Low-Code in the Age of AI and Going Enterprise, with Howie Liu from Airtable

0:05Hi, listeners. Welcome to KnowPriors. Today we're talking to Howie Liu, the co-founder and CEO of Airtable, which now serves half a million organizations around the world, including folks such as Scale, Benchling, Adobe, Riot Games, Amazon, and Pottery Barn. Recently, Airtable launched a suite of AI features. We're really excited to have Howie on to discuss the state of low-code and no-code AI tools, how he's transformed the business over the last few years, and what's happening generally in enterprise AI. Welcome, Howie. Thank you. Excited to be here. Most of our users know what Airtable is, but for anybody who's new, what does Airtable do and where'd the idea come from?

0:40You know, Airtable has been around for a little over 10 years and we launched in 2015, spent two and a half years building the product before then. But for the people who knew of Airtable back then, I think, you know, you probably would say Airtable is like a spreadsheet on steroids or a really awesome productivity tool. And I think those were true statements. But what we've always been underneath the hood is a true app platform that just happened to be really, really easy to use. So we kind of cut across different categories. There's the low-code app platform category that pre-existed us, filled with pretty complicated platforms and required a fair amount of technical expertise.

1:15You had collaboration tools like Trello and then later like Asana and so on that were very easy to use, but were more project management centric. We came in and did something in between, which is give people the ability to build real apps with a real relational data structure, logic and automations and then interfaces, but did in a way that was so much easier to use that it doesn't look like your traditional app platforms. And I got the idea basically by working at Salesforce. I had a very small company before then, a startup that was acquired by Salesforce and working within their company, just all the power of the platform model, right?

1:52Realize that Salesforce didn't win all these CRM use cases because they had just built all the features for CRM, but really because they had created a platform that could be customized for every customer's needs. And so, you know, coming out of that, I really wanted to apply that concept, but democratize it and sort of make a much more accessible app platform that could really open up apps to many more people, citizen developers, and use cases than, you know, had been possible before. So very strong conventional wisdom in Silicon Valley, we'll say, like, build a killer app, not a platform. So like, you know, why does that apply here?

2:27Why did Airtable work as a platform from the beginning? And I hate to interrupt, by the way, but I've known Howie from his first company. And when he was starting Airtable, we met and he walked me through this and all the applications and the ability to have different verticalized applications and to build apps. And I remember thinking, that's crazy. This is going to be so hard. It's impossible to do as a startup. And of course, he pulled it off, which is like amazing. so I think to Sarah's point you really beat conventional wisdom and you did something really outstanding in terms of building out this broader this broader popcorn you know actually and now now I'm just going to interrupt you one more time because it is and we want the answer by the way but it is quite funny I was on a blow up a friend in common that I'm sure a lot knows too Eric from open door and he he was also we were talking about angel messaging at some point.

3:20And he was just like, I really didn't think this thing was going to work. Like it was so poorly scoped, but like how he seemed good. And, and so, you know, it's a little bit unbelievable. I don't think, you know, it turned out, you know, you weren't wrong. Anyway, like it did turn out to be hard in many ways. I think a few things that we had going for us. One is, you know, we did have an existing product and paradigm to sort of compare ourselves to, which is spreadsheets, right? So everybody has used spreadsheets at this point. I mean, it's like the most prolific app building platform out there.

3:56And I think the fact that now we're how many decades in, like four decades in to, you know, the usage of spreadsheets, I mean, they were one of the first core applications for all computers. And maybe at that time, it was like kind of a mind-blowing paradigm, like, oh, wow, you have like all these cells, you can do whatever you want, you can model any data. But, you know, four decades in, I think the spreadsheet products have paid the cost of going through that steady march of kind of creating a new category, right? And getting people to be familiar with it, such that by the time we came in, we made a really intentional set of design choices to make Airtable as approachable and sometimes literally feel like a spreadsheet.

