SaaS Interviews with CEOs, Startups, Founders
Did Everyone Make Money in the Bizzabo X.AI Deal?
09 Jul 2021
Chapter 1: What is the main topic discussed in this episode?
Put some cap on it, however big range you want. I just don't, I mean, north of a thousand could be 10 million. I don't, I don't, just put some cap.
So it is, it is not 10 million, but it's a substantial amount and it's not a thousand.
The easiest way to do that is to go to getlatka.com and use our filtering tool. It's like a big Excel sheet for all of these podcast interviews. Check it out right now at getlatka.com. Hello, everybody. My guest today is Dennis Mortensen. He is an expert in leveraging data to solve enterprise use cases and a serial entrepreneur who successfully exited several companies on that theme.
His long-term vision of killing the inbox led to the formation of X.AI. Many of you have probably seen Amy and Andrew in your inboxes. This is that guy. Artificial intelligence assistants who schedule meetings. He speaks frequently to anyone who'll listen from the crowds of WebSummit to his building's doorman about an optimistic future for AI productivity and the future of work.
Dennis, you ready to take us to the top? Let's do it. All right. So let me jump in first. Is Amy working?
That's a good question. Are you working? So if you're asking me whether there's ambiguity in that of asking some agent to do some job, You're absolutely right. As an ask a human to drive from my place to your place, there is some risk that we'll see an accident.
If you ask a human to schedule a meeting, there is some risk that, you know what, she forgot that summertime starts a little early in London versus the US. And if you ask a machine to do the same, you're probably likely, given that we train the machine, to see some of that ambiguity end up in the machine agent.
However, the funny thing is that machine agents tend to be very good at the things that you and me are kind of not so good at and sometimes stumble on things you and me might think of as common sense. Having said that, we are setting up hundreds of thousands of meetings and this being one of them. So we're certainly in a, in very good shape.
But then again, we've been in that basement for the last five years.
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Chapter 2: How does Dennis Mortensen envision the future of AI in productivity?
So I think there's certainly very little debate on whether that of setting up a meeting is a real pain or not. So you and me, through your example, have just agreed that we kind of not really like to set up meetings. We like this, though. You and me chatting, that's the fun part. Having it set up, we don't really care whether it's Monday at 1, Monday at 9, Tuesday at 10. They're all equally good.
So if we can somehow escape that, that chore it is of setting up a meeting, that would just be wonderful. And I think what we're seeing right now is some group of prior solutions for where you are now somewhat outsourcing part of the pain to the guest as saying, hey, please go click my link.
And if you'll be so kind and just apply a little bit of effort and be okay with that, we can get the whole thing kind of solved with certainly some total less pain.
What we've tried to do is to kind of come up with some sort of solution for where we could potentially cater a little bit more to the guests where you can just reply back in natural language and say, I can't do next week, but I can do some afternoon the week thereafter. Or you know what? Let's do this in person. I'll be in Manhattan in first week of July.
Now, that is obviously awesome because that means we're kind of flipping it ever so slightly so the guests should be happier. What you describe is what if we have competing agents as in you have one solution, I have another one.
Mm-hmm.
Our solution doesn't have the intelligence to pick up some link in some text, visit that website, look at it through the eyes of, say, something like a human, and figure out, I should probably click one of these links and determine from what I see what time we can meet. That is just above and beyond what is feasible today. So that is certainly a conflict. What I would like, though...
is for our agents to be able to follow some of those links. That means now we can cross barriers. That would be awesome. I think, without me going all geek on you, what is really the question is, whoever gets to build the bigger network first It's probably going to end up having some sort of advantage because if you and me both used Amy or Andrew, neither you nor me would have to do anything.
We can just say shit like, hey, can you get us together for 15 minutes on Skype later tomorrow afternoon? As you click send, it is already agreed upon because Amy works for both of us. So that we certainly see happening internally for both. all sorts of meetings for where, hey.
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Chapter 3: What challenges does x.ai face with AI scheduling assistants?
Of course, yeah, yeah.
The counter to that is like, I pulled up a couple of examples here because it's like, you know, a guy at Ben Holloway, who's in the private equity space, really wanted my data, said, Nathan, great to hear from you. Happy to have a call sometime next week. And this is what he said, I'm reading, I'm copying in, I'm CCing in Amy, who will help us find some time to speak.
Look forward to chatting, best Ben. Amy, which is your thing, pops in and you can tell, you know, does Monday, May 1st at 11 PD work? Alternately, Ben is available on Monday and Tuesday. I then replied back, Tuesday works. But like they didn't. Amy didn't realize I was accepting her Tuesday recommendation and said, hey, Nathan, thanks for letting me know. Would Tuesday work?
Ben is also available on Tuesday at a different time. So it was. And then I wrote back. I eventually just said, Ben, these automated bots are a terrible strategy. You can see why below I'm going to pass on having the call with you. Best of luck. And I literally just and I just stopped it like I can't do this. Because there's no way to re-engage him, right?
So I just wonder, like, I just think the negative brand value around that is so big. I don't see how something like this gets to the point where everyone is using it, where you get to this nirvana you talk about.
So we certainly, so I'm four plus years in, Amy and Andrew have set up about 3,500 meetings for me. And it takes about eight emails for every meeting, about three and a half on each side in general, if two people are trying to negotiate it. As in, you and me have no software attached to what we do. So that means there's some 20,000 odd emails that do not exist in my life.
