From no-code to AI-powered apps with Airtable’s Howie Liu

You're listening to Gradient Dissent, a show about making machine learning work in the real world. And I'm your host, Lukas Biewald. In this interview, I'm talking with Howie Liu, an old friend of mine and the CEO of Airtable, a very successful app building company for businesses. I first met Howie when he was just out of college and he interned for me. And even at the time, it was obvious he was one of the smartest people thinking about product and business. And now he's become one of the smartest people I know thinking about the intersection of product and business and AI. So it was one of the easiest interviews I've ever prepped for. I had so many questions for him, and I hope you enjoy this interview as much as I enjoyed doing it. All right, Howie, good to see you.

Awesome.

Thanks for doing this. Thanks for being on the podcast.

Yeah.

Um, you know, I feel like most of the people watching this or listening to this probably know About Airtable and what it does, but maybe just for the record, or maybe for those who don't know, if you could just describe Airtable today and the vision behind it.

Yeah, for the record, it's actually the name of our blog because Airtable records for the record.

Nice.

Yeah, so Airtable was founded with a very clear premise, which was I had worked at Salesforce for a year and a half before, had sold a small startup to them. And while at Salesforce, learned 2 really important things. One is, you know, the startup I had was kind of this small disruptive CRM product, had a bunch of cool new features like integrations with social, etc. And, you know, obviously Salesforce is this, you know, CRM giant, right? Like they just dominate and they, you know, even at that time had multiple billions of revenue, but they were already the clear winner. And so first, what I learned is that Salesforce is so dominant at CRM, not because they built all these fancy bells and whistles for CRM specifically, They have some of those, but the even more important premise is that they are a platform, right? So they can be completely customized for any use case. The data model is custom to every customer. So it's not just always the same setup of contacts, accounts, et cetera. So platform, you know, being powerful, that was one takeaway. And then I think the second is, you know, going after a really, really large market, in their case CRM, and then later, you know, adjacencies to the sales CRM market. just lends all kinds of opportunities to continue to innovate and kind of drive, sustain like this engine for being more ambitious over time. So it's a self-fulfilling kind of prophecy. And I took both of those away in the founding of Airtable. One, because we wanted to build an app platform that was just radically easier to use, right? So Airtable is designed to enable people who are actually in the business, whether it's a marketing person or a product person, et cetera, to build a really useful app. But with this platform approach where we're not going out and prescribing, hey, here's exactly what the solution for every single possible use case will look like, we're enabling people to build their own apps and define their own data model and create their own workflow around that. And then secondarily, the vision has always been about starting small, as is the case with the typical low-end disruption, but moving up and up and up and up into more and more high-scale, high-complexity, high-value use cases, which we now have done. So, you know, kind of the condensed, you know, 10-year-plus history of Airtable is we started with a disruptive, you know, really, really simple, easy-to-use no-code app builder. And since then have been scaling up to support really large, you know, kind of transformative end-to-end processes like content production at the world's largest media companies, which is a very multi-stage, kind of almost ERP-like process, but instead of for physical assets, for in their case, content or digital assets. And that's a very common type of pattern we've seen even in other industries like retail, tech, financial services, et cetera. Interesting.

That's different than what I thought you were going to say. I feel like maybe I'm out of date or something. So let me repeat it back. So Airtable is like an app building platform. Is that fair? And is that all? Because I remember in the early days kind of watching you build it and sort of thinking, this looks like almost like a way to have a database without having to administer a database was kind of my early experience of it. Is that wrong or?

No, that's correct. And I think what we really centered on is that The types of apps that we're focused on are very database-centric, data model-centric, right? So Airtable is for internal business apps, not for external, like you wouldn't use us to build a game. You wouldn't use us to build like the next high-scale consumer app. You're not going to build like Instagram using Airtable, right? But what you are going to do is build internal use cases that look like CRM, that look like ERP, that look like inventory management. And it turns out having a database-centric platform where now anyone, the business person who's closest to that use case and process can actually define the data model they want. They can say, this is the schema I want. It's not projects, it's not tasks. It's really about representing this object. It could be, again, your unique inventory. It could be your unique type of content assets. That's the starting point. And I think if you look at traditional programming, I always found there to be this big unlock when I personally shifted from early days of my programming career, thinking about logic first and writing C++ code and doing Monte Carlo simulations and just logical stuff, to instead thinking about useful app development. And usually the starting point of that was like, First you define the data model, right? And like Ruby on Rails, often you start with the ORM, the object model, and say, here's the objects that I want to work with.

