1Mby1M AI Investor Forum: Anupam Rastogi, Managing Partner at Emergent Ventures (Part 2)

Sramana Mitra: So, when you look at these companies that you have invested in and are looking at your deal flow, could you summarize a bit of what you would like to see?

When I look at technology that is emerging and all the trends, and I’m sure all the investors are looking at things in this way, do you think, “Oh, I would love to see a venture like this or solving this problem.”

Has anything come up? Has anything been triggered in your head, “I haven’t seen this yet, but I would like to see a venture solving this problem.” You have principles of both deep learning as well as generative AI. Right now, the newest one is this cost efficient LLMs.  

Anupam Rastogi: For sure. I’ll just mention them more at the thematic level that we look at, rather than specific ideas.

One is, we are seeing AI go a lot deeper into engineering frontiers and a lot of areas like AI plus robotics. We just invested in a company in that space. We are currently actively looking at spaces like biopharma, drug discovery, clinical research and others, and what AI can do there, because there’s a lot of these areas with tons of data, lot of spreadsheets, lot of back and forth. Uh, so a lot of room for efficiency with AI.

We also invested recently in a company in the engineering simulation space. So, we’re seeing a lot of these very core engineering type disciplines, which have a lot of data, a lot of prior art, and a lot of people pushing regulations. In some cases with Generative AI, you can just architect an entirely new flow, which increases a lot of efficiency. In the case of drug discovery, it really accelerates that process. Or even semiconductor design or new material discovery.

 So, we’re taking an active look at those spaces and meeting a lot of founders. We think there’s a lot of opportunities, a lot of big companies that will get created in those spaces.

Sramana Mitra: We’ve done quite a deep dive into Drug Discovery over the last few months. We’ve done discussions like this, and we’ve also covered some of the drug discovery AI companies. This was before DeepSeek came into the consciousness of the industry.

One of the observations that came out of that is that the platforms on which you can do drug discovery research are expensive platforms. These AI companies or startups that are going into the space are catering to the large pharmaceutical companies.

So, if you look at drug discovery, should it only happen inside of the large pharma companies or should it become democratized? There are a lot of scientists out there who have worked with large companies, large pharma, academia, labs etc., who possibly have insights. If they had access to an inexpensive platform where they could start running their experiments and modeling their hypothesis, they could potentially come up with drugs at a much larger scale.

You’re probably familiar with Atomwise. It is not available for these individual researchers or small teams to experiment with. So now that we have this whole vista opening up of inexpensive AI models, the platform for drug discovery that can be democratized is an area where I think there could be very interesting development.

Anupam Rastogi: Yes, I think conceptually that makes sense. I think, there were certain things in the past with just given all of the friction and regulations and all the needs for trials. That just raised the bar significantly and the size of company that needs to be there. But as some of those things come down, there shouldn’t be a reason why smaller teams cannot start doing that. We’re definitely looking forward to more on that front.

Sramana Mitra: That’s the general logic of these platforms being very expensive in other domains as well, where these can come down and a lot of people can research and come up.

You brought up the material sciences aspect. New materials is definitely an area that has similar dynamics where if there are platforms on which material science research can be done at much bigger scales. Or chemistry, research can be done at a much bigger scale with proper domain specific modeling infrastructure. Those are interesting opportunities for building platform companies.

Once those platform companies come into being, there will be a lot of opportunities of building application companies as well on top of that platform as a service infrastructure that is yet to fully kick in gear.

Anupam Rastogi: Absolutely.

This segment is part 2 in the series : 1Mby1M AI Investor Forum: Anupam Rastogi, Managing Partner at Emergent Ventures
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