The Exchanges

Q And any stats you can share about the business, number of users?

A Yeah, yeah, yeah. The, the stats are, you know, 500,000 organizations use Airtable. Um, 50% of, of the, uh, Fortune 500 are paid customers. Um, and really the largest enterprises have been the, the biggest focus for us in terms of revenue growth and, and go to market, and even like the product roadmap, be able to scale up to support those customers. Um, you know, we, uh, if we were a public company, so we're not, um, but, uh, you know, we're in the hundreds of millions of revenue, and we would have ended Last year as a top decile grower amongst public SaaS companies. Um, and we also are forecasting a next 12 month revenue growth rate of, uh, also top decile, uh, growth.

AI assessment note: “500,000 organizations use Airtable. Um, 50% of, of the, uh, Fortune 500”

Q So, um, in various sort of practical terms, use cases, what, what does that mean? Like a summarization? Is that generation? What do people do so far?

A So, you know, we certainly have a lot of the simpler use cases. Um, you know, I'll, I'll give one tangible example. So a very, Uh, you know, actually multiple, very large retailers, um, are excited about using Airtable for translation of product SKUs. Right. And there's one particular one that's like very, very big and like we're working with very closely to do this. Um, but I think that the value here is like, you know, what does that mean? Traditionally, you have all these products SKUs as a retailer, right? So you're selling everything from, you know, guitars to, it could be toothbrushes and, you know, so on. And You know, if you want on your website, for instance, to show off the product and the description and the name of it in different languages, so Spanish, French, et cetera, but like also many of the other languages, um, you know, traditionally you would pay millions of dollars per year to like a translation agency, right? Like humans would go out and translate every single one of these products and it could be millions or tens of millions. And so that's an example of one where the human designed workflow and the visibility Of the process. So, you know, in our case, like we allow the customer to actually design like, Hey, here's the human approval workflow. So, you know, AI can first of all, generate a prompt for AI to run. So you can have a customized prompt for, um, …

AI assessment note: “very large retailers, um, are excited about using Airtable for translation of product SKUs.”

Q Do you think there's a world where, um, all of Airtable becomes AI first, and everything that people want to do in Airtable, they do through AI?

A So, yes, in a way, as in, I mean, I think it's, it's almost like, If we could have worked for the past 10 years on building the perfect platform to be the deployment vehicle of operational AI workflows, like I don't think we could have done much better than build exactly the Airtable product that we have today, right? It is the fastest, easiest way to stand up a data set or to integrate with existing data sets and to build a human workflow. Around it, like a human in the loop workflow, um, that's very intuitive. And now to plug in AI steps, right? Um, whether it's an AI field output that you can drag onto an interface layout, whether it's an AI automation step, you can, you know, kind of make calls to AI services through our embedded, uh, serverless, uh, hosted, uh, you know, code, um, or use our API, right? To do more integrations. And, you know, I think all of that results in, you know, kind of an experience where like, I do think the positioning of Airtable increasingly will be about, you know, not just, Hey, you can use Airtable to build, you know, data driven apps or, or have like a business app where that's, you know, you got data, you got the workflow, but actually the motivator for it is going to be increasingly by default. Hey, I want to like automate my workflow. I want to like systematize and then automate my workflow with AI. Right. And so yes, is the short answer b…

AI assessment note: “And so yes, is the short answer”

Q you sort of scale yourself as a CEO? What was your journey to going from founder to being able to run a company with, by the way, this level of complexity with where you have like PLG bottoms up and you have enterprise and you do, you know, horizontal and lots of vertical applications and automation and AI. Like how do you, how do you, how did you navigate that?

A Well, I'm gonna answer that in a flashback style, which is to say, I'll start with the conclusion first, and then go through the, the, uh, the story. What I wish I knew at the time of each of these phases is to take advice from people who are as close to the next phase, the next immediate phase of the company and the problems that we need to solve as possible versus taking advice from people who are too far removed, either in the forward direction or in the back. Right. But, you know, there was a time where I tried to really learn from as many people as possible. And I think one thing I learned is like, Maybe like a neural net, you know, if you feed too much noise into it, it actually gets confused, whether it's feeding it into a prompt or in the training data, like it can actually create more confusion than it creates like, you know, kind of coherence. And so I think being very selective about who and what you learn from at every phase, and you do have to come in with an opinion about You know, it's, it's like shaping the source of learning for yourself. Um, and so you have to come in with some thesis of the type of organization, business product that you want to build in the next phase. Meaning, you know, if you went in and talked to like, 50 different really smart advisors, and they could be other founders, they could be VCs, they could be operators, like, You could talk to …

AI assessment note: “take advice from people who are as close to the next immediate phase”

Q industry, uh, cover. So, you know, maybe one way to think about it is that there is like a Horizontal infrastructure layer and that's the Databricks and the snowflakes and, and there's a horizontal application layer and that's the Salesforce and the HubSpot and so on and so forth. Where, where do you position? Are you in between? Are you overlapping? Are you competing with them? How does that work?

A You know, I always find these very difficult because like my mind goes to, well, like there's actually 20 different dimensions. So let's, let's draw out like the full space of, of like, you know, 20 dimensional, you know, kind of, um, Like where everybody plays, but obviously, you know, to simplify things, you know, I think the easiest way to, to put it is there's this convergence between categories now. Right. And traditionally collaborative work management lived in kind of this like low end shallow category. And I think a lot of the players there still are more tactical team level project management oriented. There's historically been a category of digital process automation. So think like old school companies like Pegasystems, right. That are really tackling very complex business processes. But in a very brittle way, right? Like once you implement something on Pega, you're not iterating on that, like on a daily or even monthly or even probably yearly basis. Right. And it's a very heavyweight thing. Um, and you know, and so you're really only doing it for like a very small number of high importance processes or, you know, processes where like you really have to have it standardized.

AI assessment note: “there's this convergence between categories now”

Q mentioned, um, a, um, you know, some great customer use cases, um, What's your sense of the reality of the market, reality of, uh, enterprise demand for AI? You know, it sort of feels like last year AI was about to kill us all, and it sounds like this year we're all like, okay, well, maybe my POC can get budget approval kind of thing. What's the reality of it?

A Yeah, you know, I think it's funny. It's, it's one of those things where, uh, there's that quote, like, we often overestimate what's possible in the next few years, and we radically underestimate what's in the next 10 years. Yeah. Um, yeah, who knows? Uh, but, uh, whoever said it, they sound smart. Um, uh, but you know, I think, I think what's happened is first off, obviously chat GPT, Went mainstream. And I think that's what called a lot of attention to what even that generation of models were capable of. And it's funny because, you know, I talked to people who worked on, you know, that model. Uh, and I think the, the wide consensus amongst the model research community, not just at open AI, but like other companies is like, you know, I don't think it was anticipated by anyone that like it would blow up in such a mainstream way. If anything, it was like, maybe this is like the precursor To the model, maybe GP four or GP five will be good enough to like really be the one that kind of breaks out. But like even GP three, you know, wrapped around in like a pretty simple chat interface, it turns out. And obviously there was some like, um, you know, some tuning to, to work well with like instructions and chat, um, you know, kind of, uh, commands. But like, I think the fact that it took, you know, such mainstream attention is because it is so broad and so deeply capable. And it was li…

AI assessment note: “I think what's happened is first off, obviously chat GPT, Went mainstream.”