The Lean Startup methodology has created a frenzy to pivot. Entrepreneurs seek instant gratification, and when they don’t get it, they rush to pivot. The market is strewn with false negativities as a result, because entrepreneurs don’t have the patience to stay with a concept, develop it, and sell it.
ADARA is a counter-example that pivoted, but to an idea that took a lot of lengthy selling to gain ground.
Sramana Mitra: Let’s start at the very beginning of your journey. Where are you from? Where were you born, raised, and in what kind of background?
Charles Mi: I was born in China. I’m a first-generation immigrant. I came to the US when I was around 12. I went to college at UNC, Chapel Hill and then came west to California for graduate school at Stanford. That was my educational background. I started my career at IBM Almaden Research. It was a very exciting first job. I was actually building an anti-money laundering solution using data from the public Internet and also from financial institutions.
Sramana Mitra: This was fraud prevention for money laundering?
Charles Mi: Yes, this is fraud prevention. What organizations were looking for was basically, “How do we identify money laundering online?” We were putting together this solution where we were not only using the financial institutions data but trying to connect various financial institutions data together into an ecosystem. We use a lot of signals from both structured and unstructured side to try to do a better detection.
Sramana Mitra: What year are we talking?
Charles Mi: That was 2003 to 2004.
Sramana Mitra: How long did you work in IBM?
Charles Mi: I worked there for two and a half years. When I first started, it was a bit smaller. When I left, it was much bigger. In the beginning, that product was just an idea. It was more of a research project. Then the solution team came in and really saw the value that we built. Eventually, it became a product.
Sramana Mitra: What happened after you left IBM? What was your next move?
Charles Mi: My research background back at school was in Natural Language Processing. That was why IBM hired me for that particular role. While working through these different projects, I started getting exposed to the value of data and seeing the Internet not just as a source of facts, but also as a source of attitudes and opinions.
I and my co-founder, who went to Stanford with me, started a company together that focused on building a search engine for attitude. Our proposition was that we know Google is really good at searching for facts, but we were saying, “There is a missing link here for day-to-day information retrieval. People are also looking for opinions, trends, and other things.” We started working on OpinMind, which was a search engine for attitude. In OpinMind, you basically see people getting excited about a movie or how many people are going to see the movie. If it’s a movie that’s already out there, what do they like about it?