The Neuron: AI Explained
Your AI Meeting Agents Aren’t Enough: Otter.ai's Sam Liang on Enterprise Knowledge
16 Dec 2025
Chapter 1: What is the background of Sam Liang and Otter.ai?
Hello, everyone, and welcome, humans, to the Neuron Podcast. I'm Corey Knowles, editor of the Neuron, and we're joined, as always, by Grant Harvey, writer of the Neuron Daily AI Newsletter. How's it going, Grant?
Going well, going well. Today, we've got Sam Leong, the co-founder and CEO of Otter AI. Before Otter, Sam built the blue dot for Google Maps, you know, that thing that tells you where you are. Now he's transcribed over a billion meetings. And last I heard, crossed $100 million in annual revenue, which is pretty awesome. With less than 200 people at the time, that's pretty awesome.
Welcome to the show, Sam.
Chapter 2: How is Otter.ai evolving beyond meeting transcription?
Thank you. Thank you for having me here.
Yeah. So you recently announced that Otter is moving beyond being just a meeting note taker app and building into an enterprise knowledge base with agentic workflows, MCPs in there, kind of trying to take the insights that people get from meetings and then expand that and connect all of your enterprise tools to it. That is a big, awesome strategic shift.
Could you explain the thought process there and what all you're doing in the enterprise space? I think it's really awesome.
Yeah, of course. I wouldn't use the word shift. I would say it's an evolution.
Chapter 3: What is the significance of a meeting-centric knowledge base?
We created the AI meeting note taker space. Basically, we started back in 2016. We launched our first product in 2018. Then we built the other AI meeting note taker that can join your Zoom meeting, Google Meet, Microsoft Teams, WebEx. No matter what tool you use, Otter can help you. You can work for online meetings, for offline meetings.
Like if we meet in person at Starbucks or restaurant, we can use Otter mobile as well. We also recently released Otter MacBook. We're working on a Windows version as well. So I would say there are two stages. The stage one is the meeting note taker. The idea is that all of us spend so much time in meetings.
There are a lot of data show that enterprise knowledge workers spend at least 30% of their time in meetings. And if you're a manager, if you're a VP, you spend maybe 50, 70, 80% of your time in meetings. Traditionally, all this data is lost.
Chapter 4: How can voice data improve enterprise efficiency?
People use a paper notebook. I still have a paper notebook in front of me. Or Google Docs or Notion to manually take notes. So we create the AI meeting note taker to automate all of that.
um then we see that the uh more and more people are using this uh even in fortune 500 companies or tons of people using honor but most people are still using it as a individual tool um you know they they record something they keep it to themselves um but the Value is way bigger if you aggregate all these meeting notes as a team. For ourselves, for example, we have just over 200 people now.
We record almost all our meetings in the last eight years. Sales meetings with customers, marketing meetings, product, project management, design, recruiting. Both external meetings and internal meetings. That allows us to operate really efficiently. We organize the meetings in either public channels or private channels, very similar to the way people organize their workspace on Slack.
We actually build Otter workspace using the same model as Slack because we see the similarity between Slack and Otter. Because basically Slack you communicate using text messages. But on Otter, we capture all the meeting contents where you communicate using voice.
Chapter 5: What are the potential applications of AI in meeting workflows?
The similarity is really strong because between Slack and Otter, you basically talk to the same group of people on the same set of topics. So that's why we built Otter Workspace in a very similar way to Slack. So Otter Workspace allows you to organize and manage all your meeting contents. So effectively, it create a meeting-centric knowledge base.
The reason I use a meeting-centric, the reason is interesting is that traditionally when people think about knowledge base, they only think about the written documents, like documents in Google Doc, Notion, emails for Slack message, or some data in MySQL CRM. People rarely think about voice data because traditionally all the voice data is all lost.
But now with Otter, we help people capture that voice and meeting data. And we create this workspace to help you organize it so that you can find the content in the right relevant channels. Or you can search globally. In our company, you can almost find anything in any team. Oh, wow. No matter where you work. So most of our content is actually public.
The idea is that to reduce information silos.
Chapter 6: How does Otter.ai ensure data security and privacy?
Again, in the past, almost no meeting is captured. But now some meetings are captured, but most people still keep it online. to themselves, keep the meeting notes to themselves so that each team, there's still a wall between each team. It really slows down information dissemination or propagation. That slows down the operation of your team. They create a lot of inefficiency.
So we see this is why it's important to create a meeting-centric knowledge base. Another reason is this, if you think about it, number one, for most enterprises, actually meetings is the most expensive activity. Most people didn't realize that. Just think about how much time all the team members spend in meetings. It's a lot. It is a lot.
If you're a manager, if you're a VP, you spend more than 50% or 70% of your time in meetings. Effectively, the employer spends most of their money paying people to go to meetings.
Yeah.
Chapter 7: What challenges does AI face in understanding human conversations?
Right. You can count up all of the salaries of the people involved and you can say how expensive the meeting is.
Exactly. If you spend 50% of your time in meetings, effectively half of your salary is spent in meetings. Traditionally, there's not even a method to evaluate, are these meetings even effective? What's the return on that investment? What are the contents that people talk about?
Again, people's brain can remember a small fraction of the contents of the meeting, and they forget really fast within the week. Probably 90% of the things they already forgot.
So, you know, the facts are pretty clear right now. AIoT is really reshaping industrial efficiency, security, and decision-making in a very real way. We've all heard the buzz around artificial intelligence of things. But what's interesting is that it's finally moved from promise to performance.
Companies are actually seeing measurable improvements now in efficiency, in resilience, in safety, even in decision speed. And the organizations that are leaning in the hardest, they're pulling ahead already.
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Chapter 8: What future innovations can we expect from Otter.ai?
A new global study from SAS digs into this shift, looking at how leading manufacturers and energy organizations are combining AI and IoT to run smarter operations, accelerate their company innovation, and build a genuine competitive edge. And if you're wondering whether now is the time to act, the takeaway is pretty direct. It really is right now.
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Another issue is like, for instance, say you're having a meeting and you're using the built-in Google transcription. And then that's nice that it's transcribing everything for you, but it's sitting in a Google Doc. And unless you go back and check that Google Doc, that data is just still as lost as if you had had the meeting and nobody transcribed it.
It's there, but nobody's doing anything with it. Yeah.
That happens to me a lot. Transcribe a meeting and it's just lost.
Unless you share the meeting notes with the team, with even cross-functionally, the value of that note is very limited. So it's really important to create that network effect of meeting data. The definition of network effect is that the more content is created, the more people have access to it, the higher value it generates.
So this is, again, why a meeting-centric knowledge base is so important. Another point I want to make is that I already saw some data that claims, I don't know how true it is, that claims that 50% of the written document on the internet, new content, is already generated by AI, rather than written by humans manually.
If you think about it, maybe in a few years, 90-95% of the written document will be generated by AI. People will rarely write themselves.
I can see writing being a lost art. Yeah. Especially as voice transcription gets better.
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