Sramana Mitra: Since the hype cycle has been so rapid, enterprise and business buyers have made AI a priority now, which was not the case, right? When you started doing H2O, it was not that case. It was much more niche, much more selective, and much more difficult to get the buyer attention, and that
Sramana Mitra: Let me synthesize this point for our audience. In startups that are going the open source route, including non-AI startups, what is great is that developers with insights put something out there in the open source realm and start getting usage. Then, by the time they go out for investment, often there’s a
Jishnu Bhattacharjee, Managing Partner at Nexus Venture Partners, has been investing in AI startups for over a decade. This is an excellent and insightful discussion about his AI investment thesis.
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Sramana Mitra: I’ve been talking to various people about their various experiments and experiences selling enterprise AI software in vertical AI, and I think this issue is coming up. To train an AI model, you need people who really have deep domain knowledge in that workflow, in the kind of data and the kind of
Sramana Mitra: Interesting. Let’s just follow through on the same case study of Dili. What is the adoption and what is the experience of the company in gaining adoption around this particular use case?
Sramana Mitra: Can we talk about a few use cases of the kinds of companies you’ve invested in with that thesis? Sailesh Ramakrishnan: Absolutely. I can point to one of each. In the tooling area, we are investors in a company called TALC. TALC tries to solve the problem of how you assess the quality
Sramana Mitra: Very interesting. All right, so now I’m gonna switch back to the more usual way I do the AI investment thesis discussion, which is to ask you about your firm’s perspective about AI investments and how are you thinking about it? What is your investment thesis in AI?