That is a great point jrod and not one I had considered. It is definitely an issue with crowd-sourcing in general that the data you collect is probably representative of your current customer only and that in some cases may preclude you from attracting a different type of customer. I also agree with others here that the platform is super important when considering the type of language “rules” Grammarly should use. One way to mitigate the issue you bring up is by being very focused on attracting a diversity of applications/platforms. My guess is that the variation in how different demographics text is much lower than the overall variation in the type of language different demographics use. Thus filtering and controlling for platform context might help with this issue.
Awesome article Matt. I am very interested in the power of prediction markets and especially interested about which environments they fail in. I think the idea of crowd-sourcing such large-scale issues such as terrorism can be very tricky because the awareness of the prediction market itself can cause people to change their interpretation of reality. So for example, if there were some public mechanism in which people could report their beliefs about a potential terror attack, my guess is that if those results were public that would actually affect people’s perceptions about the likelihood of an attack. So you sometimes get weird effects like that. One thing that I think would be really interesting is an internal prediction market within the CIA or broader intelligence agency network. I think Google and many other tech firms do similar things with their employees and they incentivize it by reporting on who is most accurate in their predictions.
Very interesting article. One tension that I see in the world of 3D printing is that you are caught between mass-production, low margin products, and traditional, high margin artisinal products. With 3D printing you have relatively high manufacturing costs for very little in the way of quality improvement over a mass-produced item. As mentioned the competitive advantage is the speed in which they can react to changing consumer tastes. In other words they are in the fast fashion business. But I think this is a risky space to play in because unlike a large fast fashion firm they are relying on individual and independent designers who might not be able to take on the volatility of having their designs only sometimes be making money. As an independent designer I would prefer to get a guaranteed long-term contract with a traditional company or to go the high-margin route with more high-quality artisinal products (e.g. Etsy).
Very interesting article. I think you are exactly right about the long term risk of playing in a space that is so dominated by tech firms with higher margins and R&D budgets than in the shipping industry. I can easily see a future in which shipping companies invest heavily in AI and then Amazon simply goes to a different shipping company and offers to improve their operations by integrating their own (Amazon’s) AI. In this relationship Amazon would provide the “competitive advantage” to the shipping company via their AI. And the shipping company would simply hold all of the large assets (e.g. trucks) and thus most of the risk. I think this is an inherent problem with investing in AI right now. It is very unlikely that AI developed right now will be a sustainable advantage over the long run, especially for non-technical firms.
Very interesting article. I have experienced this trend as well in my previous work. Our relationship with our IT service vendor mostly encompassed traditional enterprise application integration. But towards the end of my tenure we saw a shift in this relationship as our vendor increasingly pushed its data analytics and resource planning services, both backed by AI. I definitely agree that the future of these firms will be in analytics and AI. However I do think it will be hard for companies like Zensar to be competitive in this space given how rapidly the space is evolving. I think the value a firm like Zensar can offer is more in the nuances of implementation than in the actual development of AI. Thus I think focusing on niche cases might be a sustainable way to grow in the space for now.