Aubrey Graham's Profile
Your opening comments are striking – true that 3D printing has been around for years, but technology improvements and falling material costs have made the economics much more viable in the past decade. I agree that highly custom applications could present the most value gain from 3D printing (this example reminded me of many 3D-printed earbuds/headphones I had seen). I also think speed-to consumer and decentralizing manufacturing are great value adds, to your point on improving access to hearing aids in developing markets. I could imagine a philanthropic “Doctors without Borders” model with an ENT traveling with a 3D printer to diagnose and provide hearing aids in remote areas difficult for today’s distribution system to reach.
The “democratization of information” idea reminded me a lot of the mission statement of organizations like Wikipedia and Google. I’m hesitant to take that further and democratize academic research for the arguments you’ve laid out – there is an industry that revolves around publications that uphold certain standards. Academics are also judged on the citations a paper receives, and open information would disrupt the established system. I think there is something to be said in the arena of public health, and perhaps journals related to the field ought to lead the way in opening access to test the impact of doing so on academia and innovation.
One thing I find concerning is the changing consumer preferences in fashion that may not align with product design lead times imposed by Betabrand’s crowdsourced method. Even e-commerce consumers typically turn to online retail when they want to purchase something that is already fashionable and they won’t know in advance what a season’s trends will be in order to provide “advance” feedback. The investment from a consumer to provide input to Betabrand then wait for a product design doesn’t align with my concept of millennial buying behavior.
The social and environmental benefit of adopting this technology are striking. To reduce herbicide use by over 90% not only will make food safer, but it will make our soil healthier and reduce waste in the Ag value chain. However I’m curious how this technology has improved yields? I would assume the see & spray can not be 100% accurate, so there may be some “misses” in spraying the pesticide that result in crop death, whereas blanket spraying the entire fields with pesticide would presumably protect all plants and maximize yield? In any case, if the technology is affordable and improves yield, Deere could drive adoption by letting farmers test the technology and see the results.
I’ve thought a lot about the flaws in relying on fundamental (historical) analysis to innovate or predict future consumer behavior. There are some innovations we can look back on (smartphones, the internet, maybe even the printing press!) and find it hard to imagine how any analysis about how things had been done in the past would have been relevant to how that innovation would enable things in the future. If Machine Learning algorithms are only able to make incremental predictions based on observed historical data, they will never be able to “go from zero to one” in creation of a new concept (or in this case, piece/genre of art, like rock music). Can we design machine learning algorithms with the creative capacity of humans?