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Loved the article – thanks, Jaclyn! While I think that Lego has correctly identified the direction it needs to move in to keep up with the digital age and has used open innovation to produce products with known consumer demand, I also think that Lego, with it’s immense brand equity, is uniquely positioned to do something really disruptive in the toy industry. I like your idea of collaborating with the other parts of the supply chain to generate more ideas, and I think that relying on consumer feedback heavily for idea generation can prevent truly novel innovation.

Another random thought – with its digital focus, Lego has the opportunity to make its product a lot more collaborative in nature. I can envision a digital platform that allows children to team up with other children across the world in designing and building anything from a rollercoaster park to a fortress and gamifying the combined results. While I think there’s the very valid concern of Legos being the antithesis of video games, I believe Lego has the ability to add a hands on, tangible aspect to gaming in its quest to build connected toys.

On November 15, 2018, JT2020 commented on The Regulatory Challenge of Autonomous Vehicles :

Great article – thanks for writing this! I fully agree on your safety point – safety and human life should never be put in jeopardy for the sake of innovation, and I believe establishing a concrete legal framework for AVs will encourage companies to emphasize safety to mitigate the (what should be outsized) costs of accidents / harm. While I think AVs can provide huge value to society, I’m skeptical that they can replace vehicles in major cities so long as their is human negligence and irrational driving behavior alongside the AVs.

On your communications system point – this is super interesting because I see why the ability for AVs / companies to communicate would alleviate many potential risks and concerns, but I also worry about cybersecurity risks. If you have AVs in a human-trafficked location that are all connected through a centrally-controlled system, a hacker now has a new weapon to inflict large scale physical harm.

On November 15, 2018, JT2020 commented on Prellis Biologics: Manufacturing Life :

Loved this article, Emma – thank you for sharing! I echo a lot of the comments above that mention trials on consumers with very high demand / who are unlikely to otherwise receive a kidney in time. I’m certainly not a medical expert, but I wonder about how insurers and doctors would view 3D printed organs given the industry’s nascent stage and the lack of knowledge on long-term risks. If something goes wrong with the organ or if there are serious side effects, who shoulders the legal responsibility? I can see hesitation from insurers to cover any of the cost and hesitation from doctors to recommend a 3D printed organ, at least until the long-term risks are clear, which would likely slow down the entire adoption process.

On November 15, 2018, JT2020 commented on Printer-to-Table: The Next Food Movement? :

Thank you for this – I found it really interesting! I’m surprised that management is currently targeting more high-end restaurants: this is mentioned in a comment above, but I have a difficult time believing that high-end chefs would be okay with using this in their dishes, and if they did I’m sure they would conceal the fact to the diners (which would defeat the purpose of creating consumer awareness / familiarity with eating 3D printed foods). I also worry about the lead times / set-up times necessary to produce something in these machines, since at a restaurant you’re not going to have full visibility into how many times a certain dish will be ordered in a given night. Seems like something that would be most useful in large cafeterias, food-service businesses, or catering businesses where they know demand ahead of time and are producing very large quantities of the same dish.

I actually really like the ‘breakfast bar printing’ idea. More broadly, it would be interesting if you could choose 50 different foods you really like, input your taste preferences and your nutritional needs, and the machine would produce meals or supplements for you with the exact right mix of nutrients. It would be a vitamin on steroids.

This was super interesting – thank you! I very much agree with your critical considerations, but on the privacy point I’m inclined to think that the success of direct-to-consumer health diagnostic tools, such as 23andme, suggests a willingness for patients to provide data in exchange for better / more preventative health outcomes. One concern I have is the extent to which medical providers will use this data to drive care decisions and resource allocation – as we’ve learned, machine learning can create blind spots based on its training data (which you allude to in your second consideration), so could you end up neglecting the anomalous, very dangerous / costly cases that go less noticed because resources are focused on other patients Cyft points to? Additionally, I wonder if Cyft could partner with more tangible medical tracking devices (even something like FitBit / Apple Watch) to gain data and become more visible to the consumer, so consumers could also actively learn to care more about tracking and understanding their health.

Thanks for this well-researched read! While I think Nordstrom has stayed ahead of the curve in terms of the customer experience (omni-channel integration and personalized service / recommendations as you described), I struggle to see how other retailers (like Amazon) and brands themselves can’t replicate this with enough capital and data. I would suggest that Nordstrom use machine learning to drive more private-label brands which are not only significantly higher margin but also defensible against Amazon if you can create enough brand equity around individual brands. While they already have a number of successes in this department (incl. Zella, their activewear brand, and more recent influencer-collaboration brands like Something Navy), I think they have room to further penetrate vs. third-party brands.

On other initiatives, I like your idea on leveraging machine learning to reduce in-store costs. I wonder if in addition to predicting store traffic and employee scheduling they’re also using ML to better predict what type of inventory to carry at which stores. I imagine that stores already merchandise to specific locations (i.e., carry more down outerwear in Maine vs. Florida), but it seems like they have the opportunity to be more precise with carrying exactly what they know consumers are looking for and when.