Wow, definitely one of the more popular articles here, Ankur! Good job 🙂 Crowd sourcing content from users seems to be great idea because in a way, you are trying to learn what users want you to make. Creative talents can also come from everywhere, not just your in-house creative writers, so casting a wide net can help you increase the chances of getting the next big hits. However, I wonder in the world of Netflix, Hulu, and yes Amazon itself, those content/tech companies are already using data to learn what their viewers want, and/or predict what is going to work and what is not. Perhaps that is more cost and time efficient method to get closer to viewers.
A common concern I have seen in a number of articles on additive manufacturing, including this one, is cost. In this case, while it is true that the cost to procure physical objects from overseas is prohibitive, per unit cost is still very high, and as far as I understand, because of the customization, there may not be as much economies of scale. For a poor country like Rwanda, it is a tall order to ask the government to pour resources into 3D printing, but I wonder if other well-funded nonprofit organizations (for example the Bill and Melinda Gates Foundation) can see the potential in this technology and thus be willing to put more resources into it. Another possible and more sustainable solution is to invest in tech companies that are actively developing it (with the hope that better technology can bring down the unit cost).
Thanks for sharing the article! It is very interesting. I didn’t know that there have been very practical applications of additive manufacturing as in the case of the Ministry of Supply. To answer your question whether Adidas will be able to bring down the cost of the 3D-printed products, I think that this will largely be dependent on the progress of the 3D technology, which is still in a very early stage. Other tech companies, not Adidas, are developing the technology, so if Adidas truly believes that the customization through 3D can become one of its core competitive advantages, it doesn’t seem farfetched to suggest investing in one of those tech companies to have the proprietary technology. In fact, many companies (outside of tech) have their own venture/incubator fund to invest in small but promising emerging tech companies!
I tend to agree with “Probably Alex” (LOL) in the above comment. I believe in the potential of machine learning and artificial intelligence but I also think that they have been overused as “buzzwords” in fashion and retail, just as Gucci can be a fad for those millennial shoppers. Data will be extremely influential in customer engagement (upselling new products, engaging churned customers etc), but data is also very backward looking. It helps to see what customers are ALREADY searching for, which means that those trends already exists. Customers can’t search for things that they don’t know that exists (sometimes they don’t even know what they want), so data may be not very useful to help guide trends. However, one caveat is that fashion doesn’t exist in its own isolation. It is impacted by pop culture and societal changes, usually with a lag. One example is the rise of streetwear in recent years, which can be explained by the popularity of hiphop that happened many years ago. Therefore, using machine learning and artificial intelligence, creative directors can detect and predict what pop culture trends are going to emerge and influence consumers. Those trends can help guide them with the actual creative process of fashion.
I think that this is a very important topic, especially in light of the growing tremendous influence social media is having on our society nowadays. I definitely think AI and machine learning can have a lot of potential in identifying “bad” content, making the process more efficient, and hence saving costs for technology companies. However, I have some doubt regarding the “efficacy” of AI when it comes to language, especially in long-form content. Language is arguably one of the hardest tasks for a machine to master, because it needs to not only understand the vocabulary, but also to able to put into context, get the nuances, the puns, the emotions conveyed through texts, stuff that are hard to be built into an algorithm. Hate speech nowadays can be extremely subtle. Also as social media today is extremely global, the technology or “AI” built to tackle this issue also needs to be global, i.e can master the subtleties of many different languages, which to me seems to be a very daunting task.