Interesting. I remember also when we were first talking about Airtable before I even saw it, you were talking about your love of HyperCard, which is something I remember you hacking on as a kid. Is there any of that influence still in the Airtable product?

I mean, you're revealing just how cool I am. Some people talk about sports, some people talk about their cool hobby, and here I was talking about HyperCard, this 30-year-dead product that was really cool and ahead of its time, right?

Totally.

And by the way, I do want to give a shout out to the backstory, which is the way you and I met. My first job out of college, and I had done a lot of tinkering, programming, knew I wanted to do a startup, even then was interning for your last company, CrowdFlower. It was kind of like an early pioneer in a lot of what would become this AI world and training data and so on. But I think even then, something that really excited me was this idea of solving the meta problem behind building apps. I had personally been tinkering on lots of different apps. I think I even built maybe one or two in the context of being your intern. And then even past work I had done while programming, a lot of it was building some kind of app. Actually, at one previous internship while I was in school, I worked for this ad tech startup and I actually ended up realizing that the most valuable thing I could do during my summer there was not the job they had intended for me, but in fact building them this custom asset manager, content management system for them, for creative assets as they were going out and pitching their brand customers. And what I ended up building was basically this database with a layer on top of it. And the point of all this is that I've always tried to step back and think about what's the underlying abstraction that can save me the effort of having to make each and every one of these apps. And I think HyperCard was like a really early and kind of like profound product in that like they really tried to make, you know, app development very accessible. It was kind of maybe one of the first no-code products because while it felt like a spreadsheet or not a spreadsheet, a slideshow product, like you created different cards, you know, there were underlying data table concepts and you could kind of create logic. And if you click this, it would go to, you know, this page. And so there was this like interface layer on top of a data layer and some logic in between that as a kid, I remember building some really interesting interactive apps with. If you actually go back and look in the historical archives, there's videos of people who have talked about, I'm a car dealership owner and I built my entire inventory management and CRM system on HyperCard. So just an early example of how if you design a really good and flexible platform, People end up figuring out how to solve lots of different business problems with it.

I guess one more question on the history of Airtale before I get into what you're doing now. One thing that I watched, you know, kind of from the sidelines was it took you a long time before you did a public REIT. And it was kind of interesting because it was your second company and it seemed like you actually slowed down the process before putting something out into the world, just so contrary to the the advice that you normally get. Can you talk about like, what was the thinking behind that? And maybe like, would you do it again the same way? Was that instrumental in your success?

Yeah. So, you know, the, so to kind of reflect on your point, my first company lasted exactly a year, almost to the day from start to acquisition, right? By Salesforce. And Airtable, to your point, took probably 2 and a half years to even build the GA product that we could launch publicly. So like the entire span of my first company was like not even the time it took us to build and launch, you know, the second product, right? Or the second company, Airtable. And I think there's a few reasons for this. One is, you know, when you don't have much conviction about the market and, you know, kind of your unique take on the market, I mean, ultimately like creating product market fit, is about having a differentiated point of view on how real-world problems can be solved with a new offering. If you're doing the same thing as everybody else, by definition, you're not going to really gain entry or be disruptive. And so you have to have a fresh take on the problem and maybe even what the problem is itself. And I think with my first startup, eTacts, I don't think we really had that much conviction about it. I mean, we thought We had an inkling of it. There were some interesting trends emerging around lighter weight CRM, CRM that could incorporate automatic intelligence around social media profiles, et cetera. But if you said to me, hey, you gotta bet your entire future livelihood, your entire next 30 years of your career on this product idea being the one that's gonna just be right and make it, I would've said, okay, well, jigs up. I give up. And to some extent, that's kind of what happened, right? Like a year in, we had made some traction. We had gotten so far and I think done some interesting things and actually started to monetize that product, but also kind of realized there was a lot we didn't know. And so having the humility to even admit to yourself, hey, I don't know what I don't know. And so going into Salesforce, I both learned about and kind of formalized my thinking around this platform opportunity. But then also kind of got a glimpse into a company. It was the first big company I ever worked for, right? A company that had really kind of reached takeoff trajectory, right? And probably at a scale of like a couple billion in revenue, but still early and scrappy relative to like the huge, huge giant they are now. And I think that also gave me some conviction of seeing what is a company that has made it and has figured out the market and and now have this massive flywheel of momentum look like? And so going into Airtable, I think my approach was, look, if I can like be very deliberate and thoughtful about the product bet that I'm making and, you know, really invested in creating a differentiated and, you know, hard to replicate product, then, you know, sort of similar to like building a rocket, like if you're SpaceX, you know, I know that once we do it, There is a market for it. I understand the use cases and the pain point. And I also believe that we can create this flywheel so it gets easier over time and we get more leverage, right? Versus I think there are some products where it's faster to build and launch, but then you have less durable advantages over time, right? And you could be a flash in the pan taking advantage of kind of like a near-term, you know, especially in AI right now. I think there's a lot of like, you know, cool products that ultimately become short-lived because it's more of like kind of a cool fatty AI tourist experience, like all the avatar products where, I mean, they're fun and cool, but it's hard to imagine kind of a durable flywheel compounding from there.

