Today I’m chatting with Bob Mason, Managing Partner at Argon Ventures. Bob is a VC who seeks out early-stage founders in the ML/AI space and helps them inform their go-to-market, product, and design strategies. In this episode, Bob reveals what he looks for in early-stage data and intelligence startups who are trying to leverage ML/AI. He goes on to explain why it’s important to identify what your strengths are and what you enjoy doing so you can surround yourself with the right team. Bob also shares valuable insight into how to earn trust with potential customers as an early-stage startup, how design impacts a product’s success, and his strategy for differentiating yourself and creating a valuable product outside of the ubiquitous “platform play.” 

Highlights/ Skip to:

• Bob explains why and how Argon Ventures focuses their investments in intelligent industry companies (00:53)

• “In a former life, I was a software engineer, founder, and CTO myself, so I have to watch myself to not just geek out on the technology itself because the most important element when you’re determining if you want to move forward with investment or not, is this: is there a real problem here to be solved or is this technology in search of a problem?” — Bob Mason (01:51)
  

• “I don’t think the technical folks in an early-stage startup absolve themselves of not being really intimately involved with their go-to-market and who they’re ultimately creating value for.” – Bob Mason (07:07)

• “When we’re making an investment decision, startups don’t generally have any customers, and so we don’t necessarily use the signal of long-term customer adoption as a driver for our initial investment decision. But it’s very much top of mind after investment and as we’re trying to build and bring the first version of the product to market. Being very thoughtful and mindful of sort of customer experience and long-term adoption is absolutely critical.” – Bob Mason (11:23)

• “If you’re a scientist, the way you’re presenting both raw data and sort of summaries of data could be quite different than if you’re working with a business analyst that’s a few years out of college with a liberal arts degree. How you interpret results and then present those results, I think, is actually a very interesting design problem.” – Bob Mason (18:40)

• “I think initially, a lot of early AI startups just kind of assumed that customers would be patient and let the system run, [waiting] 3, 6, 9, 12 months [to get this] magical ROI, and that’s just not how people (buyers) operate.” – Bob Mason (21:00)

• “Re: platform plays: Obviously, you could still create a tremendous platform that’s very broad, but we think if you focus on the business problem of that particular vertical or domain, that actually creates a really powerful wedge so you can increase your value proposition. You could always increase the breadth of a platform over time. But if you’re not solving that intrinsic problem at the very beginning, you may never get the chance to survive.” – Bob Mason (28:24)

• Argon Ventures: https://argon.vc/