David Shim is no stranger to the startup world. A repeat founder, former Foursquare CEO, and active investor, he has a deep understanding of what it takes to build and scale a successful tech company. His latest venture, Read.ai, is on a mission to become the ultimate AI co-pilot for every professional, everywhere. Starting as an AI meeting summarizer, Read.ai has rapidly evolved, leveraging unique sentiment and engagement analysis to deliver smarter, more insightful meeting outcomes. The company’s product-led growth has been explosive, attracting over 25,000 new users daily without a dollar spent on media and recently securing a $50 million Series B funding round.
In this conversation, David shares the origin story of Read.ai, detailing how a moment of distraction in a Zoom call sparked the idea. He explains their unique technological approach, which combines video, audio, and metadata to create a richer understanding of meetings than traditional transcription. David also dives into his philosophy on building for a horizontal market, the future of AI agents, and his journey as a founder and investor.
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Nataraj: You’ve worked and created companies before and after COVID, and you mentioned a lot of your team is remote. Do you have a take on whether remote or hybrid work is better? What is your current sense of what is working best for you when running a company?
David Shim: I’d say hybrid is the future; it’s what works. Where I would say hybrid doesn’t work as well is if you’re really early in your career. It is harder to build those relationships and get that level of mentorship on a fully remote basis. It’s not to say that it’s impossible, but it is a lot more difficult. When you’re in an office, you have that serendipity of meeting people and building connections. When you’re fully remote, especially early in your career, you don’t know who to ask beyond your manager and your immediate team.
That said, once you’re more senior, I think it becomes easier to be fully remote. You know what to do, who to talk with, and you’re not afraid to break down walls. I think Reed’s is the same way. We’ve got a third of our team fully remote, and then people come into the office Tuesday, Wednesdays, and Thursdays. We let people come in on Mondays and Fridays, but they don’t have to. What’s really happening is people actually like that level of interaction, so they’re coming in without being required.
Nataraj: Let’s get right into Read.ai. Great name, great domain name. Talk to me a little bit about how the company started. What was the original idea?
David Shim: The original idea started when I was in a meeting. After I’d left Foursquare as CEO, I had a lot of time on my hands. It was still peak COVID, so no one was meeting in person. I was doing a lot of calls, either giving advice or considering investments. What I started to realize was within two or three minutes of a call, you know if you should be there or not. I’d think, ‘I should not have been invited to this meeting. Why am I here?’ But you can’t just leave. So, like most people, I’d surf the web or answer emails.
One time, I noticed a color on my screen that matched someone else’s screen. I looked closer and saw a reflection in their glasses. It was the same image I could see on ESPN.com. That triggered an idea: can you use not just audio, but video to understand sentiment and engagement? Can I determine if someone is engaged on a call? It wasn’t about being ‘big brother,’ but about identifying wasted time. In a large company, you can invite 12 people to a meeting, and they’ll all accept, potentially wasting 12 hours if they didn’t need to be there. So, the idea started to form around using this data to optimize productivity.
Nataraj: So were you analyzing video and text at that point, or just text?
David Shim: Video and text. Transcription companies already existed, as well as platforms like Zoom and Microsoft that had transcription built-in. I didn’t want to build something that everybody else already had and just make it a little bit better. I wanted something that was a step-function change. So we said, let’s take the existing transcripts but apply sentiment and engagement to them. Think about it: David said this, but Nataraj responded this way. That narration piece is missing. Our AI can go in and say, ‘This is how the person reacted to the words.’ Now, an LLM not only has the quotes that were said but how individual people reacted. It could say, ‘The CEO was really skeptical based on his facial expressions and became disinterested 15 minutes into the call.’ You can’t pick that up from quotes, but you can from visual cues.
Nataraj: What’s the main value that the customers got from Read.ai at that point?
David Shim: In 2022, we launched the product with real-time analytics, showing scores for sentiment and engagement. People found it interesting and valuable. But what was missing was stickiness. People would say, ‘You’re telling me this call is going really bad, but what do I do?’ You’re not giving me advice. That’s when the larger language models came out at scale in late 2022. We tested them and wondered if our unique narration layer, when applied to the text of the conversation, would create a materially different summary. The answer was yes. Comparing a summary with our narration layer to one without, it was totally different. You want to put what everyone is paying attention to at the top of the summary, and getting that reaction layer really changed the quality of a meeting summary.
We started 2023 as the number 20 meeting note-taker in the world. Now we’re number two, and we’re within shooting distance of number one. To go from number 20 to number two in less than 18 months highlights that there’s a difference in our approach, methodology, and the quality of the product.
Nataraj: And how did you acquire your customers in these 18 months? Was it inbound, outbound? Did you target a certain segment?
David Shim: Many VCs say to pick a specific niche and go vertical. My take was that this is such a big market. If this is a seminal moment where everyone’s going to require an AI assistant, it means everyone from an engineer at Google to a teacher to an auto mechanic will need it. So we went horizontal versus vertical. That has helped a lot from a product-led growth motion. We’re adding over 25,000 to 30,000 net new users every single day without spending a dollar on media. It’s pure word of mouth. If you see the product, people will use it, talk about it, and share it.
Nataraj: Is that because if you’re on a meeting with someone, the other people see it being used and then they buy it? The product inherently has that viral aspect, right?