4:39So that was super important because a lot of the no-code app builders before us or even alongside us had a different layout. They would make you go and define the database schema in one interface that looked more like a data modeling thing, or maybe like defined form-based layouts and that kind of thing. And for us, it was really, really important that Airtable felt as easy to use as a spreadsheet. So the initial version of the product was very grid-centric. And you could use all even the keyboard shortcuts of spreadsheets, So copy paste, you could even copy paste data directly in from a Google Sheet or Excel into Airtable and it just kind of worked.

5:14So I think what we got there was the ease of getting people to shift away from this existing product that everyone uses. And a lot of use cases of spreadsheets really should be databases or apps, right? Anytime you're dealing with something that's not like number crunching and instead is some kind of tabular data, a workflow, like a database like thing of customers or could be inventory, which turns out is like most use cases of spreadsheets. You know, we wanted to make it really, really easy to port over. So that was definitely part of it. And then second of all, I think we later did find that it was really important to build use cases into Airtable.

5:50So templates were kind of our initial version of that. And so in a way, we did end up having to build apps. We just got to build many different apps and each of them kind of represented only a small part of the long tail of use cases we could go after. The platform has evolved from the original very simple experience to something still simple, but much more powerful. The company as well has just become much more enterprise facing in recent years. How did that evolution happen? What did you have to change most to support that? And when did you decide it was time to go do that if there was a decision point?

6:25So that was always part of the master plan. And we actually wrote this vision deck and business plan, or as close to it as we got back in 2012 when we started working on this, that laid this out. And we said, look, generally, it's probably harder to start with a really complicated product. You're not looking at SAP and saying, okay, over time, they're going to make it simpler. Whereas it is very common, or at least more intuitive to start with a very, very simple product and then make it more powerful, customizable, complex over time. Right. So and actually, I think I got this this terminology originally from Mike Krieger.

6:59But, you know, we like this idea of like, let's start with a really low floor, get the floor as low as possible. So we really are coming in and undercutting all the existing local platforms entirely. We're undercutting Salesforce service now. We're undercutting, you know, like these old school products like QuickBase and so on. And it's going to be so much easier to use. But then over time, we can improve the ceiling. Right. And initially, we're going to get some like lightweight, medium weight use cases. But over time, we want to improve the data scale. So actually literally just making it possible to score hundreds of thousands of rows or objects in our table and soon like millions rather than just the thousands or tens of thousands.

7:39And then also kind of adding new layers of extensibility. So literally code extensibility into the platform. So you can write your own integrations code or scripting logic that runs within our platform's serverless environment with access to your data or automations that kind of thing. So a lot of it has been kind of in the works for a while. And I would say in the past few years, we've really kind of been able to lean much more into enterprise, partly because we've already been making these platform investments, right? So I think had we tried to go really hard into enterprise in the first couple of years, it would have been difficult because some of the bigger, larger scale use cases would have just broken the product.

8:19In fact, we did see customers kind of pushing it to the limit. So it took us that time to actually make the platform scalable and robust enough to go after the most ambitious use cases. And then I think the second thing that happened for us is, you know, we started to get enough organic adoption, like the whole product-like growth engine that we had been compounding for so many years in the early days actually resulted in enough usage and, you know, enough high-value use cases emerging organically within the enterprise that we could actually start to lean into those. And rather than having to invent a completely new use case or vertical solution on the product, like some companies do, we always kind of got the cheat sheet within our own customer base.

8:58And so now being able to say, we understand what global content production looks like at media companies, and that we can actually architect an implementation of Airtable to solve that end-to-end process allows us to also So, you know, double down on marketing and selling to that use case, as well as making sure that our platform continues to kind of support it. So I think it sort of was like a petri dish that, you know, had a lot of like blooms of growth, you know, emerging. And then, you know, we just got to look at the areas, the hotspots and say, OK, like now it's time to really double down on these areas and sell more repeatably to them.

9:35Howie, I asked a few people what question, you know, we should talk about that would gain from your wisdom or from the Airtable journey. And Fenton had said, like, you know, his views of product management seem to have changed a great deal over the past few years. Like, I guess, how so and what does that mean operationally? I think product management is, first of all, just like a really hard discipline to do right or to do well. Because I think, you know, a lot of companies have some flavor of it that ends up, you know, only solving for one of the multiple hats that I think ultimately you need to solve for.

