No Priors Ep. 73 | With Airtable co-founder and CEO Howie Liu

1. 0:00 – 0:29

   Introduction

   1. SGSarah Guo0:00

      (instrumental music plays) Hi, listeners. Welcome to No Priors. Today, we're talking to Howie Lu, the co-founder and CEO of Airtable, which now serves a half 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

2. 0:29 – 2:31

   The Origin and Evolution of Airtable

   1. SGSarah Guo0:29

      in enterprise AI. Welcome, Howie.

   2. HLHowie Liu0:31

      Thank you. Excited to be here.

   3. SGSarah Guo0:33

      Most of our users know what Airtable is, but for anybody who's a new, uh, what does Airtable do and where'd the idea come from?

   4. HLHowie Liu0:40

      You know, Airtable's been around for a little over 10 years, and we launched in 2015, uh, spent two and a half years building the product before then. But for the people who, who knew of Airtable back then, I think, you know, you, you probably would say Airtable is like a spreadsheet on steroids or a really awesome productivity tool, and I think those were, 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. Um, you know, there's the low code app platform category that preexisted us, filled with pretty complicated platforms, um, required actually a fair amount of technical expertise. You had collaboration tools like Trello and then later like Asana and so on, um, that were very easy to use, but were more project management centric. And so we kind of came in and did something in between, which is give people the ability to build real apps with a real relational data structure, you know, logic and, uh, automations and then interfaces, um, but did it in a way that was so much easier to use that it doesn't look like your traditional app platforms. And, um, I got the idea basically by working at Salesforce. Um, I, you know, had a, a very small company before then, a startup that was acquired by Salesforce, and working within, um, their company, you know, just all the power of the platform model, right? Um, you know, realized 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, and sort of make a, a much more accessible app platform that could really open up apps to many more people, citizen developers, um, and use cases than, you know, had been possible before.

   5. SGSarah Guo2:19

      So, very strong conventional wisdom, uh, in Silicon Valley will say, like, "Build a killer app, not a platform."

   6. HLHowie Liu2:26

      (laughs)

   7. SGSarah Guo2:26

      So, like, you know, why doesn't that apply here? Why did Airtable work as a platform from the beginning?

   8. EGElad Gil2:30

      And I, I hate, I hate to

3. 2:31 – 6:09

   Challenges and Successes in Building Airtable

   1. EGElad Gil2:31

      interrupt, by the way, but I've, I, I, 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, I, I remember thinking, "That's crazy. This is gonna be so hard. It's, you know-"

   2. HLHowie Liu2:48

      (laughs)

   3. EGElad Gil2:49

      "... impossible to do as a startup." And of course, he pulled it off, which is, like, amazing. Um, so I think to Sara's point, you really beat conventional wisdom, and you did something really outstanding in terms of building out this broader, this broader platform.

   4. SGSarah Guo3:01

      You know, uh, actually, and now, now I'm, I'm just gonna interrupt you one more time because it is, uh... And we want the answer, by the way. But it is quite funny. I was, um, on a blow up, uh, a friend in common, uh, that I'm sure Elad knows, too, Eric from OpenDoor, and he, um, he was also, we were talking about angel investing at some point, and he was just like, "I really didn't think this thing was gonna work, like, it was so poorly scoped, but, like, Howie seemed good." Um, and, uh, a- and so, you, you know, it's a little bit unbelievable (laughs) .

   5. HLHowie Liu3:33

      I don't think... You know, it turned out, uh, you know, you weren't wrong, uh, any of you. Like, it did turn out to be hard in, in many ways. Uh, 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, um, uh, 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. Um, and 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, 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, the spreadsheet products have paid the cost of, you know, going through that steady march of, of kind of, um, you know, creating a new category, right? Um, and, and getting people to be familiar with it, such that by the time we came in, you know, we, we made a, uh, a really, uh, intentional set of desi- design choices to make Airtable as approachable and sometimes, like, literally feel like a spreadsheet. So, that was super important 'cause a lot of the, the no code app builders before us, or even alongside us, had, you know, a different layout, right? They would, they would make you go and, like, define the database schema in one interface that looked more like a data modeling thing, um, 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 that the kind of initial version of the product was very grid centric, right? And you could use all, even the, the, uh, 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. So, 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 it could be inventory, which turns out is like most use cases of spreadsheets. Um, 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. So templates were kind of our initial version of that, and so in a vers-... You know, 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.

