Investor Discussion Series: Evangelos Simoudis of Synapse Partners

Evangelos Simoudis is the founder of Synapse Partners, a VC firm that focuses on AI and Big Data investing.

How do you really identify and due diligence AI companies, versus companies just doing statistics or have vaporware?

There is a lot of hype in the space about what is possible with the technology available today.  Anytime there is hype in a sector you get pretenders along with the startups that are developing important IP. 

At Synapse we partner with large corporations in automotive and transportation, financial services, and telecommunications. We work with Sr. Executives from corporations in those industries to understand what they view as strategic problems for their companies and we then determine which of these can be addressed via data and AI.

Since Synapse Partners invests exclusively in early-stage startups developing enterprise applications combining big data with AI, my personal background in AI proves to be very helpful when we consider new investment opportunities.  We also tap into our firm’s advisory board that includes senior AI and data scientists.

What kinds of moats do AI companies have from what you’ve seen?  Does it really just come down to the data/all the algos are mostly the same right?

Identifying and gaining access to the right data sets for solving important enterprise problems, selecting the right AI approach to exploit that data, properly preparing the data for processing by the AI system, and finally making sure that the results of this exploitation are correct are the prerequisites for creating such moats.

It is an oversimplification to think that all one needs is data and that more data is always better. The moat is created by the uniqueness of the data and its quality, as well as the ability to exploit  it in a smart way.  It’s a fallacy to think that by taking some open source machine learning software and presenting a large data set means that one can create an important product.  This approach may have been used in the past but the low-hanging fruit has already been plucked. 

For example, properly labeling data before presenting it to machine learning systems turns out to be a difficult and expensive task that today is mostly performed manually. As corporations work with very large data sets, such as those generated by autonomous vehicles, such manual labeling becomes prohibitive. We have invested in a company called Understand.ai that uses AI to automatically annotate that type of data.    

What Trends are you currently investing in?

●      For the past couple of years, we have been investing in companies that develop AI-based software to enable autonomous mobility.  More recently, we developed and currently pursue an investment thesis around using big data and AI to monetize autonomy. For example, we are looking at startups developing fleet management and commerce-related AI systems for passenger transportation and logistics where autonomous vehicles will have an advantage. This includes tasks like scheduling, and maintenance of autonomous vehicles aimed at increasing a vehicle’s uptime.   

●      Intelligent software agents (not chatbots) that operate within larger software or hardware systems, for example warehouse robotics. We are interested in systems that understand natural language so that they can collaborate with humans, and can learn from such interactions.

What’s Overhyped today from an investment standpoint?

AI, autonomous vehicles, blockchain and cryptocurrencies, augmented and virtual reality are all hot areas but are all overhyped right now in terms of their potential impact and the speed with which this impact will occur. It will take us longer than the popular press talks about to really see the impact from these but I remain optimistic that we will have important changes as a result of using these technologies to address enterprise problems. We will continue to see significant pilots and experiments being done by corporations using these emerging technologies.  However, people need to keep in mind there is a big difference between experimenting with a technology and extracting insights and being able to have broad deployments.

On the other hand, I am very optimistic about the accelerating proliferation of cloud computing in the enterprise. But you can see how long it has taken cloud computing applications to permeate the enterprise and for the market to become as large as it is today. 

What’s the key signal or two you look at when thinking you want to invest in an early enterprise startup, what ultimately convinces you?

I start with the team and I like to see how driven they are for the startup. I pay attention to how complementary the team members are, since I consider well-rounded teams to be an important ingredient to a startup’s success. I don’t want to see a team that consists only of engineers.  I like to understand the team’s background and how they got where they are.  Of course, we always pay attention to the market opportunity, which can often be challenging when the startup is trying to address a brand-new market.  Lastly, at Synapse we always syndicate. It is therefore important to understand who are the other investors.

What are some resources you use to stay up to date on a space?

I spend a lot of time reading and working with large corporations. Technology- and startup-related conferences are also important sources of information.  These days I find myself connecting a lot more with PhD and academic colleagues.  The biggest challenge I have is finding good enough filters to discard the overhype of the AI field right now.

Any advice to young venture capitalists and angel investors out there in sourcing deals?

It’s not only about writing checks for new investments; it’s about making money for your investors.  Finding a company that wants your money is the easy part, understanding if this is a good company and important investment opportunity to make you want to be part of the company for the next 5-10 years is something you need to focus on. 

Sourcing these days is very difficult. There are so many startups being created and capital is a non-factor.  Having deal flow today is not hard. Having the right deal flow is the important part.  You get the right deal flow by being able to show to the entrepreneur and your co-investors that you have something unique and important to offer.

Today local networks are not as important as they used to be.  You need to be able to tap into global deal flow and be willing to invest globally even as a small investor. 

Anything else you think AI  investors or enterprise entrepreneurs should know?

AI is a complex field. It includes several different areas.  Not only machine learning.  People don’t have as much of an understanding of AI as they think.