking about. For example, as it evolves to monitor more than just hand washing, but also more physiological factors, what will be the threshold of error that we require? And, as we discussed in the IBM case, what will be the threshold for decisions made autonomously versus decisions that need to be human aided?
Thanks for your perspective Ennis. Having been a student of a semester-long GA course on iOS app development, I can attest to the importance of bringing outside contributors in to help define the curriculum. My teacher was a current Uber product manager and my TA was a current Rent the Runway app developer, which made the classroom environment incredibly engaging as they were bringing their real world experience into the curriculum. One challenge most employers would have in insourcing this is that the curriculum becomes stale very quickly, particularly in the fields that GA and their competitors are teaching. The great benefit of having a teacher who worked at Uber was that he continuously updated the curriculum, making daily changes and requiring us to upgrade to the latest OS whenever it was launched. This real time evolution of curriculum is incredibly difficult for employers and makes the model fairly sticky.
Really interesting, Allison! My perception of 3D printing to date has been that it is primarily used for prototyping. Its great to see how far Under Armour has progressed from that stage through strategic partnerships and a focus on innovation. As you mention, I agree there is a huge opportunity to increase customization for customers based on their unique measurements. Similarly, I see opportunities for an almost infinite number of potential order formulations and combinations, given that the variable cost of customization is almost zero with this kind of technology. I will be curious to see as well if Under Armour takes more of this capability in house or continues to partner with companies like EOS. This is something a lot of these companies are grappling with at the moment and I imagine the next few years will be instructive for the industry.
This is really interesting, Sam. As a frequent and enthusiastic passenger of DB, I’m happy to hear that they are continuing to innovate! I also see a lot of parallels here with the challenges the MTA is facing in NYC. For the MTA, the largest issue is one of the same ones that you mentioned: procurement of obsolete parts. As one of the oldest systems in the world, the NYC Subway suffers daily from the consequences of having been a first mover. No other system in the world still uses the parts that the MTA relies on, driving up both the cost of the parts and the cost of installation. I see a huge amount of opportunity for them to also use additive manufacturing to address some of these issues.
As to your questions, I see Yury’s point that it could be beneficial to focus on the low volume parts for in house production. Doing so, however, raises the question of whether the fixed costs of launching such a program in house wouldn’t then just merit producing a greater number of items in house. Given the size of the German market and the rail network, I would assume that parts could be transported fairly quickly around Germany to the various hubs where trains are serviced, and would probably lean more on the side of continuing to outsource this functionality, pending cost difference of course.