The Exchanges

Q Who did you think the customer would be for that?

A Initially, I thought it was going to be sales. I think you were, I think the way you've alluded to makes sense. It's like on a sales call, you want to know when it isn't going well, because then you can stop, pivot, and ask a question. You can say, I'm going to take a beat. Do you understand this? We have sales, but what we realized kind of in 2023 was AI is making this mainstream where people are going to find applications that we would have never expected. So one example was someone in 2023 reached out to us really early when we started getting product market fit. It said, Hey, I'd love to jump on a call. I've got a use case and I'd really like to talk to someone and I'm going to have my caregiver join the call as well. We're like, okay, this is interesting. Did not expect this. All right. Jump on the call. We're like, Hey, I was like, Hey, I love your product. This is great. Um, let me tell you how I use it and why I've got the caregiver on my call. Uh, I have early onset dementia. Um, I forget things. Uh, I know I forget things right now and I want to, I talk with my family once a week and I want to remember the things that we talked about so they don't feel bad. And so the caregiver was there to actually say, hey, how do we keep this subscription going while this person is having the conversation where it is going to get worse over time because he's looking at these notes …

AI assessment note: “Initially, I thought it was going to be sales.”

Q be connected to meetings, I, anyway, um, Um, I'm, I'm obviously, I'm very excited about the space. It really is so fun. I'm excited about your software, but let's go back to the original sales thing. Were you thinking that you would sell this to salespeople and they would have that sentiment analysis in real time or for post game analysis to see what they could have done differently? Both.

A So we started as purely real time that then you could look at it after the fact, because if you're a salesperson, you get one opportunity to make the pitch in a lot of cases. So you want that instant feedback. Uh, what's not realistic is your manager sitting on every single call that you have to give you that feedback on Slack, on Teams to say, hey, slow down your pace, talk about this, you forgot about that. Uh, it's not realistically, that's not realistic. So from our perspective, we thought that real time feedback would be incredibly valuable. Um, what we found was It is valuable for that niche use case, but a lot of times it becomes cognitive overload where if you are doing poorly and all of a sudden the score goes down from an engagement standpoint, people are like, oh shit, what's going on? What do I do? How do I solve this problem? And what we found was the feedback was like, this is great. I actually believe this. This is pretty accurate, but you're not telling me what to do next. And so that's where we were kind of like, okay, what do we need to do as a next step? Beyond just showing the metrics went from 80 to 75 to 69 to 62, how do we change that outcome?

AI assessment note: “So we started as purely real time that then you could look at it after”

Q What's your background in AI? How did you know that this was possible?

A I think my background in AI was more machine learning. So early in my career, I worked at a company called Faircast. We did airfare price predictions. So in the old days, this is really old days, you'd actually have to have boxes in different areas to actually go on the internet, pull data down, and you'd build these things called scrapers. And they would go in and say, scrape the airline ticket prices from all these websites on a daily, hourly basis. And we actually built a model that could predict airline ticket prices. And so that was really kind of Early days, machine learning, a little bit of AI, depending on how you want to look at it, to go in and say, we can predict airline prices based on date, time of day, uh, routes, weather, all these different variables that come into play. Uh, then when we started placed, uh, kind of very similar thing. If you remember the early days of the Apple phone, that blue dot would bounce all over the place. It would normally be a giant blue dot that someone, you're somewhere in this neighborhood. You go to Chicago, you'd look like you're on the other side of the river. And so with all those, those noisy data points, Uh, were signals for us. So we actually built models to go in and say, Hey, it says you're across the street, but if I send you a push notification to check into the nearest location that you have, where are you? Oh, you're at…

AI assessment note: “my background in AI was more machine learning. So early in my career, I worked”

Q users are using Read and what else they, what else they would want and what they're not? I guess what they're not using is easy enough. You check the software and you see what people aren't using and you maybe mark it a little more or understand that it, that it hasn't worked, but the things that they need that they haven't expressed, how do you come up with those?

A Uh, part of it is the metrics that you had talked about. I think the other part is kind of going in and just getting feedback. So because we have 50,000 new users every single day that are coming in, they're constantly trying these things out and we add new features. Uh, we use a company that recently was acquired, StatsSig from OpenAI, where they're able to let us test different things out to go in and say, hey, should we focus in on Google Drive Connect or SharePoint or OneDrive Connect because people want to search their meetings and the files that they've created to make sure that we can connect the dots there. And so we do these tests. Stats like lets us do that. We're able to see what the behavior changes are very quickly, and we give a winner, and then we turn that across the board. Or it's feedback where someone says, like, hey, I need this in Portuguese, and this is where we, this, Brazil is our top five market for us. We would have never known that had it not been for the customers kind of just coming out and telling us and saying, like, hey, we need Portuguese, and we're like, okay, are we seeing a big uptick in Portugal? I was like, actually, no, it's Brazilian Portuguese. It's a little bit different, and so we went in and we said, Hey, are we going to build two separate models for Portuguese and Brazilian Portuguese? And we're like, yeah, I don't know. The user sta…

AI assessment note: “part of it is the metrics that you had talked about. I think the other part is”

Q and make sure that they're getting their work done. I would imagine that there would be a note taker for the different role in a company, and I'm not seeing that here. And again, I'm looking at you guys as the model for what's the future of a lot of AI SaaS because you're so far ahead. So why do you think that it hasn't broken down that way here?

