Very interesting post Saad, you are tying together so many important threads all in one (reuniting families, AI use as a public good, failures of capitalism, VC biases). I am curious about how Dr. Ming optimized to have a low false negative rate, as I imagine she would want to minimize to near zero the instances where the model does not connect a searching family with their loved one erroneously.
My partners mom lives on an island off the coast of Brisbane AU and she is flooded out every year. I’m going to share this company with her! I found how they are partnering with Waze and Public Service Agencies as a means of monetizaiton an interesting an important step to create value across end users. I’m also interested in how asset managers could use this for their climate risk planning as you alluded to.
Very cool concept! I am still hopeful for a day where all trash is single stream (ie everything goes in one bin, no separating recyclables) and then AI image detection is used at waste processing plants to sort out recyclables en masse with high accuracy. It seems to me that Rubicon is getting a lot of value for using AI in place of otherwise “managerial” decisions, like when to send a new truck out, how often to get it repaired, labor demands and schedules.
Very interesting concept and I hope Warehowz does not fall into the cycle that AirBnb has where customers decreasingly found value in the platform. Another question I’m thinking about is the ability for disintermediation, given the level of integration and specialisation possibly required for warehousing.
So interesting! I have come across TGTG before but have not used it since the bags are “mystery” and I have dietary restrictions. One question that comes to mind though is the restaurant’s ability to disintermediate this platform – for instance, where I used to work the local food hall would heavily discount there left over food after the lunch rush was over, and since it was a high traffic area, there sometimes was a second post-lunch lunch rush to get discounted meals. I wonder how TGTG could mitigate this risk
Such a great post as a Vivino user myself! I think this is a great platform since it has such obvious cross side network effects and same side effects for the users. It also is such a globally applicable business I think it’s monetisation through the wine club is applicable everywhere. One question I do have is it’s defensibility and any barriers to entry it has built – just because it is the current platform of choice, will it always be? Or will a competitor find a way to provide even more value to both sides of the platform?
You’re totally right! One of their other use cases is crop insurance and they’ve won AgTech Innovation Award for their tool’s ability to help farmers understand their likely crop yields. Early warning systems is a great idea too and I’d be hopeful to see them expand their customer set to local or stat egovernments.
Thanks for sharing Aditya! I found your blog post as well as the videos incredibly interesting. 3 things came to mind as I was reading:
1) I am curious to know if Asian Paints not only uses historical, seasonal and cyclical buying patterns, but other future looking big data insights such as scraping home-influencer or interior designer profiles in India to best predict what colors will become popular and where.
2) I found especially interesting how the encumbent frequency of distribution (every three hours to every paint store) enabled Asian Paints to have a competitive edge not only in the 1970s but even today as they capitalized on the frequency of turnover into data insights. I’m curious how this sort of application could be extended to other fast moving consumer goods.
3) It appears the market for paints in India are all pre-mixed colors in pre-determined sizes. this differs from the market in the US, in which paint colors and quantities are custom mixed at stores using a small-footprint machine which knows exactly how much of each pigment to use to achieve the desired color. I wonder if this could disrupt Asian Paints model?
I loved learning about this and would love to go to a Sushiro! The integration between the front of house (hosts inputting how many adults and children in the restaurant) and back of house (chefs), without any input by traditional go-betweens (waiters) is cutting edge. Due to it’s benefits in increasing customer satisfaction, decreasing food waste, and increasing quality, I’m curious as to how other food service businesses could implement something similar – perhaps in formats such as buffets or hotel catering or room service. I also appreciate your point about experimenting with new menu items – with this type of data usage, it is always at risk of confirmation bias ( ie customer likely to pick a after b, but really customer wanted d which isn’t on menu), so the fact that they are experimenting with new menu items and relying on the knowledge of chefs and management is especially helpful.
Thank you for sharing this – I had never heard of Flexport before, but it is clear how their model is potentially disruptive to global logistics and export/import companies. I found your point about privacy especially apt, and it makes me wonder about how Flexport’s potential network effects could be hindered by resistance in the industry of benchmarking company a’s performance against others in an open platform. However, this seems to also be the key value driver of Flexport, offering companies’ a list of transit options and their efficiencies. I’m also curios as to how this data can be used for “inventory management” aboard cargo ships and planes, ie there is extra space on ship B departing on a set date, and I wonder if they could monetize this “unused” space by earning a commission by selling this otherwise unused capacity.