10:15I mean, I think, you know, I found it really compelling that Brian Chesky talked about this on a podcast somewhere recently where, you know, this was, I think, misinterpreted. But like, there's a big buzz around the statement that they had made around like doing away with product managers. Right. But what they really meant was they were kind of splitting the role into two constituent pieces and actually making those into explicit roles that were complementary, which are product marketing and then program management. right? And I think those two reflect two really important hats of a PM. So for instance, on the product marketing side, it's really about understanding what is the market for this thing, right?

10:55I think there's a lot of PM functions that are more inward looking and just focus on what are we building? What's going to be hard about it? How do we keep the technical capabilities on track or make sure it fulfills the technical capabilities? How do we make sure that from an engineering sprint standpoint, we're on timeline, et cetera. But that's more of the program management side of things, super important because you have to know what the requirements are. You have to keep on pace against that. But the product marketing side is really interesting because I think it's often neglected in PM.

11:30And it basically is about starting from the customer, starting from the market, saying, who is our competition and what is the problem we're trying to solve. I mean, like the JTBD or jobs to be done, or it was ultimately meant to kind of really put the emphasis on it. But like all frameworks, I think it's like great in theory, hard to actually implement well in practice, right? Like the framework alone doesn't solve the problem. And then I think for us, maybe even more so than some consumer companies like Airbnb, I think we might have like a third hard hat to be worn, which is really around like thinking creatively about more complex UX, when you think about the amount of just pure informational density in our table, or the fundamental complexity of some of the concepts that we're trying to mock.

12:14There's just a certain unavoidable amount of degrees of freedom and complexity and nuance to whether it's like, how do you build these AI primitives or even our existing features like automations and so on. And so, you know, add to the mix like a third bucket. And I think for that third bucket, actually, like the Google and Meta were formerly known as Facebook at the time, you know, kind of PM disciplines did a pretty good job of like cultivating for that. I think in the early days of Google PM, that's when you had so much emphasis on finding people who could think about those kinds of hard design problems, like the informational architecture and how to handle the UX of a more complicated interaction.

13:02So you might put that as a third thing, which almost crosses over into some amount of UX design, et cetera. But to me, Those are kind of at least three of the really, really important buckets. And I think what we've done as a company is started to recognize, even if not to explicitly split out these roles, starting to recognize, first of all, the importance of all three and making sure that someone is covering each of those three. And it doesn't have to be the same person. Sometimes in a group, maybe the design lead actually fulfills more of that third bucket, right? And maybe the end lead fulfills more of the program management bucket.

13:37and maybe the PM fills more of the product marketing bucket, but just making sure that we are thinking with all of those hats on for most things that we build, especially those that demand more of one or the other, right? I mean, there are some functions or features that are a little bit more kind of dry and like kind of more straightforward to build. So maybe we don't care as much about like the product marketing, really understand the customer requirements and market dynamics side. But I would say it's more about like really kind of recognizing PM is not like a single art, just saying, hey, look, we're going to hire PMs who are great at other companies will not necessarily mean success here.

14:15And really to kind of start cultivating our own definition of like, why is product so hard at RT? One way, I was just at an onsite at NVIDIA where Jensen came out and he's always so inspiring in terms of some of his philosophies. although some of them are completely inapplicable, I think, to other companies. To mortal companies. I don't think I can have like 50 reports and take no one-on-ones with any of them. But, you know, one of the really inspiring things was this idea of they love to solve hard problems, right? And in fact, like if a problem is not hard enough, like they almost don't want to go into it because, you know, it's going to be a commoditized space, right?

14:57And it's going to be about other factors that drive success in that market, maybe go to market excellence or so on. And while I wouldn't say we're quite as hardcore as NVIDIA in that regard, like we're not, you know, problems nearly as hard as the scalable compute problems that they deal with. But I do think, you know, part of where we're recognizing more and more that like part of our success and our culture is rooted in solving problems that are uniquely hard from a UX standpoint and from understanding the market, you know, the true job to be done, you know, problem in a novel way where we're not just trying to build a slightly better version of some other person's product, right?

