[Highlight] Airtable's Howie Liu on What Founders Get Wrong About Building Product

0:00And I think what's been consistent but not necessarily understood is that we've always wanted it to be more of an app platform than a collaboration tool. So when we first launched, we had to obviously pick an MVP that was, you know, simple enough to build quickly, although quickly for us was like two and a half years. You spent two and a half years before you shipped a product. Correct. Yeah. Actually, weirdly parallel timeline to some other great PLG companies like Figma and others, which we can come back to and like why I think that was a really interesting moment for creating this new grade of like desktop rich, you know, kind of experiences that were web-based, collaborative, instantly shareable, and kind of disrupted certain work patterns.

0:36But we spent two and a half years building this product. And the reason for that was this was not meant to just be like a simple project management tool or even like a spreadsheet replacement, but really like an app platform that was way easier to use. Now, the V1 looked like a spreadsheet that was partly on purpose because we wanted to make it really intuitive. You could just start using it, didn't have to learn a new UI, but then also partly because we didn't have the time to build out the interface layer or like additional views. You didn't have the time after two and a half years. I mean, hey, we were working, you know, methodically.

1:08And it makes sense. And obviously it worked out for you, built this incredible tech infrastructure. But just on this meta point, if a founder, because you do some angel investing, the founder came to you and said, Howie, here's my pitch. And I'm going to spend the first three years building it. And then I'll start shipping to the world. Well, don't lead with that part. You know, talk about like the big, big... So what has to be true? What kinds of products lend themselves to that type of formation versus the lean startup iterative? Yeah, I think, I mean, my belief now, in hindsight, and I had a very short-lived startup before Airtable called eTax, ended up getting Aqua hired by Salesforce.

1:39So I only have a data point of two, maybe like 1.2. But my belief is that either you have to have very strong conviction in the end game, and ideally even like the mid game of the company, meaning like if you're building SpaceX, you have like enormous confidence that there's like massive market opportunity at at the end of the road, right? If you can really make a cost-effective, safe, like scalable rocket, there is a market for rocket payloads that are cheap, right? And so you know there's a big payoff if you can do this. You also know you have like durable advantages because other people are not going to go and just enter into this space really quickly.

2:15So you know the mid and end game are strong, but then the kind of beginning of the game is really hard. I think Airtable looks kind of like that where we thought really methodically about the space. I mean, the premise was really, you know, from working at Salesforce, realizing that there's a large class of business applications that really need a platform, not a solution, to be served well, as in you need to customize the data model per customer. Every customer has a different workflow. And maybe it's just a long tail use case that doesn't warrant a really good best of breed vertical solution like a workday for HRIS, right, or Salesforce for CRM.

2:51And even Salesforce itself did a really good job of serving customers, not because they built all the features for CRM, but because they built a platform that could be customized for every customer install. So that was the insight. And I felt pretty convicted based on, you know, both like working within Salesforce, doing some early customer validation, you know, talking to customers, understanding like what are the different workflows you're doing in spreadsheets or underserved by current products to kind of validate the market. Obviously, you can never be 100%. But I felt pretty confident if we could nail the execution of a platform that could serve all these use cases with a real kind of application or relational database model, that we would be differentiated.

3:32We'd be competitively advantaged. So it wouldn't be easy. I mean, the fact that it took us so long to build it meant that it wouldn't be something that other companies could just really quickly fast follow, right? And by the way, how many companies did you talk to when you're doing over the two and a half years? How many prospective customers did you talk to? Probably like dozens, not hundreds. Wow. And that was, do you think that's a lot or? It seems like a small number. Yeah. Well, I mean, maybe you're just saying. What do people think? Does that seem like a lot or a lot? It's like, gosh, you didn't build multiple views.

3:59You only talked to 12 customers. Yeah, exactly. It's kind of the lazy startup approach. Yeah, yeah. No, I mean, on that point, I think of it as actually, so in math, there's this concept of eigenvectors, which is where you have a really large, multi-dimensional search space. But to reduce the dimensionality of the space, you can pick vectors or dimensions that best represent that space. So to make it very simple, imagine a 3D space. You have a bunch of dots throughout it. Well, find the 2D axis, the plane, that best captures the spatial coordinates of all the dots, right? If they actually kind of naturally tend towards a certain plane.

4:40And so I think the idea here is, you know, you don't have to go and collect every single dot, first of all, across every single dimension to understand, like, you know, the diversity of customer use cases you want to solve for. And I think, like, if you're picking 12, you know, frankly, even like three, and I'll come back to, like, how we apply this concept even today to product development. But I think if you're picking like 12 customers across a wide range of industries, company sizes, or whatever, like the space that you want to, you know, kind of fit well into, it's pretty unlikely that you're going to overfit like exactly to just those 12 and that every other company that looks like them is going to be completely different or like your solution is going to be inapplicable, right?

5:19there's always kind of this healthy balance between, yeah, you want to collect more data, but I don't personally think you have to go and get 100 customers to validate the premise if you have 12 or even five pretty representative ones that are not all weird outliers that are super early adoptive and nobody else will ever look like them.