I guess what you've defined your product very broadly. So I guess in some sense, some kind of app building tool surely would be successful. But what gave you like the, what gave you like the confidence to, like, what gave you that confidence? Was it sort of seeing Salesforce customers? Like, I do remember talking to you about like, wow, like, you know, Salesforce has all this like missed opportunity here by not like making things like more abstract so that customers could build these types of apps. Is that where it was coming from?

I think there are a number of different insights that added together gave me that overall conviction. It's kind of like your Bayesian priors. If you add enough of them up, you get a total confidence that's higher.

Sure.

And so in my case, it was partly seeing some of the insights from the ways in which Salesforce was really strong and also the ways in which it was underserving use cases that were more away from CRM, traditional CRM. The further away from that you got, the harder it was to kind of, you know, there weren't many good solutions to build like a, to stand up an app, right? And aside, like, you know, as I explored some of these app ideas, like inventory management for SMBs, let's say, like there were very crappy, kind of not very flexible or scalable custom solutions or vertical solutions, right? Like there were entire companies that just focused on that one use case, or it could be like donor fundraising, right? There were actual products that just focused on this. And by the way, we would later encounter a lot of these and like kind of subsume, I think, the micro markets for a lot of these after we launched. Like one of my favorite pieces of press to this date was the Idaho Farm Bureau had an op-ed in it that referenced how Airtable was the new CattleMax. And apparently, like if you Google CattleMax, it actually was this like verticalized software for just cattle tracking. It was like a cattle tracking database-powered app, right? And so like, There were just a lot of these very brittle, narrow niche markets that I think were being poorly served because there wasn't this flexible app platform that had, in our case, also a lot of modern innovations like real-time collaboration, a mobile-friendly layout, and just this high degree of UX, ease of use that was reflective of a new generation of software. I will note also that I think the timing was right. I think a lot of business ideas you know, are sometimes about the timing, right? Like Webvan predated Instacart by at least what, like a decade? And that timing was wrong, but Instacart's timing clearly was correct, right? And so I think with Airtable, we started around the same time as Figma. And I think what we both benefited from was both a shift in consumer or, you know, even like business end user expectations for their software. They wanted it to be more flexible, better, UX, web-based, and also real-time collaborative. And then second of all, I think the technology to build that kind of software became more accessible and also potent, right? Like we were able to go and build a web-native product that felt like a desktop-grade experience using a lot of the benefits of like HTML5, WebGL, some of the like just modern standards in web that, you know, in my prior company, even just a few years prior, were not possible, right? Were really, really hard. Even the real-time backend element and creating our own proprietary database engine would've been much harder, I think, 3 or 4 or 5 years prior. All that being said, we also didn't just lock ourselves in a room for 2 and a half years and build this product hoping it would work. I think we were doing a lot of market validation even without a fully launched product in between. So early on, it would be taking a wireframe prototype to some target customers, users, often in our friends and family network, and showing product mockups to them and saying, hey, do you think you could see yourself using this for XYZ use case? And so we were doing use case discovery and trying to find the jobs to be done specifically for this product even early on. And we would basically do that at every phase. At some point we built a backend, It was kind of a very flimsy backend or intentionally just kind of a prototype backend, but it was enough to start testing it for even small solo use cases. So I think we had a few customers, if you could even call them that, beta testing this early, early version. And then eventually we'd get more robust use cases even before launch on our beta. So I think at Everycoin, we were able to validate A, people needed this kind of an app platform and that the UX that we had designed around it made sense to them. B, that they could actually fully implement a valuable use case once we had built it because the theory versus the reality of these things sometimes is not the same. And then C, once we launched, I think we figured out there actually was enough stickiness and virality to this product that you could create this compounding growth flywheel from it.