   6. SGSarah Guo6:03

      The platform has evolved, uh, from the original, um, very, you know, simple

4. 6:09 – 9:44

   Airtable's Transition to Enterprise Solutions

   1. SGSarah Guo6:09

      experience to something still simple but much more powerful. Um, the company as well has just become much more enterprise-facing.... in recent years. Um, like, how did that evolution happen? Like, what did you have to change most to support that and when did you, when did you decide it was time to, like, go do that, if there was a decision point?

   2. HLHowie Liu6:26

      So that was always part of the, the master plan. And we actually wrote this, like, vision deck and, and kind of, like, business plan, or as close to it as we got back in 2012 when we, we started working on this, that laid this out. I mean, we said, "Look, like, generally it's probably harder to start with a really complicated product." Like, you're not looking at SAP and saying, "Okay, over time they're gonna make it simpler." Whereas it is very common or at least, um, you know, more intuitive to start with a very, very simple product and then kind of make it more powerful, customizable, complex over time, right? So, uh, and actually I, I, um, I think I got this, this terminology originally from Mike Krieger, but, um, you know, we, 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 low-code app platforms entirely. We're undercutting Salesforce, ServiceNow. We're undercutting, you know, like, these old school products like QuickBase and so on. And it's just gonna be so much easier to use. But then over time we can improve the ceiling, right? Um, and initially we're gonna get some, like, you know, lightweight, medium weight use cases, but over time we wanna improve the data scale. So, you know, actually literally just making it possible to store hundreds of thousands of, of, uh, rows or objects in Airtable and soon, like, millions rather than just, you know, the, the thousands or ten- tens of thousands. And then also kind of adding new layers of extensibility, so literally code extensibility into the platform, um, so you can write your own integrations code or scripting logic that runs within our platform's, uh, serverless environment with access to your data or automations, that kind of thing. So a lot of it has been, you know, kind of in the, um, you know, in the works for, for a while. And I would say in the past few years, we've, we've really kind of, you know, 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 years, it would've been difficult because some of the bigger, larger scale use cases would have just broken the product. In 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. Um, 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-led growth engine that we had been compounding for so many years in the early days actually resulted in enough usage and, and, uh, 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. And so now being able to say, like, we understand what global content production looks like at media companies and that, you know, we, we can actually architect, you know, an implementation of Airtable to solve that end-to-end process, um, allows us to also, you know, double down on marketing and selling to that use case as well as making sure that our, our platform continues to, to kind of support it. So I think it, it sort of was like a Petri dish that, you know, had a lot of, like, blooms of growth, um, you know, emerging and then, you know, we just got to look at the, the areas, the hotspots and say, "Okay, like, now it's time to really double down on, on these areas and sell more repeatably to them."

   3. SGSarah Guo9:35

      Howie, I asked a few people what question, you know, we should talk about, um, uh, that would gain from your wisdom or from the Airtable journey. And,

5. 9:44 – 16:23

   Insights on Product Management

   1. SGSarah Guo9:44

      um Fenton had said, like, you know, his views of product management seem to have changed, um, a great deal over the past few years. Like, I guess how, how so and what has that meant operationally?

