Jon Snow's Profile
I wrote about the impact of AM on the footwear / apparel industry, and it’s interesting to see the similarities and differences in terms of cost / benefit across industries. The benefits are similar – rapid prototyping, cost savings through additive rather than subtractive processes, weight reduction for improved performance, etc. However, there is a lot more to lose in aerospace. As you mentioned, there are questions around the long term reliability of these materials, which is less of an issue in retail. It’s interesting to think about the variety of applications and the decision points for these firms, as you’ve outlined so well at the end of the article.
As someone who is unfamiliar with the blockchain (and related terms like smart contract, etc.), I found this digestible and interesting. I can see why people give credence to the idea that these open platforms are “open, but not open” because there is a centralized party that has control of the network and facilitates the meeting of buyer and seller. The decentralized, open network gives us the ability to remove this party, but I wonder if that would be a net benefit. Without fully knowing how a smart contract works, I see these companies like Handy, Lyft, etc. adding real value to these service-based industries from a trust perspective. Their legal accountability, internal protocols, service ratings, etc. all add value to the buyer / seller transaction, and I wonder how the decentralized platform solves for that.
This is a really interesting example of impact investing – we’re seeing more and more companies / funds prioritize social impact over financial returns (which I actually think in many cases will lead to longer term financial success). As you mention, there’s a tension with DDF’s concentrated bet – obviously there is diversification risk, but at the same time, their singular focus on Dementia allows them to become world-class, potentially increasing access to more philanthropic capital. I’d be interested to see if this model gets replicated for other types of disease.
I found this extremely interesting as it’s something that is frequently discussed when we talk about the next wave of technology. I never had a good understanding of how autonomous vehicles would work, but after learning about machine learning and reading through this article, it makes sense. The dependency on the cloud, where Tesla is constantly collecting data from all of its driverless cars, correcting for error and enforcing success, is what seemingly driving the tech forward. With most applications of machine learning, there is this virtuous cycle, where you get more and more cars on the road, get more data, those cars learn and drive more effectively, and thus demand for autonomous vehicles goes up. The Trolley Problem is a tough one, but I think this is where we can actually see a benefit of autonomous vehicles (albeit a cold-blooded benefit perhaps) – humans might not be able to make the rational decision, but these cars will.
I thought this article was really interesting, particularly because of how relatable it is. As a Spotify user myself, I frequently use “Discover Weekly”, “Release Radar”, etc. because I actually do find that Spotify’s personalized, curated playlists appeal to my music taste. I’ve never really thought about the science and implications though. As you mention, Spotify has created this virtuous cycle, where they use technology / machine learning to curate these playlists, users like them so more users join the platform, Spotify has access to more and more data, and can then create even more personalized playlists. I’m interested to see how far this personalization can go – as you mention, will we have playlists pop up based on what we’re currently doing? Or how we’re feeling (without telling Spotify)? It’s a cool application of machine learning and one to keep an eye on!