1Mby1M Virtual Accelerator Investor Forum: With Ankit Jain of Gradient Ventures (Part 2)

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 founder-market fit. Then, there should be a large market to go and explore the right product-market fit. In the early stages, it’s about founders and founder-market fit. As some of the stuff gets ironed out and as they’re able to show some of the proof points, we start looking for some product-market fit, some amount of success like early customer traction.

One of my mentors from the venture world told me early on, “The best companies tend to improve the quality of the team as they get injected with more capital as they make more progress.” We look at how the size and quality of the team is changing as it goes from year one to year two. Is the quality improving?

Sramana Mitra: You are, in a nutshell, willing to write a million dollar check to a founder-market fit scenario where there is no MVP?

Ankit Jain: Yes, we’ve done this. We’ve done about 15 investments to date. There’s a handful of them that we did at the very earliest of stages where it’s an entrepreneur that has a lot of grit and passion about a certain market. For a handful of companies, we’re helping incubate them at the seed stage. We’re excited to do so. There are other companies that, even at the seeds stage, have early customers. It really depends on the market and on what traction points can be expected.

Sramana Mitra: The reason I’m probing this is, there are lean and fat startups. It seems like in the SaaS world right now, people are expecting a lot being done in a lean startup mode before they’re willing to write checks. Even seed stage investors are looking for product-market fit. Series As look for a million dollar ARR.

If you have to write significant AI software, it could be that you need more time to do that. You need more heavy-duty engineers, which is expensive – certainly more expensive than what you can do in a very lean startup mode. That’s the question that I’m probing. What is your expectation of how far along an entrepreneur should be to be interesting to you?

Ankit Jain: I think it’s a fair question. By no means do we disregard some of the key metrics behind a business, but having been practitioners in the AI field, we understand that you have to sometimes invest a lot of time and sweat equity to get to certain milestones.

As we get to know a company, we really try to understand what the technology requirements are. Are they using, for example, APIs that are available for image recognition or are they fundamentally building new algorithms? If they’re building new algorithms, then we ask questions along the lines of, “So you want to train your own models. Where do you get the training data from?” You can have the best idea on the algorithm side, but if you don’t have access to the right data, you will never make it beyond that. It’s an interesting chicken and egg problem.

The best entrepreneurs tend to have thought of those things. That’s why the founder-market fit becomes so important because, they know where access to data is or how the right strategic partnerships can be forged early on in the business to unlock certain resources that are needed.

As we look at companies, especially in the AI space, one of the things that we recognize very early on is that at the seed stage, it is often very hard to recruit the best AI talent as your first or second employee. It’s not hard for any more complex reason than the risk and reward ratio often doesn’t make sense where the largest companies are paying very well for these positions.

This segment is part 2 in the series : 1Mby1M Virtual Accelerator Investor Forum: With Ankit Jain of Gradient Ventures
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