Sramana Mitra: If I were to synthesize what I heard from you, your relationships are more of the corporates who are either co-investing with you or are becoming early customers of your portfolio companies, and not necessarily with the acquirers. The acquirers are coming as you go along and develop the businesses.
Evangelos Simoudis: When we select corporate partners, it is important to identify companies that are thought leaders in the space and in the industries that we want to work in. We feel that it’s through these thought leaders that we will find the hard and important problems that need to be solved. We don’t invest in companies that try to solve every single problem we hear about.
We hear from our partners about problems where data can play a role. Then we have to apply our own filters. We use our advisory board. We use our other networks to identify the subset of those problems that have the potential to have big impact. You want to invest in startups that solve problems that are big. I’ll give you another example.
We recently invested in a company called Humanizing Autonomy out of the UK. Because we don’t take Board seats, we look at investments globally. We have investments in Israel, Germany, and UK. In this particular case when we started looking at the company, the problem that they were trying to address is predicting pedestrian behavior. That’s an important problem in automated vehicles.
It turns out it’s also an important problem to other forms of transportation like buses, light rail systems, and airplanes. The idea of how a pedestrian behaves, whether they’re boarding an airplane or boarding a bus are big problems. Where initially we thought that the market will become this much, by the time we finished our diligence, the market was larger.
Sramana Mitra: It’s also interesting that you said that you’re investing globally. That’s completely consistent with what we are doing. That’s a good fit. I will ask you one last question. What trends are you seeing? Specifically in the AI deal flow and the kinds of verticals that you are dealing with, what are the trends?
Evangelos Simoudis: I would say that to a certain extent, it is all over the map. We continue to see significant interest in next-generation mobility technology. It’s not only about creating autonomous vehicles but also monetizing that. In fact, autonomy monetization is one of the investment thesis that we are actively pursuing. We’ve made one investment so far in a company and we’re looking at additional opportunities.
As we get into these larger-scale datasets and more applications of automation, we’re starting to see a variety of robotic applications for several industries such as the harder AI problems around natural language processing and natural language understanding. The main reason we invested in Humanizing Autonomy is because in the process of solving this problem, they really have to solve a hard computer vision problem. If they solve it, then that’s going to be a big reward. It’s also a big-risk opportunity.
We continue to see companies that want to automate the entire predictive model creation process, which is becoming harder and harder as companies are realizing that there aren’t as many data scientists as they would want to hire. As the datasets are becoming larger and more complex, automating parts of that becomes complex.
We’ve invested, so far, in two companies. We’ve invested in Paxata and Ikasi. They’re both in Silicon Valley. We’re looking at additional opportunities. Finally, cyber security remains a global problem. Some aspects are domain-dependent. We talk about autonomous vehicles. They will need to be protected. We invested in a company called Namogoo out of Israel. They’re using big data to address specific aspects of security. There’s a lot of noise in that space.
Sramana Mitra: Great. It was a very interesting conversation. We are hearing AI in every corner of our universe. Thank you for your time.
This segment is part 5 in the series : 1Mby1M Virtual Accelerator Investor Forum: With Evangelos Simoudis of Synapse Partners
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