This was an interesting and informative read! Thank you for providing color to the vending machine industry, and sharing how new technology has the potential to impact an unchanging, and (at least by me) overlooked object. In my mind, vending machines are simply part of the backdrop of everyday life, and before this article, I had never thought of them in the context of machine learning, open innovation, or 3-d printing. But, precisely because the vending machine is so quotidien and has gone unchanged for so long, are there likely many yet-to-be-thought-of uses and applications of new technologies. The most advanced tech I’ve ever seen on vending machines is a credit-card reader. I’m curious to know more about the laws surrounding vending machines. If they are present in public spaces, does one require a permit?
Your footnotes are a delight– such varied & compelling sources.
Thank you for this in-depth look at Chanel’s new product and their approach to 3-D printing!
It is clear that they were able to leverage the advantages of 3-D printing throughout the R&D process to iterate quickly. I am also impressed they are ready to manufacture at least 1 million brushes per month, with only 6 machines! Scale is a notorious drawback of most 3-D printing applications. However, I am not convinced that the product has been especially advantaged by these iterations and prototypes, knowing that their intent is to revolutionize the mascara stick. Has Chanel proven that their 3-D printed wand is superior to the traditional? If they see significant cost-savings with the avoidance of traditional metal molds, and the material waste implied by injection molding, then perhaps it does not matter!
I can’t feel anything but respect for Jeff Bezos after reading this: crowd-sourcing content for AmazonStudios is an attempt to democratize an industry that has been, since its beginning, based on insane power imbalances. Why couldn’t one of us come up with something better than the same, recycled story Hollywood executives share?
Since Amazon has now tweaked its content creation model, and I think what they discovered is that there is not necessarily a business case for their initial approach. Ultimately, it may be more cost-effective to allocate less money on staff who read endless contest submissions, and spend more time and money on producing titles that have a higher guarantee of viewership. They are finding new ways to work with experienced talent, and although an initial concept, or kernel of an idea, from the outside Hollywood might make for an extraordinary movie, I would imagine that fleshing out a script in full, and overseeing a complete film production process, is best left to industry veterans. However, Amazon is still flexing its muscles in Hollywood, and showing that the entrenched, historical victors are not necessarily the best in the game! #2goldenglobes #3oscars #5emmys
I am not sure how easy it would be for Frito-Lay to crowdsource complete recipes for flavor development since this is an expertise informed by food science and food chemistry, a field not accessible to many. However, I love the idea of Frito-Lay opening up its labs and production equipment for curious people to tinker and join the development process. I think an inherent challenge in the entire food R&D process, however, is gauging customers’ real appetite for the product. You mention it yourself, that “it appears FritoLay is mostly using the competition as a marketing stunt as most of the past winning flavors have been discontinued.” How many people will actually find your product, interesting, delightful, and nutritious (or just amazing) enough to eat and buy again and again? FritoLay uses votes amassed on the internet as a proxy for the desirability of a food product. In reality…people have a wide variety of tastes and preferences within the snack food category, and even today it’s hard to discern what kind of product will be successful, or to read how taste buds en masse are evolving.
I am curious how the technology companies are gaining access to data in the construction industry. I wonder if there might ever emerge a third party within the construction industry, that can offer ML-enabled services to construction companies and replace tasks they already undertake. My hypothesis is it might be easier within the fragmented construction industry for a third party to be trusted with a company’s data over a competitor firm. However, this third party would have to find a way to deliver immediate value while it connects with many firms and amasses data, in order to eventually offer meaningful, ML-informed construction schedules or project estimates. Another alternative scenario would be for a new, ML-enabled construction company to emerge from the ground-up. If a new company came on the scene with this vision and the necessary talent, they could, over a long time horizon, find ways to make their business processes more efficient than a business not bothering to incorporate ML into their business processes.
Based on inherent barriers in this fragmented industry, it might be a while before we see ML have an impact in construction!
In answer your final question, I argue that no, there will never be a time when the role of the VC is fully automated. Particularly in the earliest stages of a venture, investors put capital into the hands of capable entrepreneurs as an expression of their belief that this person possesses the experience and personal qualities that will allow them to achieve an extraordinary outcome. At the seed stage and even Series A, decisions are not necessarily mathematically-based, but they are people-based. The questions a VC asks in final pitch meetings may not even be pre-planned–the VC reads into the body language and passion exuded by an entrepreneur to navigate the conversation. The information extracted from this encounter is not prescriptive, even if it may be guided by a VC’s own mental models. While you could argue that technology might evolve to the point where it can perform the exact functions of 1. reading nonverbal cues and 2. know the metrics associated with entrepreneurial grit & passion, part of an investor’s job is to form opinions about the future based on their past experiences (as past founders or developers of technology). A healthy VC ecosystem requires investors with a wide variety of experiences (of both successes and failures) that allows them to read into certain business and human capital contexts with their own judgment.
I absolutely agree that to be an effective tool, Preseries will need to incorporate (find a meaningful way to quantify) qualitative data points. I argue however that at the earliest stages of venture, the qualitative outweighs the quantitative to the point that an AI-based investment recommendation would not be useful.