And I promise we'll get to the AI soon, but I do have some other questions. I remember I hadn't seen you in a couple of years, maybe like 5 years, 6 years, I'm not sure. And I was at an investor conference and I watched you present this chart of your user numbers that looked like a mathematical function or a caricature of— it was just this beautiful, smooth Exponential curve that I was just so deeply jealous of that. Was there any, along the way, has it been this sort of smooth rocket ship to success like it's looked like from the outside? Or have there been sort of pivotal moments or doubt in your strategy?

Yeah, I mean, so there's definitely been hiccups for sure, but even the early days growth, I would say, First of all, when you zoom out enough, everything looks like a smoother distribution or like a curve than if you zoom in. You zoom out and there's normal distributions and there's growth curves and so on. But the truth is it was probably very chunky and there were different inflection rates throughout time, even from the earliest days. And I remember—

I'm going to ask the investor to send me that graph.

It would be pretty sweet. It's like 10 times, right? And look at this section, that section. It's just like, it's always fascinated me, this like the statistical concept of like, when you zoom out enough, like things start to like smooth out. But yeah, I mean, like living the experience of like the day-to-day and the kind of like month-to-month, like, you know, challenges we have as a, you know, as a company, like trying to gain that momentum. I mean, you know, early on, we got some early traction right when we launched and there were a lot of people who were enthusiastic about the product. But then we quickly realized we had to, you know, like build easier ways to bring data into Airtable, right? So like, I don't think we launched with even the ability to import spreadsheets, but that was a fast follow-up, right? Because, because it was obvious that people needed to easily be able to bring in their data from existing spreadsheets or tools into Airtable. Or for instance, like quickly building out templates, because as we started to see people come in, some people figured out like, here's how I build the use case I have in my mind into Airtable. but other people needed to see a starting point to react to or to edit. So there were definitely a lot of moments where I think had we not done those things, the growth probably would've linearized. And I think probably a lot of companies whose zoomed-out growth looks very smooth actually in the moment had to execute in ways that unblocked the smooth continuum of growth. It's almost like if you have an organically multiplying organism, like single-celled bacteria or something, if you don't give it enough of its necessary inputs and nutrients, it's going to level out at some point. So I think it feels like you're constantly trying to find the level-out point and figure out what is going to cause that and then unblock it. And so that could be these activation techniques, these ways of inspiring people with the specific use cases. At some point, we made this transition into more of an enterprise-sold growth model that layered on top of and supports the original bottoms-up product-led growth engine. And I think even now, as we transition into becoming this enterprise-grade platform that can solve really large spanning use cases, That means that from a revenue curve standpoint, we can continue to really compound our revenue because if we win these large enterprise-spanning use cases that are very valuable, they're worth multiple millions each. And that's something we've really been focused on. We can compound our biggest customers' value by basically 2x every single year. So this is one of the most important, more recent stats that has helped us continue to compound the growth. But you switch from just getting lots and lots and lots of small customers at the top of the funnel to keep streaming in to instead you still get them, but then you can also double down on and really compound the biggest deployment. So I think it's always been about getting ahead of the plateau point because every, I think every system, every type of product or organism. And in a way, like a company is sort of like an organism, like it's trying to grow and consume more to like then like sustain itself to keep getting bigger. And, you know, but at some point you're going to like run out of like the space it needs to grow unless you go and like figure out how to like unlock it. So it's definitely felt like that. And, you know, certainly each of the transitions that I described in the moment was like actually more complicated or required more like alignment and sometimes like, you know, not a pivot, I would say, but like a refocus of the organization internally to be able to fully deliver on.

And so is the transformation that you feel like you're going through right now, this transformation to accommodate more complicated workflows for bigger enterprises, is that right?