David Shim: 100%. Meetings are natively multiplayer. The problem now is, ‘How do I get access to those reports?’ We’ve made it really simple for the owner to share it just by typing in an email address, pushing it to Jira, Confluence, or Notion. We’re not trying to be a platform where everyone has to consume the data. This is where ‘co-pilot everywhere’ comes into play. We want to push it wherever you work. You can see the data on a Confluence page or a Salesforce opportunity that has better notes than the seller ever created. At the bottom, it says, ‘Generated by Read,’ and you wonder, ‘What is this thing?’ That bottoms-up motion has driven a lot of our growth.
Nataraj: I can almost see this becoming an ever-present co-pilot in a work setting that will change productivity for knowledge workers. Is that the vision where you’re going?
David Shim: That’s exactly what we’re thinking from a ‘co-pilot everywhere’ perspective. When you think about the current state, it’s about pushing data to different platforms. But you also need to pull data down. For example, Read doesn’t treat your first meeting on a topic and your tenth meeting as silos; it aggregates them to give you a status report on how a topic is progressing. Three months ago, we introduced readouts that include emails and Slack messages. Now it’s truly a co-pilot everywhere, not just for your meeting. The adoption becomes incredible because you don’t have to log into Gmail, Salesforce, and Zoom separately. It’s just right there.
Nataraj: You’re still thinking breadth-first, or are you now targeting what a Fortune 500 company wants versus an SMB?
David Shim: We’re still horizontal, but we’re picking specific verticals based on customer demand, like sales, engineering, product management, and recruiting. That’s why we did integrations with Notion, Jira, Confluence, and Slack. Another way to look at ‘co-pilot everywhere’ is agents. Everyone’s talking about AI agents, but in reality, you want your Jira to talk with your Notion, to talk with your Microsoft, to talk with your Google. That’s the push and pull of data between integrations. I think that is going to be the next big space.
Nataraj: What about the foundation models you’re using? Are you held in control by their pricing?
David Shim: We are not held in control. Last month, 90% of our processing was our own proprietary models. We use large language models for the last mile—taking the interesting topics and reactions we found and putting them into a readable sentence or paragraph. But we’re building our own models that cluster groups of data together, identify the subject matter, and then we go to the LLMs and say, ‘Summarize this for us.’ 90% of our processing cost is our own internal models. We have five issued patents now with more pending. We’re not just a wrapper layer with good prompts; that’s not a defensible moat.
Nataraj: What do you think about the whole trend of agents? Are we seeing any real use cases?
David Shim: I think it’s early. It’s the same way with voice. Voice is interesting, but if you look at Alexa or Siri, they had massive early scale and then kind of dropped off. It’s an important play, but it’s a feature, not the whole product. With agents, it’s the same thing. It’s not that simple to say, ‘Go search for flights and find me the best one.’ You need to know what to ask for. What dates? Are you using miles? An agent in theory could do that, but you still need to upload the training data. I think the agents working in the background are more likely to succeed, where someone has trained data on how to handle specific use cases. But for the consumer, they’re not going to know what an agent is, just like most consumers don’t know what an API is.
Nataraj: What is the vision for Read.ai for the next couple of years?
David Shim: In the next one to two years, it’s diving further into ‘co-pilot everywhere.’ We’re adding more native integrations with both push and pull capabilities. Where we want to get to is optimization. I’ve got your emails, messages, and meetings. If you’re a seller, as you have each call, I can go into Salesforce and update the probability of a deal from 25% to 50%, then 75%. We can push a draft to the seller saying, ‘We think this opportunity should go to 75%,’ and include the quote from the meeting that justifies it. Now, what was the most hated function for a seller—updating Salesforce—becomes an automated process where they just swipe right or left. That’s the level of optimization people will ask for.
Nataraj: I want to slightly shift gears and talk about your investing. What’s your general thesis?
David Shim: On the venture side, my thesis is if you believe in me enough to invest in my company, I should have the same belief to invest in your VC fund. If you’re a portfolio company, they’ll often give you access for a lower amount. I think every founder should take advantage of that. When I do angel investing, it’s one of two things. One, anyone I know that I’ve worked with before who asks me to invest, I’m more likely to say yes. It’s about giving back that same level of trust. The second is for more interesting opportunities that come up on my radar where I feel they have something novel that can deliver outsized returns.
Nataraj: What do you know about being a founder that you wish you knew when you were starting?
David Shim: At my first company, Placed, I was a solo founder. That is very expensive on your time, stress level, and relationships. You have nobody else to go to. I would say, don’t force it. If you can find co-founders that you trust and work with really well, do it. With Read.ai, my co-founders Elliot and Rob have been incredible. It distributes the work, stress, and knowledge. When you have three really smart people coming back together with different ideas, you can ideate better. So for any founders out there, if the opportunity exists, go with a co-founder versus solo.
From an investor standpoint, outside of your own startup, don’t over-index on anything. Whatever is hot will stay hot for a little bit, but it will almost always drop off. Be careful about over-indexing. A lot of times, just put it in an index fund. The S&P 500 is up 25%—that’s better than most VC IRR on a yearly basis, and it’s liquid.
This conversation offers a masterclass in building a modern AI company, highlighting the importance of a unique technological moat, a powerful product-led growth engine, and a clear vision for the future. David’s journey provides valuable lessons for any founder navigating the AI landscape.
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