A I think you're, you're in that scenario, you're applying traditional SaaS approaches where it's like, I've got a solution for sales. I've got a solution for coaching. I've got a solution for intro meetings. And in this AI world, you don't need that. I think that one AI solution needs to go in and say, how do I tackle this for an entire organization where you're interviewing customers, where you're doing sales calls, where you're having internal standups and having that one solution actually gives you much more value than an individual standalone. So to give you an example, Let's say you've got a sales team of 50, they're going out in the market, they're pitching the product, clients are asking questions. That might be in a solution like Gong today. And you've got Gong, but it's too expensive, like you had mentioned before, where it's like, it might be too cost prohibitive, and the product team doesn't use it. Well, now all of a sudden, the product doesn't have access to any of that information. That is siloed in this wall where it's like, salespeople are talking with customers every single day, telling them why they like the product, or why they don't like the product, or what features that they want. Where we become is the system of record for productivity. And so that's going in and saying, bring in your emails, bring in your messages, bring in your Google Drive, your SharePo…

AI assessment note: “in that scenario, you're applying traditional SaaS approaches... in this AI world, you don't need that.”

Q it, 2007 when the iPhone came out, right? So that's a good indication of when the start time was. For chat GPT, for, for AI, chat GPT's launch November, 20, 22 is obviously not the start of it, but it's the start of suddenly the hype. You launched the year before to give people a sense of when, when this was. What did you use to build the first product?

A Yeah, so the first product was we actually looked for meanings that were available in the public domain, so being able to pull in those different conversations, but then we realized if you can't see the other person's reaction when someone is saying something, that creates low value because I'm only analyzing the person that is speaking, and that's, that's not that interesting. I want to see how people react to the words that are being said, so we had to go out and find additional data sources where we could see reactions. We hired actors to go in and say, like, read this script and read this out. Then we realized, That's too expressive, because they're acting for movies. So it's like, no one smiles with a giant smile like a joker. You're going to do a subtle smile or a smirk, and we need to pick those things up. So we started to go in and try to find these data sources, create these data sources, and that became our training data set. And then from there, we kind of tuned the models as we went. We used, early days, we used things like Mechanical Turk to go in and say, hey, what is this A versus B? Does this look correct from a labeling perspective? Then we started to go in and hire more people to go on the ground and watch full meetings that we generated internally and say like, hey, what do you think about this? What is your takeaway from this? What is the action item that yo…

AI assessment note: “So all those things combined kind of got us to that first model.”

Q just move on. Their AI interaction is kind of confusing. Do you have a platform risk, though? Because they clearly care about this. At some point, Google is Clearly gonna care about it. They, I, I don't know if they have Gemini in there yet or not, but I, I see them kind of at the edges. What's the risk of them coming in and creating an okay enough product?

A Uh, I think we already know the answer to that, and that's going in and saying they've got products in place. If you only use Zoom, if you only use Google Meet, if you only use Microsoft Copilot, you might get away with, like, this is enough for me. But honestly, I think you're exactly right, where the products aren't necessarily focused after launch. It's going in and say, we've launched this thing, here you go. The continued kind of investment isn't there, because that's not their core business. They're working on the next thing to introduce Copilot to more people. They're working on the next thing to enable Gemini. And The, the, the really interesting thing for us has been like, there's not much loyalty on platforms. So for our users on a weekly basis, we see that they use more than one platform for the vast majority of our users. So it's going in and saying like, Hey, now I've got a Google meet meeting with Gemini that has a different voice from zoom has a different voice from Microsoft teams. They don't talk with each other. The agents don't work together. They're just kind of in these silos. So I'm never going to look at this and it's a waste of time. And then the last thing I'll say on that is like, from a growth perspective, uh, when Microsoft Copilot launched in about 20, late 2023, 24, everyone was like, oh, you, you're in trouble guys. Like they're going to take over…

AI assessment note: “The continued kind of investment isn't there, because that's not their core business.”

Q What are some ideas that you wish somebody would create in AI that maybe are some simple ones that no one's jumping in and doing?