15:34Or like the literal solve for a thing, like, because then we would look more like a vertical SaaS company more than a platform. And so we're now trying to apply a lot of that same philosophy and those principles of product management to how we go and attack AI. And that ultimately will be either our differentiator or we don't do it well enough and we won't be able to win in a big way. But it is the unique take that we have on seeing the capabilities of these models and ultimately thinking about how to productize them into our platform. You folks were pretty early to this AI wave in terms of early iterations and thinking about it and thinking about how to integrate it.

16:13And I think you're always very thoughtful on the product side in terms of like, how do you actually take something and convert it into something that has, you know, real user value? Can you tell us a little bit more about that journey? Like, how did you first become aware of some of the things happening in generative AI? What made you decide it was different from prior ways of ML? And then, you know, how you thought about progressing with it? So, you know, I actually like really was interested in neural networks in college. It was kind of ahead of the current wave of exciting breakthroughs, right?

16:41This was back in like 05 through 09. So still kind of like in the wintery phase, I would say. But ImageNet had definitely not come out yet. This was not the time where we were saying just year after year, there's amazing new capabilities of these models. But at that time, I still found it really fascinating, more intellectually. It just has this academic concept that, wow, instead of having to go and laboriously write all the code to tell the computer what to do, whether it's for interface code or for business logic or whatever, you could just basically have this approach where you tell the data or you tell the computer, here's all the data, here's all the patterns I want you to look at, whether it's just basic Netflix recommendations, engine type things, or in the future, images.

17:27but look at all this data and I just want you to figure out the patterns and I'll tell you like what I want the output to be and you figure out like what the rules should be right and I think I just found it fascinating because it's you know in many ways I think the best or the most curious software engineers are actually fundamentally lazy at heart right because you're trying to find ways to like you know build the meta solve to like solve the thing that you're trying to do right and in a way Airtable is kind of a meta solve for you know application sets right it's kind of the the database, the interface and the logic layer to allow you to build any application rather than if we were to try to go and actually build like a hundred different vertical SaaS products.

18:05And I always found AI to be like, you know, really interesting meta solve to a lot of software, you know, problems at large. And so I think I've just been kind of studying it a little bit from arm's length, you know, over really like, you know, since college and, you know, it was really excited to see some of the image breakthroughs in terms of like convolutional neural nets and be able to like actually you know start to classify images which historically was a pretty hard problem to do as well as humans but like now it could right do it very very cheaply and scalably and in fact i interned at a um at a company called crowdzler which i think you both uh know well um the founder of which lucas now is the the founder of weights of biases um and i think it was like three people or two people at the time that i first showed up in this alley in an admission and they were already starting to do some really interesting work with labeling data that eventually would kind of go towards labeling data for AI applications.

18:59But I found it just kind of interesting to see there, okay, like we are reaching this tipping point of initially a lot of the workloads were about labeling or kind of actually manually just doing image classification or image moderation for say social media companies. And that was the steady state solution. Like it wasn't even about training the AI model. It was about, hey, let's just do this at scale, but cheaply and scalably through this giant farm of basically click work. And I think in the future or gradually, it started to shift to, oh, wait, let's actually use this content to train models that actually now have reached human level.

19:42And so that was one little sliver of seeing some breakthroughs firsthand. And then much, much later, I think as we started to see the text-based applications of Transformer models, I think that became really interesting. And probably just playing around with some of the models like GPT-4 really early on, but also even just the chat as soon as it came out, be able to see the reasoning capabilities there beyond just... I think a lot of people were enamored with like the fun, in this case, it's like composed via sonnet, you know, do these like fun, you know, kind of fanciful things. I was more enticed by the fact that this, you know, it felt like you can actually do some really interesting and meaningful reasoning work, which, you know, is in my view, a massive, massive unlock that still has not been fully exploited even today.

20:31Like I think we could pause model development today and still get a million times more economic value impact from today's generation models than we've fully realized. How do you think about that user impact? Because I feel like there's a lot of false starts when people first start using this. And so did you all go through a similar thing? Were there things that you thought would make for compelling products, then it turned out that's the wrong direction? Or how did you think about what was actually important to do? Yeah, so we are in a somewhat interesting position because not only do we need to think about how to use AI in our product to enhance the product experience, right?