   2. HLHowie Liu9:55

      I think product management is first of all just, like, a really hard discipline to do right or to do well. Um, 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. I mean, I think, um, you know, I found it really compelling that Brian Chesky talked about this on a, on a podcast, uh, somewhat recently where, you know, uh, th- this was I think misinterpreted but, like, there was a big buzz around, uh, this statement that, that, that, uh, 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, and actually making those into explicit roles, um, uh, that were complementary, which are product marketing and then program management, right? And, and I think those two reflect two really important hats of, you know, a PM. Um, so for instance on the product marketing side, it's really about understanding what is the market for this thing, right? I think there's a lot of PM functions that are more inward looking and just focus on what are we building, what's gonna be hard about it. Like, you know, how do we keep the technical, you know, kind of, um, uh, you know, capabilities on track or, or make sure it fills the technical capabilities? How do we make sure that from an engineering, uh, you know, kind of sprint standpoint we're, we're, uh, you know, on timeline, et cetera? And, but that's more of, like, the program management side of things. Super important 'cause you gotta, you know, you have to know what the requirements are. You have to kind of keep on pace against that. But the product marketing side is really interesting 'cause I think it's often neglected in PM, um, and, and it basically is about, like, starting from the customer, starting from the market, saying, like, "Who is our competition and, like, what is the problem we're trying to solve?" I mean, like, the JTBD, uh, or jobs to be done framework 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. Um, and then I think for us maybe even more so than some consumer companies like Airbnb, I think 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, like, the amount of, like, just pure informational density in Airtable, right? Or the fundamental complexity of some of the concepts that we're trying to model, right? Um, there's just, like, a certain unavoidable amount of degrees of freedom and complexity and, um, and nuance to whether it's, like, how do you build these AI primitives or, you know, even our existing features like automations and so on. And so, you know, add to, add to the mix, like, a third bucket, and I think for that third bucket actually, um, like, the Google and, uh, uh, and Meta or for- formerly known as Facebook at the time, uh, you know, kind of PM, uh, disciplines did a pretty good job of, like, cultivating for that. Like, I think, you know, in, in the, uh, uh, early days of Google PM, like, you know, that's when you had so much emphasis on finding people who could, who could think about, like, those kinds of hard design problems, right? Like the informational architecture and, like, you know, how to, like, handle the UX of a more complicated interaction. So you might put that as a third thing which almost, like, crosses over into, like, some amount of, like, UX design, um, et cetera. But to me, um, 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...... you know, to explicitly split out these roles. Starting to recognize, you know, first of all the importance of all three and making sure that, you know, someone is covering each of those three. And it doesn't have to be the same person. Sometimes in a group, like, you know, maybe the design lead actually fulfills more of that third bucket, right? And maybe the eng lead fulfills more of the program management bucket. And maybe, you know, the, 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, um, 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. Um, but I would say it's, it's more about, like, really kind of recognizing PM is not, like, a single art. Just saying, "Hey, look, we're gonna hire PMs who were great at other companies," will not necessarily mean success here. Um, and, and really to kind of start cultivating our own definition of, like, what are the... why is product so hard at Airtable in a way... You know, I, um, I was just at a onsite at NVIDIA where, where Jensen came out and, you know, uh, he's always, you know, so inspiring in terms of, uh, you know, some of his philosophies, um, although some of them are completely inapplicable, I think, to, to, uh, uh, to other companies. Um, I don't think I can-

   3. EGElad Gil14:38

      To mortal companies.

   4. HLHowie Liu14:39

      I don't think (laughs) I can have, like, 50 reports and take no one-on-ones with any of them, but... (laughs) Um, but, you know, one, one of the, uh, 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 gonna be a commoditized space, right? And it's gonna be about other factors that drive success in that market, maybe go-to-market, uh, 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, trying (laughs) to solve problems nearly as hard as the scalable compute problems that they deal with, um, 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 a understanding the market, you know, and the, the true job to be done stand- uh, 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? Or, 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 I think that is... you know, that ultimately will be either our differentiator or we don't do it well enough and, like, we won't be able to, like, win in a big way. But, um, but it is the unique take that we have on seeing the capabilities of these models and ultimately thinking about how to productize them, uh, into our platform.

   5. EGElad Gil16:07

      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, and 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

6. 16:23 – 21:55

   Integrating AI into Airtable

   1. EGElad Gil16:23

      first become aware of some of the things happening in generative AI? What made you decide it was different from prior ways of, of ML and then, you know, how you thought about progressing with that?