So I'm obviously not even happy. I'm fucking ecstatic. As in, I can do exactly what you described, outsource a little chore. And what happens in the vast majority of cases is that people say, yeah, I can't do tomorrow, Dennis, but I can do the day after. I can do next Wednesday, or I can do 1 o'clock, but not 2 o'clock. That is great.
Does that mean that we don't have a foo here and there, given that language is not a solved science? Sure we do.
But Dennis, we can measure it. I might be overblowing this because the number that would measure what you're talking to me, which is fine, is all about the upside. This many emails saved, etc. You can calculate, and I'm sure you have the data, how many times an x.ai email has been introduced, copied in to a conversation, and no meeting resulted.
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Chapter 4: How does x.ai handle ambiguity in scheduling meetings?
And you know what? I don't want to buy something from you. Yeah, 100%. So just ignore it. No, no.
What I'm trying to measure is the amount of times what happened to me happens across your platform. Where it is so frustrating, I just give up and say, like, screw it.
So we pick up on that level of frustration. But just to kind of give you perhaps... a better measure. So 83%, which was the last kind of look at of all the meetings that we do, are rated five stars, as in we apply the option for people to rate the dialogue.
I think that's fine, though, Dennis, but that I totally believe that I'm sure it's a great experience. But I'm talking about when they weren't scheduled at all.
Exactly. What I'm saying is, in that regard, that's certainly what we see that the vast majority are ending up successful when they don't end up successful. The funny thing is, what you described is actually not what we see that often. It is when the thread length goes beyond a certain inflection point.
What we tend to optimize on is to shorten the thread length, as in, what is the minimum amount of emails or the least amount of communication which I can do with you to get the meeting on the calendar? The less we have to talk, the better we tend to be off. They're saying, can you do next Wednesday at 1? If I can make a prediction for Wednesday, be a good time, that'll be awesome.
But you can just say, sure, let's do that. So that is the vast majority.
Yeah, by the way, I love, I get the product, by the way. I would love for it to, I mean, I love what you're trying to build. I'm just trying, I mean, these are just edge cases we're talking about. So hey, we beat this to death for 10 minutes. We only have 15. So I want to dive into some more of your story here.
If someone's listening right now and they have never heard of XDA, they want to start using, what's the average customer paying you per month for the tool? 10 bucks. Okay, 10 bucks. So good. Anyone can really get started. You launched, you said four years ago, so 2014? Yeah.
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Chapter 5: What metrics define success for x.ai's meeting scheduling?
So that is... I think compared to any other consumer product for where it's all about whether you get the next customer, I would agree. Remember, when I schedule a meeting, I get two things. Obviously, I have a product in market. I might even be so good at it that I can charge for it, but I also get a data input.
The worst thing that can happen right now is that I start to pollute my data set because I think I have everything solved. So we are extremely protective about that data set. So when you say something to Amy and that ends up being labeled and annotated in our data set, I need to make sure that that is true and honest.
We are now just at the inflection point for where we believe we have enough confidence in the data that we have for where we'll be less sensitive to what arrives tomorrow.
Okay, so I'm not going to push harder. I'm not sure that I agree with that, but we'll move on. So you launched in 2014. You build for a couple of years. You turn the paywall on about a year and a half ago. Talk to me about growth channel. So where did you go to get your first 100? It sounds like maybe a freemium plan, but where did you get your first kind of 100 paying customers?
What channel did you use?
So... First, I can start just rewinding a little bit. First, 50 beta testers was just friends of mine, the usual, hey, guys, I know you don't have an assistant, and I'm sure I can help you out here, start using it. That was certainly how I built up that initial pool. Their use of the product kind of brought the next 400.
And that kind of gave me that kind of 500 people actively kind of using the product, which was friends and friends of friends. Then we ran... a pretty successful kind of waitlist as we were kind of working that very initial MVP, which was certainly not robust enough to be a chargeable product in market.
And on that, we ran up hundreds of thousands of people on that waitlist simply because there was a grand lust to remove this chore.
When did you launch the waitlist? What year and how many years did it take you to hit 100,000 on the waitlist?
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Chapter 6: How has x.ai grown since its launch and what are its customer acquisition strategies?
But I'm not running the kind of traditional team that you would usually put in place and we've done in our prior ventures. So we are at the beginning of that.
Yep, and what's total rates at this point? I think you've raised something, right?
About $40 million.
Yeah, 40. And then, so I guess with that money, how aggressive are you willing to be on fully weighted CAC? Will you spend a full year one kind of ACV to get the customer or more or less? What do you like to come in at?
So... I tend to be quite comfortable once you have full understanding of it. I don't think I have yet an understanding that is so robust for where you could give me $1, and I can tell you that in the other end comes $3.20, because if I could do that, you give me $100 million, and then I can somehow turn that into more on the other side.
I do think I'm getting close to have a robust understanding, but I tend to be generally
very willing once all your metrics work out as you and i have talked about to inject as much as you can on one side but i do still think i have some unknowns well dennis but hold on what you what you just talked about is very different than what i asked ltv to cac ratios can be very healthy a dollar and three dollars that would be a three to one kind of a three right but what's way more important is if
if your churn is so low, your payback period could be three years and you could still have a three to one LTV to CAC ratio. It's actually way more important is how to get the flywheel turning faster where you get the cash back faster. So my question is actually a speed one, not a, does the ratio work one?
So a, so one, that's fair. So ratio, I think most people will be comfortable once they figure out how to get the ratio, right? Because that's just, they built something that is working. Then the speed one, I think it's just perhaps, some level of willingness to risk on the certainty of the future playing out as you would hope for, I turn to be, you know,
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