21:08The same way that, let's say, Figma uses AI to make it really easy to design, right? It can kind of co-design with you. It can generate decks now and so on. So we have to think about that way of integrating AI into our own product experience and changing kind of the user experience around it. But separately, because we are a very meta app platform that enables our customers to build their own apps, a big, big part and probably the biggest thing that we're excited about is our opportunity to make it possible for our customers to build AI apps, right? So like taking the meta approach first, I think what's really interesting is that, you know, when we first launched our AI capability in beta, it was really about that runtime capability, right?

21:53So we put out, you know, effectively a wrapper around, you know, the OpenAI models and later Anthropic and so on, But we basically wrapped around it, but then made it really easy to kind of use these, you know, Lego pieces to build an AI call into your data and into your workflows, right? So Airtable is all about having your, you know, first party data in a very usable form and having humans interact with it, collaborate on it, and then perform workflow around it. You know, this made it really, really easy for you to add a workflow step, either as like an AI field that took inputs. Let's say it's taking inputs for a product feature and then generating the first draft of the PRB.

22:33Well, these are things that technically you could have done in ChatGPT separately, but because it's embedded into your data and workflows, it's a lot more recurring and automated, and you can have prompts that are predefined. I think what we quickly learned was that there is a lot of fear and also just intimidation right now, especially amongst enterprise customers, but even amongst the broader B2B landscape of customers, there's just a lot of unknowns around how they can actually use AI. And there's a lot of immaturity of market understanding in terms of how these models work. I mean, not just on a mathematical level, but even just in a basic sense of beyond doing some experimental fun chat prompts into Chat2T, most people don't really understand what they're capable of, right?

23:24Whether it's translation use cases or categorization or even more advanced things like reasoning, right? And synthesis, take this, you know, earnings call on the customer and actually extract really, you know, specifically applicable insights to our sales team about how we can sell to Nike better, for instance. Like, so I think what we've learned is that, you know, this is going to be a really difficult product to just kind of release out there in a horizontal way and hope that everyone just figures it out, right? Even though there are so many different applications, you can apply it to almost any use case, any industry.

23:58I think that the gap right now is in imagination and know-how. So we basically worked on two things. One is we spent a lot of time with specific customers. And we had a thousand customers in our kind of beta before we launched publicly. And since then, we've gotten many, many more. And we've worked very, very hands-on with a lot of these customers to really not just show them how to use the feature, like how to point and click and implement the feature, but really help them understand what parts of their workflow they can automate and even kind of challenge them to be a little bit more ambitious about where they're applying.

24:34So for instance, like a top five law firm is an AI customer of ours. And they're actually coming in with ideas around how to automate a bunch of parts of the contracting workflows, et cetera. But then we want to work with them to even challenge their idea of what they can do. So that requires this level of immersion and design partnership with these customers. And then the second part is much like the whole premise of Airtable being a very horizontal platform, but we realize as easy as it was to just get started with the product, it was also important for us to build the templates and guide people towards use cases.

25:09We're starting to do more of that with our AI, right? So, you know, we've built a bunch of prompt templates that are common to, you know, kind of our most popular air table use cases, whether it's in marketing or product management, et cetera. And I think that doing more and more to automatically infer and suggest and make it like not just easy to implement the feature, but to kind of break through the imagination barrier of how can you use this and what can you use it for is going to be really important. I'm really excited to actually use AI to do a lot of that. You can infer from the contents of an app and the data what the use case is and even then have an LLM suggest what are good use cases for an LLM in this workflow.

25:54What are the questions I could ask against this data set? Yeah, that's super interesting. You mentioned earlier that you feel that if we just froze things in time today, there's all these things that could be built and value unlocked through AI and current LLMs. What do you think are the biggest things that are missing from a technology or functional perspective as you think about how to translate that into product? Like what couldn't you do right now? Or what is missing that would allow you to do a lot more? I think we're all very centered on this idea of the chat interface as the main kind of UX design pattern for LLMs, right?