Episode Summary: Companies looking to raise money are often asking what investors think of their company, their industry, and how they’re making investment decisions in related companies. In this episode, I ask these questions of Robert Seidl, who is managing partner of Motus Ventures, an investment firm focusing funding early-stage software companies for autonomous Vehicles and the IoT. Seidl talks about various data sources and the people and networks from which investors draw information when they don’t have what they need on-hand, but need to make important investment decisions. He also shares his perspective on the high-energy and competitive investment world of AI, including his thoughts on the most exciting (and confusing) areas in the industry today.
Expertise: Venture Capital & Private Equity
Brief Bio: Prior to coming to Motus Ventures, Robert Seidel started a number of software companies, held engineering as well as marketing and executive leadership roles, and worked in Silicon Valley companies like Apple and Adobe following acquisitions. Robert has an engineering background, but has always focused on creating user-friendly products. In 1995, he founded the company that built the first commercial dedicated web page editor and web site management tool: PageMill (which was acquired by Adobe and generated over $20 million in sales in its first year). In 2000, Metacreations Inc. acquired his next company, Canoma, whose technology allowed rapid creation of photorealistic, textured 3D models from images. Since 2002, he has managed Realtime Video Systems, a technology licensing company that provides US drone vendors with video processing and object tracking software. He also cofounded a MDV/Accel/Emergence/Walden funded startup called Genius.com, which provides sales and marketing people at B2B companies with easy-to-use, real-time lead generation and nurturing tools.
Current Affiliations: Managing Partner of Motus Ventures; Transportation Advisory Board Member for ProspectSV
(1:31) Around the topic of investing in domains that are not necessarily your academic expertise…how do you sort of get the empirical backing to make those decisions without that being an academic focus, what do you have to as an investor?
(6:06) What are those other grounders in reality that help you make educated decisions in these fast-moving domains? Is it also talking to the bigger players in this field…what are those other activities that you have to undertake to stay abreast of these fields the you’re focused on?
(14:21) Do you work with some degree of a formal stable or is it just personal contacts that you pull in during certain decisions, there might be times where you say, “Man, if we’re going to make the call of how much to invest in X company, then we should have someone who knows ‘this thing’”…how do you plug in the people or the knowledge you need at these critical decision points?
(18:42) You’ve got the right bigger industry folks in play who have the broader connections…rather than a coffee and a favor level of willingness to do so, they’ve really committed themselves to being part of the company…I imagine that’s a pretty important decision for you, who those people are going to be, and I imagine if there are companies who are interesting in pitching to you, they should look at that board…
(21:17) When you look around the investment world…what do you think the investment sentiment is around machine learning and AI…and how have you seen it changed?
(24:11) It sounds like the demand is even higher than the supply…from a perspective of an analyst, there’s still so many places to get snapped up if you’re an AI company, and in some way that makes your job more challenging because you have to assess such a fast-moving technology in a very frothy competitive field…do you feel like other investors feel the same way?