35M Users. $100M ARR. My 10-Year Bet Was Right. | Otter.ai, Sam Liang

1. 0:00 – 1:24

   Intro

   1. SLSam Liang0:00

      I was at Harvard University just two days ago, and a lot of professors actually don't allow students to use a tool like Otter to help them learn. I think that's, that's old thinking. The way we do education, the system was created at least 100 years ago. They have to allow the students to use whatever AI tools. Although human beings have been talking with each other for hundreds, thousands of years, most of the voice knowledge in the history has been lost. We never heard from Shakespeare. We never heard from Charles Darwin. There's tremendous loss of human knowledge and human intelligence. With those frustration and insight, we thought that the voice AI will be really huge in the future. I'm Sam. I'm Co-founder and CEO of Otter.ai. We started as a, a transcription tool, then evolved it into a AI meeting assistant, and now we're building a meeting centric enterprise knowledge base with agentic workflows on top of it. So far, we have over 35 million users. We exceeded $100 million in ARR. Now enterprises are adopting it to manage their huge meeting content. [upbeat music]

2. 1:24 – 3:54

   The Bet Nobody Believed In

   1. SLSam Liang1:24

      I did my PhD at Stanford University, and I learned a lot from my PhD advisor. His name is David Sheraton. He has the vision about what will generate a big impact, what will change the world. That's why he actually recognized the talent of Larry Page and Sergey when he wrote a $100,000 check to them before they had anything. I learned a lot from him in terms of thinking big. I was at Google 2006 to 2010. I was the leader of Google Map location platform. Then I quit Google in 2010 to start a mobile startup in Palo Alto. We were the first that build a location tracking system and also do persistent sensing on mobile devices that understand users' mobile behaviors, so that we can personalize the mo- mobile services for them. That company was successfully acquired. Then in 2016, I was thinking about something new and something bigger. While I was building the first startup, I had a lot of meetings with investors, a lot of meetings with our internal team and customers. Really hard for me to remember all the meeting contents. It's also hard to share that knowledge with all the team members. So I think there must be a better way to address that. Back in 2016, we say we're gonna record everything. We're going to enable it to be shared with other team members. Both made most people uncomfortable. Number one, being recorded is uncomfortable and also share meeting notes with other people is uncommon because traditionally people take notes on a paper notebook. It's a personal thing. We anticipate that the mindset will change, the culture will change, so we build the product that enable that change. You can convince some people. You cannot convince everyone. That's okay. You know, for any new product, it, it follows certain adoption curve. For people who adopted a product like Otter early, they actually get value and benefit sooner. They can become, uh, more effective, more productive, and they can show the value to their colleague. You know, they can help convince the other users as well. So you have to pick something that most people haven't, haven't been convinced yet.

3. 3:54 – 6:01

   Why He Refused to Use Third-Party APIs

   1. SLSam Liang3:56

      If you want to really go big, you need to have deep technology roots. I came from technology background. I like technologies. I like engineering. I also see that a lot of revolutionary companies are built on deep technologies like Google. Today, there are a lot of APIs you can use to quickly build a meeting note taker. Any college student can do that already. In 2016, 10 years ago, at that time, if we were waiting for someone else to create the API, you know, we, we would be many years late. When we decided to build our own speech recognition technology, we didn't know how long it would take. We know there, there is a lot of risks. We know that we had a lot less resource, a lot less money, a lot less people than Google or Microsoft. What, what if Google or Microsoft or other people catch up fast? Our choice is to build deep technologies which can enable us to create a new revolution in the future. What differentiation can you create? That's the biggest problem for a new startup. From day one, we always had that belief that that should be the way, that should be the right way because we're, we own our own technology, so we can keep the cost low. If you use a third-party API, you have to pay them a lot of money that limit how much a free service you can provide. There are still deep problems that require, uh, AI scientists to work on. For example, you know, when we have hundreds of millions of voice data, how do we use that to truly model human conversation? How do we model the interactions of multiple speakers talking to each other in the meeting? That's still a unsolved problem. To solve that problem, you cannot just rely on third-party APIs. You have to build your own deep AI tech. If it's too easy for you to build, it's very easy for 100 other people to build as well.

4. 6:01 – 8:09

   The Next Interface Isn't a Screen

   1. SLSam Liang6:01

      Behavior always change when you have new technologies. I- if you look back in the last 50 years, right, before internet became so common, it feels like we have been having emails forever, but then a new tool like Slack became popular, then people actually send fewer email. They rely on Slack. But then, you know, with when the voice technology become much more mature, we think that voice will become the primary interface for enterprise intelligence. You probably don't need to write so much. In a few years, people will rarely write anything, and they will rarely use keyboard to write anything. They can just talk because talk is easier than writing. They can just talk and our AI will write everything for you. It's, it start to happen. A lot of people actually use AI to write documents, to write emails, to write LinkedIn post. It's already happening. It will only accelerate. So our view is that voice is becoming the primary interface with, uh, business intelligence. Looking forward to the next many years, there's still a long way to go. At least 95% or even higher, I would say 90, 99% of the world hasn't adopted a tool like Otter yet. We have to look at the next 10 years, not just today. That's how you know this generational companies are built. People say, "Hey, building a startup like running a marathon." Actually building a startup is way harder than running [laughs] a marathon. I've run 11 marathons and going to run another one in two months. That definitely help me stay healthy, handle stress, help me push through all the challenges. Most people give up pretty fast. If you're building something challenging, the difficulties are as expected. You have to persist and, and continue pursuing your goal. [outro music]

Episode duration: 8:09