Yeah. And I would say it's not just about complexity because sometimes there's like highly complex workflows that are sort of esoteric, right? Like you have a really complicated financial planning use case in finance that is just very complicated, but not enterprise-wide spanning. And so the types of use cases we're really interested in basically kind of look like the modern equivalent of what ERP was and SAP was to a supply chain, physical manufacturing-centric world. To now, obviously the supply chain that matters is more of a digital one, It's digital product operations, not just in tech companies, but in every company, large retailers, financial services companies that are all trying to become digital companies with their own consumer-facing digital products. And so building the products actually becomes a new form of supply chain, right? Or I mentioned like the media company example of like producing content and deploying it at a much faster rate and more of them to streaming platforms or to you know, new methods of delivery, right, across other platforms. You know, it could be within, you know, a really large retailer, actually, like, you know, some blend of the old physical supply chain with now, like, the new way that they do omnichannel planning and marketing, et cetera. And so I think what we're trying to attack is the new economy value chain of all of these companies that are basically getting disrupted and their industries are getting transformed. And we want to be the backbone of that value chain in each of those top industries. Mainly, you know, we see the best growth in retail, financial services, media, and tech. So we want to be to this new world order what SAP was to the old world order. Of course, our product looks radically different, right? I mean, it's dramatically easier, faster to build on. It's, you know, there's centralized, you know, centralized datasets. And in fact, now we have a lot of customers who are pulling in data from, for instance, Snowflake or Databricks into Airtable, but then also creating these operational workflows that represent the different stages of the value chain into Airtable. So that's one vector in which we're really kind of focused. And then I think the second vector is really around disrupting ourselves and reimagining what this whole thing looks like when it's truly AI native, right? And I'm sure we'll dive more into this, but as a very meta product ourselves, we are an app platform, so we enable our own customers to build apps. We have a very meta role to play in terms of incorporating AI. So it's not just about leveraging AI to enhance a feature. It's really about both using AI to completely change how people build apps, because now you can have AI help you build that app in the first place, and then also to incorporate AI into the runtime of those apps. So effectively allowing you to take the capabilities of today's already models and put them to work in the context of real, you know, company data in real workflow form, not just through kind of a RAG Q&A use case, but actually embedded into a process and automating key recurring steps that might actually add up to many, many hours of employee time and also slowing down the output. So if you can deliver content faster and for half the cost, I mean, that's like completely disruptive in the media industry, right? And we want to do that with our AI runtime capabilities for every industry that we serve.

And I guess before going down the AI path, I want to make sure I really understand what you're doing. You're a little abstract for me in terms of like, you know, kind of managing big companies' operations, which maybe I don't understand. as well as I should. Like, could you describe like a real-world use case that's sort of representative of what someone in like, say retail, you know, might do with Airtable?

Yeah, so a common one is if you think about like what a retail company does, right? Like, you know, they sell stuff, right? And they used to sell stuff in stores, now they sell stuff online. And a big part of their value add, especially if they don't necessarily make all their own products, is first, you know, having a central database, usually called a PIM or a product information management system, that represents all the entire universe of possible products that they could have in stock, they could stock, they could source from their vendors, right? So it's kind of like the master dataset of like, here's all the products I could be working with. And then they generally pick which of these products do I want to use, right? And actually market and sell for this season. And then they'll go through kind of creative operations where they take these products, they get a version of it, they photograph it in front of beautiful settings. Like you think about like furniture companies and like, you know, it's about how you make the furniture look in a beautiful home with like beautiful sunlight and, you know, kind of arrange that beautiful kind of design. That's a lot of the value add, right? Is making these products look enticing and giving a vision of like how you would use them. And of course, you actually like take these assets and put them on the website, you put them in marketing campaigns, you may put them in like physical print catalogs, etc. And then ultimately, that may drive e-commerce transactions. People buy in store. There's kind of a cross-pollination between the two. And at each of those steps, there is a role for Airtable to play, both because we can be increasingly the scalable database that houses the source of truth for a lot of that, right? So if you pick the seasonal edit of products that you're going to feature this season, you can actually represent that in Airtable. Airtable becomes the system of record for that, right? And then if you're going to do like creative production and manage like the literal photo shoots and the creative asset production around each of those products, you can manage all of that in Airtable, right? So here's all the different steps. Here's the different like review steps of the shoots that we've done. Here's the creative assets we've produced. We can represent all of that in Airtable. And then finally, when you actually want to go and deploy it into marketing campaigns and put it on your site, et cetera, you can manage all of that in Airtable as well. So You know, it's not project management because I think project management to me associates with more like dry and tactical tasks, right? So it's like a to-do list, but it really is like representing all of these different stages of, you know, what the business cares about and giving you a database to house that data, but then giving you this kind of workflow interface layer. And increasingly, you know, what we've been shifting towards is, you know, when we first launched Airtable, the only interface you had was this spreadsheet-like interface, right? So you got a database, but the main way of interacting with it was like a spreadsheet-looking thing. And what we've increasingly done is built more streamlined interfaces where you can create like something that looks more like a really custom interface for somebody to go through and like, you know, triage the creative assets, right? Or perform a workflow. And so that's really where, you know, we've evolved from just being the database plus a default grid view on top of it to now being able to support these bigger datasets, but also these more sophisticated and more nuanced workflows around it.