   2. HLHowie Liu16:31

      So, you know, I, I actually, like, really was interested in neural networks in college. You know, I... like, it was kind of ahead of the, the, the current wave of, like, exciting breakthroughs, right? This was back in, like, '05 through '09 so, so kind of, like, in the wintry phase, um, I would say. Uh, but, you know, and ImageNet had definitely not come out yet. Like, you know, this was not, like, the time where we were seeing just, like, year after year there's, like, amazing new capabilities of these models. Um, but at that time I still found it really fascinating more, more, like, intellectually, um, and just as, like, this academic concept that, wow, like, instead of having to go and laboriously write all the code to tell the computer what to do, right? Whether it's for interface code or for, like, business logic or whatever, like, you know, you could just basically have this approach where you tell the data or you tell the computer, like, "Here's all the data. Here's all the patterns I want you to look at, whether it's just basic, like, you know, kind of Netflix recommendations engine type things or in the future, like, images. Um, but 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, you figure out, like, what the rules should be," right? And I think I just found it fascinating 'cause it's... you know, in, in many ways I think the best, um, or the most curious software engineers are actually fundamentally lazy at heart, right? (laughs) 'Cause you're, 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 SaaS, right? It's kind of the, the database, the, the, like, 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, uh, products. Um, and I always found AI to be, like, you know, really interesting meta solve to a lot of software, you know, problems at large, right? And so I think I've just been kind of studying it a little bit from arm's length, uh, you know, over, over really, like, you know, since college and, you know, was really excited to see some of the image, uh, 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 CrowdFlower, which I think you both, uh, know well. Um, the founder of which, Lucas, now is the, the founder of Weights & 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, uh, in mission. And they were already starting to do some really interesting work with, like, labeling data that eventually would, would kind of, you know, go towards, um, labeling data for AI applications. But I found it, you know, just kind of interesting to see there, okay, like, we are reaching the tipping point of initially a lot of the workloads were about labeling or, or, you know, kind of, um, actually manually, uh, just, you know, doing image classification or image moderation for, say, social media companies. Um, and that was the steady state solution. Like, it wasn't even about training the AI model. It was about, hey, let's just, like, do this at scale, but cheaply and scalably through this giant farm of, you know, basically click work, right? And I think in the future, or, or, uh, you know, gradually, it started to shift to, oh wait, let's, let's actually use, you know, the- these, um, uh, this content to train models that actually now have reached human level. And so that was one little sliver of, of seeing, like, some, some breakthroughs firsthand. Um, and then, you know, much, much later I think as we started to see the, the text-based, um, applications with transformer models, I think that became really interesting and, you know, like, probably just playing around with some of the, the, uh, the models like, you know, GPT-4 really early on, um, you know, but also, like, you know, even just the, the ChatGPT as soon as it came out. Like, be able to see the reasoning capabilities there beyond just, I think a lot of people were enamored with, like, the fun use cases, like compose me a 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 could 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. Like, I think we could pause model development today and still get a million times more economic value impact from today's generation of models than we've fully realized.

   3. EGElad Gil20:40

      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, did you all go through a similar thing where the things that you thought would make for compelling products, then it turned out that was the wrong direction? Or how, how did you think about what was actually important to do?

   4. HLHowie Liu20:57

      Yeah. So we are in a somewhat interesting position because, you know, not only do we need to think about how to use AI in our product to enhance the product experience, right? The same way that, let's say, Figma uses AI to, you know, make it really easy to design, right? It can, like, you know, kind of co-design with you. It can generate decks now and, and, uh, and so on. So we have to think about that way of integrating AI into our own product experience and changing kind of the, 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, in beta, it was really about that runtime capability, right? So we put out, you know, effectively

7. 21:55 – 30:30

   The Future of No Code and AI

   1. HLHowie Liu21:55

      a wrapper around, you know, uh, the OpenAI models and later Anthropic and, and, uh, so on. Uh, but we basically wrapped around it, but then made it really easy to kind of use these, you know, Lego pieces, uh, to, to get, build, um, an AI call into your data, 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, um, 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, you know, kind of, uh, uh, inputs for a product feature and then generating the first draft of the PRB. Well, 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, you know, kind of recurring and, and automated and you can have, you know, prompts that are predefined, right? And, you know, I think what we quickly learned was that there is a lot of fear and, and kind of also just, like, intimidation right now, especially amongst enterprise customers, but even amongst, you know, kind of just the broader B2B landscape of, of, uh, you know, customers. Um, you know, there's just a lot of unknowns around how they can actually use AI. And, you know, there's a lot of immaturity of market understanding in terms of how these models work. I mean, not just like on a mathematical level, but even, like, just in a basic sense of beyond doing some, like, experimental fun chat prompts into ChatGPT, most people don't really understand what they're capable of, right? Whether 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, uh, what we've learned is that, you know, this is gonna 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?

