Greg Andrews's Profile

The intellectual property question is an interesting one for ultimately judging if this program has legs. As a comment from Lindsey above points out, structured protections for valuable IP are important for accessing the scientific and academic communities. There is industry precedent for collaboration between pharma companies and academic researchers, but many of these instances include co-ownership and royalties, providing much more future upside to the researchers. Naturally, the Lilly program seems like a potential boon to the Company given “cash prizes” are likely a relative low cost of product acquisition, though it comes with a shorter term outlay. Will researchers bite?

This is an interesting look at the potential future of space commercialization. I wonder about 2 areas for future development. One is the future of the materials. Does the introduction of 3D printing allow for the use of new, more innovative materials that might be lighter or more heat resistant and could then be printed into rocket parts, beyond just the engine? And the second is how the public sector will play a part in the development in space. Given it is not just a US issue, how will international bodies react in the coming years to the quick ramp in satellite traffic?

While one could justly question Tyson for being a little behind the AI 8-ball, NotCo’s approach here seems very interesting. By relying on existing data sets for plant proteins, they can use the algorithm to essentially run simulations of recipes and test for taste, texture, nutritional content, etc. This could be a very inexpensive way to probe for new protein alternatives. I would think a conglomerate like Tyson could either purchase recipes from companies like NotCo (similar to a pharma model) and become a mass producer or try to take this development in house.

This brings up some interesting debates about how a retailer should consider “customer loyalty” as opposed to customer value. For example, how should ASOS evaluate a customer that is a loyal buyer but also more likely to try and return items? They provide value in terms of consistent sales, but if half the items in each purchase get returned, the net value to ASOS could be quite mixed. Does this behavior call for a different application of machine learning? Or does showing a customer a webpage with only the items the customer is likely not to return lessen the impact or engagement that customer has with the brand experience?

This reflection on the future viability of 3D printing for Nike touches on the key balance between R&D time and performance. I would wonder how the thermoplastic polyurethane material, though surely with manufacturing time benefits, performs relative to Nike’s other materials. Nike has generally not shied away from longer product development times given their central focus on providing their elite athletes with the best possible product. If there is a performance gap between the TPU and other materials, it should be interesting to see how Nike treats this trade off in the medium term while further investment in the technology progressively shrinks the gap.

This seems like a very interesting potential solution for a large brewer like Carlsberg managing significant inventory and waste costs. I do wonder about the efficacy of a machine-learning solution being able to create new flavors that match the varying tastes of the day, such as bitter IPAs of the US West Coast or the more hazy, floral IPAs of the Northeast. Does a machine solution just tend toward the mean, thereby creating a relatively forgettable taste experience?