A Uh, I would say from a simple perspective, uh, It's not simple, but it's, it is going to be table stakes is I've spoken with a few companies in the last election that happened where they thought it was going to be a bigger contributor to noise in the marketplace. And what ultimately happened was it wasn't, it wasn't there ready for prime time. We're all AI savvy enough that we could say that image looks a little bit wrong. Something looks off there. It's not really happening. Other countries that don't have that, they actually ran into a lot of issues. The U S didn't talk about it that much, but in the U S itself, it was very marginal in terms of the impact that it had. I do believe in about the next year or two, it is going to be almost impossible to figure out, is this AI generated or not? And at that point, it's a simple problem in the sense of like, you want a certificate of authenticity. You want to say this is real or this is fake, and the ability to measure that is going to be incredibly important. In the same way with ad impressions right now, you've got viewability. Was that ad viewable or not? I think you're going to have the same thing when it comes to any type of video or text content where there is some kind of seal that says, Real, AI generated, or a combination of both. I think whoever figures that out is going to build a very big company, um, that is going to be…

AI assessment note: “you want a certificate of authenticity. You want to say this is real”

Q to still understand what other, what other possibilities there are for AI companies. You are someone who, I didn't realize this until I looked at your LinkedIn in preparation for this conversation. You invested in dozens and dozens of companies in addition to launching a couple of really successful ones. What, what are you seeing, uh, that's working for startups that are building on AI or building AI first companies?

A I think launching very quickly is kind of a, a, a thing that people said all the way back to the early days of Ycomber, fail quickly, push it out there. If you launch a product and you're not embarrassed by it, you're not launching it in a timely manner. Those things are all true, but it has never been more true when it comes to AI. You have open AI, which has a problem called hallucinations, where it makes stuff up to the question that you were asking. And that application has close to a billion monthly users now. And so that goes on and says, People are willing to go in and say, I'm okay with mistakes. I'm okay with drastic mistakes. As long as you're clear that this is a new technology, this could happen. So you need to make sure and review these results. But if you were able to give me 98% of the time, something that looks like magic, I will sacrifice the two percent of hallucination. So I would go in and say, like, be okay that your product is not perfect for early stage founders where you've got smaller products. Now, at our stage, we have to be more careful. We have to put checks and balances. Because now we're the system of record. But it's not.

AI assessment note: “launching very quickly... has never been more true when it comes to AI.”

Q Are you thinking of doing email too? Like, it sounds like you're, you're thinking of, of making email more useful by taking all these signals and then popping up the more important emails to the top of an inbox, but you're not thinking of creating like a superhuman, are you?

A We're not creating a superhuman, so we think you got to work within your existing workflows. We're not trying to change your workflows. That's why we send email updates for your meeting reports, but We are bringing in that content, that context. So in tiny now for Gmail, as well as for outlook, you can actually connect your accounts and we have millions of accounts that people have actually connected. And now we've got all the emails that come in and that you send out on top of the meetings that you have. So imagine you and I are having a meeting. There's four action items, those action items in a silo of a meeting. We, unless we meet again, they never got completed, but in an email, if you followed up and I said, Hey, here are the three PowerPoints that you asked for. Plus here's a quote on pricing. Now that action item from the meeting is complete. So you need that full level of context. So I think going back to your original question around like with the meeting note takers, why won't you have, why do you believe that you're not going to have siloed for product, for sales, for client success? Well, that information has to go to different organizations. So if the sales team goes and says, Hey, I'm getting this constant feedback that this feature isn't working correctly. Um, You want product to have easy access to that information. And in that same concept, you want those emai…

AI assessment note: “We're not creating a superhuman, so we think you got to work within your existing workflows.”

Q Can you some examples of what you've seen?

A So one is kind of on the creative editing side. So on the ad tech side today, um, I, my background's a little bit in the ad tech space. Uh, what you found was like, AI should be applied to every single ad that goes out there in the world today. Because You can go in and adjust it based on performance, do it in real time. Hey, the shirt should be red instead of blue. It should be winter, not summer, and do these things. It hasn't caught on because you've got old school, uh, companies going in saying like, well, does it align with the brand tenants? Does it align with what we're doing from a messaging perspective? And who's winning is the people that are focused in on performance. These are people where it's like, hey, I'm going to drive results, and if the results prove themselves out, eventually we will go out and deal with like, Does it align with the brand goals? Is this right color palette? Those things don't matter. So, uh, one has been around kind of video editing. Um, there's one company, I can't talk specifics on their numbers, but they've done incredibly well where they do video editing. And at first it was kind of like 50, 50 chance you'd get something that was great. But, the 50 times that was great, it was done four to eight weeks faster than anything that you could do in the current world. So, you would just go in and create more and more content, and now they are, …

AI assessment note: “So one is kind of on the creative editing side.”

Q If you use the report. But how do you know that, that sentiment is a part that they care about?

A Uh, because it's fed into the model. So it's going in and saying, it is easy, it has been never easier to compare when it comes to meeting transcripts and meeting summaries Three different solutions at the same time, because you've been on a call where you've got me and they'll take one, two, and then read perfect world. You just look at all three and say, which one do I like the best? And what we found is nine times out of 10, we're going to win that. And we see that from an adoption perspective where, like I had mentioned, uh, in our primary conversation, we're seeing about 50,000 signups every single day. So that's going in and someone's saying, I find this so compelling. I am going to go in and create an account on read. And those people, when they use it are sticking around 80% of the time.

AI assessment note: “we see that from an adoption perspective where... we're seeing about 50,000 signups”