   2. NANarrator23:53

      (laughs)

   3. HLHowie Liu23:53

      Even though there are so many different applications, you can apply it to almost any use case, any industry, I think that the gap right now is in imagination and know-how. Um, so we, we basically worked on two things. One is we spent a lot of time with specific customers, and we had 1000 customers in our kind of beta before we launched publicly. And since then we've gotten, you know, many, many more. Um, and, uh, 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, and implement the feature, but really help them understand what parts of their workflow they can automate and e- e- even kind of challenge them to be a little bit more ambitious about where they're applying it, right? So for instance, like a top five law firm, um, is an AI customer of ours. And, you know, uh, they're actually coming in with, with ideas around how to automate a bunch of parts of the contracting workflows, et cetera. But then, you know, we want to work with them to even challenge...... you know, their idea of what, what they can, right? So that re- requires this level of immersion and kind of design partnership with these customers. And then the second part is, you know, much like the whole premise of Airtable being a very horizontal platform, but, you know, we, 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 these cases. Uh, we're starting to do more of that with our AI, right? So, you know, we built a bunch of prompt templates that are common to, you know, kind of our most popular, uh, Airtable 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, 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 gonna be really important. And, you know, I'm, I'm really excited to actually use AI to do a lot of that, right? You can infer from, you know, the, the contents of an app and the data what the use case is, and even then have, uh, an LLM suggest what are good use cases for an LLM in this workflow, right? What are (techno music plays)

   4. EGElad Gil25:56

      Yeah.

   5. HLHowie Liu25:56

      ... real questions I could ask against this dataset?

   6. EGElad Gil25:58

      Yeah. That's super interesting. I mean, and 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, as you think about how to translate that into product? Like what, what couldn't you do right now? Or what is the, what, what is missing that would allow you to do a lot more?

   7. HLHowie Liu26:19

      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? And it's no surprise. I mean, ChatGPT was kind of the thing that broke through and made this mainstream and even kind of, like, escalated the world and, and every enterprise's attention, um, and urgency around, you know, uh, the, the, what, what these LLMs could do. Um, but yet I think while chat is really powerful and very open-ended and you see a lot of companies building RAG use cases against their own internal data. It could be HR data, um, so that now any employee can have, like, an AI HRBP to ask comp or, um, you know, or benefits questions too. You know, you see companies that do that with, like, internal data around, like, product development or whatever, just to make that, uh, information more discoverable. So I think those are great use cases, but ultimately I see them as just one small sphere in the broader, you know, kind of, um, you know, town of potential applications for AI. And I think a lot of the, the, uh, more interesting use cases are those that involve some kind of structured recurring process and being very deliberate about saying which parts of that process can you automate. Now, I think this is happening in certain very narrow, uh, solution domains. So obviously support has been a really great one. Um, you have companies like Decagon, like, they're going and trying to tackle the end-to-end support automation problem with, with AI and kind of taking an agentic model, uh, to do so. But I think that, that, you know, you'll have some use cases addressed through solution companies that can be very big, um, you know, uh, each individually. And yet, you know, I, I still think even if you added all of them up, they're still tapping into such a small fraction of, you know, all of the actual departmental workflows and processes that are happening within the enterprise. So, like, to, to attack more of those, I think you need an approach that looks more like an Airtable or on the very heavyweight side of things, a Palantir, which is going in and doing these, uh, AI bootcamps, AI workshops and, like, in a very Palantir-y kind of way, like, going very bespoke and, and very, um, hands-on in terms of actually building, you know, an AI process, uh, or AI automated process for a customer. Uh, but I think, you know, there's gonna be, like, very, very heavyweight use cases you can do that way or you can do with your own in-house teams. Um, but then there's, there's still a massive long tail, uh, that an aggregate, I think, is like, you know, trillions of dollars of economic, you know, value or labor value, um, equivalent that is just waiting to be, uh, solved for with, you know, a platform that has data, that has workflows, that has human-in-the-loop capabilities and yet also allows for really flexible modeling of, you know, where do you put in the AI? What are the inputs? What's the prompt? Um, how do you define the outputs in a way that is resilient to error so that you can have humans take the output, edit, approve, and then chain that with other either LLM calls or human steps or automation steps?

