Sramana Mitra: I want to comment a little bit on your personalized experience theme. I did one of the first ever online personalized fashion companies 20 years ago. The data was not quite there. Now, that’s something that a lot of people are working on.
This is a category of products that require quite a bit of technology development to work. The question is a bit of a subtle question. You want to come in after there is some validation.
Is there a group of investors you are working with who are willing to do the earlier part of this kind of business? This is not something you can do on the back of a napkin.
Nnamdi Okike: It’s something that we struggle with. When we find an exceptional founding team that may not have a prototype, we debate internally if we should jump in earlier. I think we’ll do that a bit more especially with founding teams where we have a lot of conviction around their ability to build.
More generally, we do partner with different types of investors. We are, oftentimes, partnering with pre-seed funds. There are several pre-seed funds that we really admire. As an example, Charles Hudson at Precursor Ventures is very good in identifying top founders who are looking for that first check.
There’s Afore Capital. After Afore did the pre-seed, we invested in the next round. They’re a great fund. We do look for partners who are a little bit earlier than us and can provide that first check. We also work with angel investors as well who can provide that capital.
Over time as you described, we may be a little opportunistic there. Where we see an exceptional team in a big market, we may write a smaller check ourselves and start to work with the founders, and later we may be able to write a bigger check as they get to the larger institutional seed rounds.
Sramana Mitra: These are not quite as lean startups as some of the other categories. Personalization is heavy-duty technology. You need a technical founder who can access the data on which to run the personalization engine. They need to do business development potentially to get access to the data. It’s a lot more complex.
The ecosystem has scaled building startup models very nicely. We are seeing great bootstrapped startups come out. There is a case to be made about building a bit more of an infrastructure to do fat startups to solve some of the problems that we’re talking about.
Nnamdi Okike: You’re right. These companies need advanced technical teams. They need technical founders. They also need early capital to be able to test things out and get the algorithms correct and have a large enough training dataset to provide a product that serves the work.
Sramana Mitra: It was a very nice conversation. I love the way you have articulated the three areas that you are interested in. Congratulations on that. Thank you for your time.