Sramana Mitra: In the case of Algorithmia, what kinds of use cases are they? It sounds like it’s a horizontal platform that could be applied to all sorts of use cases. You said it’s a Fortune 500 target market. What kinds of use cases are they going after?
Ankit Jain: The simplest way to describe Algorithmia is in the context of a company that has hundreds of algorithm developers. There’s a lot of companies that are doing this, especially in banking and finance where different people are trying out different models. That institutional knowledge doesn’t get shared across a company.
Often, in companies that have internal code-sharing infrastructure, everyone publishes their code on GitHub. Let’s say you and I work at >>>
Sramana Mitra: How are the companies in the Valley that you’re investing in? Are they using offshore development centers where there is AI talent available?
Ankit Jain: Even in the last year, it has changed a lot. There is a lot of interesting AI development being done out of Ukraine. Both Pakistan and India have teams that are starting to specialize in AI. At least, one of our startups has a team out of Pakistan that is doing a lot of their AI development.
Sramana Mitra: Interesting. I have never heard that before. Let’s talk about your portfolio companies. Talk about some of the highlights. Especially talk about what stage you get them in. How did you encounter them? What did
Sramana Mitra: I was talking to a friend at a party last weekend. He’s very experienced and successful serial entrepreneur. He has invested in an AI company that is doing very well. But this question of hiring AI talent is very serious right now. Let me ask you the geography question. What is your footprint? Where do you like to invest?
Ankit Jain: Before we get to that, let’s go back to the recruiting point because I think it’s a very important point. Valley folklore and reality has been that the best companies are built with the best people. If it’s okay with you, we can spend a couple of minutes on the recruiting aspect. >>>
Sramana Mitra: Double-click down for me on your definition of early stage. You said check size is from $1 million to $10 million. What is your definition of early stage? What does an AI startup need to show to be able to convince you that is has enough validation that there is something there?
Ankit Jain: That’s a very interesting question. I wish I had a clear answer of, “These are the things that you need to convince any investor that you are fundable.” Every investor has his view on this. We have a few things that we look for. They change by the stage of the company. At the seed stage, we’re looking for a strong core team that we think can execute in a given market, what people would refer to as >>>

Responding to a popular request, we are now sharing transcripts of our investor podcast interviews in this new series. The following interview with Ankit Jain was recorded in May 2018.
Ankit Jain is Founding Partner at Gradient Ventures, Google’s AI venture fund.
Sramana Mitra: Let’s introduce you to our audience. Tell us about yourself a bit and introduce us to Gradient Ventures. What is the focus of the fund? How big is the fund?
Ankit Jain: Gradient Ventures is Google’s AI-focused early-stage venture fund. We invest $1 million to $10 million in companies that >>>
Sramana Mitra: What else is interesting in your structure that is worth discussing that I have not discussed with you yet?
Utsav Somani: I think you’ve pretty much covered everything. There are some really good companies coming out of India.
Sramana Mitra: Can you talk about them?
Utsav Somani: It’s too early to be disclosing some names. I can talk about the first one which is a middleware company that’s doing Blockchain-based API for companies to quickly deploy Blockchain government systems in
Sramana Mitra: What direction does the deal flow? Is it the VC’s who are coming to AngelList, or AngelList is chasing these deals?
Utsav Somani: We are the largest tech-enabled cloud platform in the world. We are more of a product company than a service company. Our product are the syndicate products. You go do the work of finding allocations. We’ve seen large institutions bringing their own set of backers but just to clean up the cap table, they pool to one entity. Then we see operator angels. These are people who can’t write large checks. We’ve seen so many personas.
Sramana Mitra: Which of these personas are dominant in India? >>>
Sramana Mitra: I’m going to double-click down on a bunch of points you made. You said the angel networks like Mumbai Angels work like glorified VC funds versus AngelList. Elaborate and contrast the fee structures.
Utsav Somani: Everyone is structured in a different way. They help different people. The offline angel networks are places where people like to meet companies and see them pitch in person. They take a longer decision cycle. At AngelList syndicates, we enable people to write lower check sizes, but we pool capital and people make faster decisions. Every coin has two sides.
Sramana Mitra: What about the fee structure though? >>>