   8. SGSarah Guo29:12

      When y- you were doing this, uh, m- you know, yearlong beta with 1,000 customers, um, and trying to give them some, like, obviously work on real, um, workflows, structured recurring processes, as you said. Um, you know, did you, did you figure anything out about how to give other people ... Like, you've been looking at this for a long time. Uh, any frameworks for how to give people intuition for what today's models can do, either, like, in Airtable, 'cause you guys have to have the expertise, or in your customer?

   9. HLHowie Liu29:41

      The, the short answer is we've been trying, uh, to do a number of things to, to kind of codify that and, and scale it beyond, like, one-on-one bespoke, you know, kind of, uh, interactions, right? 'Cause we can't be like Palantir and go really, really, you know, forward deployed for every customer. Um, so one is we actually now run this AI workshop program. It's a lot lighter touch than, like, the Palantir AI bootcamp. Um, but, you know, for instance, we just had one in LA. We had like, you know, probably 60 people from all kinds of companies, a lot of media companies, some, like, retail, big, like, retail companies, uh, et cetera. And it's a full-day kind of masterclass in first, you know, really just teaching people, like, what are these transformer models? Why, why have they gotten so much better recently? I mean, looking at literally, uh, this slide of, you know, parameter count of these models from, like, five years ago to now, from GPT-1 to

8. 30:30 – 36:28

   Workshops and Training for AI Adoption

   1. HLHowie Liu30:30

      GPT-4, and obviously parameter count is not the end all, be all, and now, you know, smaller models are actually doing really well. But I think it, it just kind of illustrates to people, like, what is this thing that now everybody's talking about and why now? Is it just a fad or is there, like, a real foundational kind of technology, uh, you know, kind of improvement, sort of like with the AD86 processor, that has made this the time to actually pay attention and care and, like, there's no turning back now, right? Um, all the way through to some basic prompt engineering techniques and then also showing them some use cases that basically are approximated from real customers. Um, and then, like, you know, what are the different kind of...... uh, AI design patterns in, you know, in workflow automation, right? So if you think about, like, in computer science or software engineering, you have different good design, uh, patterns for implementing certain types of business logic. Um, like, you- you have the observer pattern, for instance. Like, you know, these are different ways of shaping the code to solve a certain archetype or blueprint of a business problem. Uh, we're kind of doing the same with, you know, these AI workflows, right? So here's like a chained workflow where, for instance, for translation, we've actually found that creating a pipeline where, you know, the first AI step generates a first effort at, uh, translating, you know, whatever it is. And then the second step actually critiques itself and finds potential errors, and you can have a third step where either a human reviews it or the AI tries to take its own edits, uh, from the second step and- and applies it to the first, right? But, you know, we're starting to emerge these patterns that we've found actually result in much better quality, right? Or- or getting to the desired business outcome. Here's how you solve for that, right? So part of that, uh, part of this is, like, we're doing these training programs. Um, right now they're in person. We're digitizing a lot of this content, so we wanna have, like, this Airtable Academy that's not just about how to use Airtable AI. But also, if you go through it, you actually learn a lot about how to, you know, use these LLMs in general, right? And theoretically, you can apply that to, you know, other custom AI, uh, application building with code, not just through Airtable. Um, and then the second is, we're trying to productize more and more of it, whether it's in the form of creating more prompt templates that, you know, obviously just kind of reflect the different use cases we've seen. And, um, and we try to, you know, kind of, uh, uh, prompt engineer these prompts with, you know, kind of the- the best prompt for that, uh, for that need, right? Uh, but then also, we're- we're thinking of and building more advanced primitives. So the- the initial primitive was, you know, transparently a wrapper around an LLM call. And the real value add for us is that we're not just a wrapper like one of the, you know, one of the one-off content generation apps. But, like, it's a wrapper that then allows you to embed the model call into the context of data and workflows and automations. So there is, you know, unique value there. But what we're trying to also do is, like, make that primitive more and more robust. So for instance, something we're working on is adding, you know, kind of a more native capability to do many-shot prompting in Airtable. Which kind of makes sense, right? We have all the data, so you could say, "Here's, like, five examples of a, um, of a product requirements document, like a PRD, that I wrote or my teams wrote that are really great PRDs given this feature and these customer insights and so on. Now, based on these five examples, help me generate a draft of the next one based on the- the cu- customer inputs or, like, the ideas or notes or whatever, whatever may be the inputs," right? And what we can then also do is have it learn over time, right? So we have the opportunity to build a great RLHF, you know, feedback loop that instead of having to get applied through a fine tuning run, you can actually just start stuffing more many-shot examples into the prompt. Um, and so those are some of the things that we're excited about building to kind of make it easier and easier and more native in the product itself to improve the- the, uh, the AI capability and also, you know, kind of make it easier to use. Um, we're also thinking about, you know, like I said before, how do we best show you ways to implement AI in Airtable? So, um, not only will we in the future recommend, "Hey, have you thought about adding this AI field? Because based on the content type in this table," like this is contracts and we can infer that, "maybe it would make sense to ask to extract these terms." Like if they're VC term sheets, like, you know, what- what was the, uh, post-money valuation, right? What's the amount raised? Like, extract that out automatically. And, you know, if you take that to the limit, you could imagine new onboarding flows for Airtable where you actually start with, like, a content source, right? Like, maybe it's a document folder from, like, G Drive or Box. Maybe it's, um, a bunch of call transcripts from Gong, you integrate with Gong. And then all of a sudden you get this, like, beautiful tabular display with the ability to create workflow and interface around it in Airtable that now can have these AI extractions or steps so, you know, you can start rating every Gong call from your sales team or extract, like, every competitor that's been talked about for whether it's marketing or product or- or kind of sales oversight use cases. Um, we're doing a lot of this stuff through manual builds right now, both for ourselves as- as customer number zero and also working with our customers to do so. But as we start to see those patterns emerge, we want to make more of it- more and more of it easily buildable through our very same gooey no-code kind of UX that we've always been good at.

   2. SGSarah Guo35:39

      One- one thing that you said at the beginning is that, like, kind of the no-code, um, enterprise app platform category is the thing that came before Airtable, and to some degree you're that. Um, how does it change your thinking about Airtable now that code is becoming easier to generate?

   3. HLHowie Liu35:56

      Well, um, you know, will code gen basically replace the need for vertical software? And will it replace the need for no code? Because now, like, even code is so easy to generate. And I- I have a very specific point of view on this which is, you know, I think, um, you know, sure you can generate small snippets of code very easily and maybe that's getting better and better with the more advanced models. Um, you know, I think code, uh, is obviously one of the core capabilities of all these LLMs and I think it's- it, you know, has some nice properties of being, um,

9. 36:28 – 41:25

   The Role of Code Generation in No Code Platforms

   1. HLHowie Liu36:28

      you know, simulable so you can, you know, you can actually do a good job with synthetic, uh, data and training, uh, on it. And there's just so much on the corpus of code out there that- that, uh, you know, there- there's some really interesting things you can do with training it and making the models better. Um, and there's some really interesting innovations happening out there, right? With- with, uh, not just the big companies but, like, the startups, like the Madgics and the Plu sides and, you know, so on of the world. Um, that being said...And, you know, this may come down to as much a religious debate as how close are we to AGI. I think it's gonna be fundamentally hard, like really, really hard, to generate really sophisticated end-to-end process automation type apps. So if you think about, like, a bespoke solution for content production. I mean, so it's basically like an ERP for digital content production at a company like a Netflix or like an NBCU, et cetera. It's very complicated, right? There's so many different steps. There's so many nuances to the business logic, to the data modeling, et cetera. And I think that code gen makes a lot of sense as an augmentation to human developers, because the output is easily inspectable and steerable by the developer, right? You can just basically look at it. You can, like, re-prompt it. You can edit it and take it over. Um, you kind of understand what the code is, right? In the same way that, you know, if you're generating text output or knowledge output, you can look at it and, since we all know English, you can reason about it and say, like, "Oh, I don't actually like the idea that you came up for me, uh, for this marketing campaign." Right? If- if that's what you're generating with- with, uh, text. I think, um, for app development, if you want a non-developer to be able to have that same interactiveness with- with the, uh, the code output, it's gotta be, you know, outputted in a format that they understand, right? And by definition, that is no code. Where when an app is generated in Airtable, um, and we're actually about to launch, like, this in a pretty exciting way... But, you know, when an app is generated in Airtable, the idea is that you can immediately, as a non-technical person, understand what's going on. You can see the data schema. It's not like a alter table or migration script in SQL, that a non-technical per- person would have a really hard time understanding. Um, and even the business logic. I mean, it's literally, you know, uh, built in a very human, readable way, right? We have an automation UI that feels like if this, then that, right? Uh, where you can see a very visual flow diagram of, hey, here's the logic, conditional logic, et cetera, um, for this automation logic. And then even the interface layout is very easily inspectable, right? And if you wanna, like, override it, modify stuff around, instead of having to, sometimes frustratingly, re-prompt, uh, the LLM to, like, you know, take what it generated and, like, you know, refine it, you- you just wanna go and, like, directly manipulate the thing, right? Um, so, um, you know, I'm just very strongly of the belief that short of AGI, I think we're gonna have a really hard time having fully automated, um, you know, code gen agents that replace the need for no code, because you actually want to generate the outputs in no code, because for a long time it's gonna be about augmenting the human, you know, kind of, uh, creative director of- of the, uh, of the app. The architect, if you will, of the app, or at least the business requirements definer of the app. And, um, and without a professional developer in the loop who understands the outputs and can kind of guide it and refine those outputs, um, you're gonna need to- to generate the outputs in no code.

   2. SGSarah Guo39:47

      Yeah. That makes a lot of sense to me. Um, y- you know, even if code generation gets much better, it doesn't really help non-technical folks if the generated code to them is opaque, right? Because, like, software is just such an iterative process. Like, you even have this issue where, like, you know, a requirement is communicated and the developer was like, "I thought you meant X." Right? And so if developers can't zero shot app- like, you know, professional developers can't zero shot applications, it seems unlikely that the non-developers will be able to, right? Like, how would that magically happen?

   3. HLHowie Liu40:17

      Yeah. And I mean, you can have, you can imagine, like, a, you know, a Codium style, uh... Or not Codium, um, Cognition style, you know, workflow where it's like, okay, it's gonna, you know, generate the spec for the thing, and then it's gonna try to, like, build tests for it, and then it's gonna try to generate the code that passes the tests. Um, so I think that works, again, for, like, simpler use cases. But when you think about, like, really complex business applications, there's just so many nuances there that unless a human is kind of inspecting the requirements along the way and then kind of, like, giving feedback on, "Wait, no, that... You got the logic here wrong," or, "You got the interface here wrong," um, in many, in a very iterative fashion along the way, um... And to have the ability to, I think, directly manipulate and edit it in a very precise way, um, I think it's gonna be very challenging to generate apps of any meaningful complexity.

   4. SGSarah Guo41:01

      Uh, Howie, this was a great conversation. Thanks for doing it.

   5. HLHowie Liu41:03

      Thank you so much. This was fun.

   6. EGElad Gil41:04

      Thanks. Good to see you.

   7. SGSarah Guo41:07

      Find us on Twitter @nopriorspod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.

Episode duration: 41:25