And okay, so now I get that. Now let's talk about how AI fits into your product and this vision. I mean, you gave like an incredibly compelling demo at actually not our last conference, but the year before. So you were like right on the cutting edge, like one of the first It was one of the first really compelling business applications of LLMs that I had seen. But I actually went back into your product recently in prep for this interview, and we use it lightly actually for more project management type of tasks at Weights& Biases. Maybe we should use it more.

Yeah, uplevel it.

Yeah, we got to uplevel. But I was actually in there, and the AI was definitely not in my face in the way that it is in some other products. So where does the AI fit into Airtable right now?

Yeah, so I mean, depending on when you tried it, we just launched our AI co-builder experience. So when I talk about the 2 ways in which we're, you know, sort of incorporating AI, the first is this builder experience, which is all about, you know, that is very much in your face when you first come to the website now. And the way you, you know, one of the ways that you can sign up and start using the product is in fact you just tell it what is the app you want to build, and our co-builder will build you a version of that app for you, right? And what's really cool, I think, and underappreciated about modern LLMs is that they really have a lot of cross-domain spanning context. Meaning these LLMs are pre-trained on every industry. They understand that retail workflow I just described to you. I don't have to teach it that. It already knows. And it knows all these companies by name already. It knows Williams-Sonoma. It knows Nike. It knows all these companies. So instead of having to be descriptive and tell it like, okay, this is exactly the series of workflow steps I need, and here's the data model that I need, you can just tell it like almost in natural business language, like, hey, like I am the, you know, like product planner, basically a product buyer at XYZ retail company, and I need you to help me with an app for, you know, for my use case. And it will do a pretty good job, especially if we're doing some prompt engineering and kind of prompt steps behind the scenes to tap into the LLM's existing knowledge of all of these concepts to then produce an app. We can also augment it with, if you want to upload us additional context, or if we want to do a web search and grab more context about your company or your use case, we can augment with that. Ultimately, I think what's really powerful there is that if you think about the friction today to build an app, with traditional software development, it's freaking hard. I mean, it takes like weeks, if not months to go and build an app like what I'm describing. And it's expensive. You have developers for building it, and then you get it back and it might not do what you want. And this is the whole point of the Agile Development Manifesto and reducing that feedback loop between the app developer and the business requirements holder. And I think what Codbuilder does is takes what we already were doing with no-code, to the next level, which is no-code already made it radically faster and easier to build those apps as the business person yourself. And Copilot allows you to do it even faster yet, right? Because instead of like what would've taken like maybe hours to go and drag and drop and define the data model and think through the problem, you can just tell it what you want it to build. And like a smart industry knowledgeable software development partner, it will go and actually not just build the app, but build an app that reflects an understanding of your industry. And that's really, really valuable. So that's pretty exciting. And like, literally, if you go to our front page now, that experience is front and center. And in fact, we allow you to even generate an app before you sign up. So you can come in and just test it and say, hey, here's my use case, show me an app, right? As if like, there was a software developer just sitting at your beck and call to like, make you a demo app, right? And then you can take it over. and obviously directly edit and manipulate it to get it to where you want. So that's one way. And then the second way is really about giving you these Lego pieces, and it's intentional that they're not loud and they're not like, we don't have to paint the entire page in a new color to show you, hey, wow, this is all different. Because the way we think about AI at runtime Is, you know, AI is just another capability of software at large, but the way that we're going to expose it to the customer is going to be through familiar building blocks, right? So if you want to add a field in Airtable that's powered by AI and can take structured inputs and delivers an output and allows the human to then review that, edit it, approve it, you know, and then potentially chain that to more actions, it could be fire off an automation where if the output is approved, let's say it's marketing copy, then that goes into Slack, or it goes and automatically feeds that content into a CMS to publish, or maybe it feeds it actually into another LLM call. So you can chain LLM calls together where it critiques itself with the second LLM call. But the idea of exposing it as yet another building block that is as interchangeable and as approachable as our existing building blocks of tables, fields, rows, automations, interface layouts, that's very intentional because we want AI to not feel like this completely, you know, scary new thing to builders, but instead something that they could really tap into, you know, in a modular way, right? So you don't have to like upend your entire way of working. And, you know, for what it's worth, I don't think we're going to see a world where you can just replace the need for human-driven or human-centric workflows and database-centric apps with an end-to-end agent that can just do all the work for you. Maybe in certain very narrow domains like support, especially very linear types of support, like if you're DoorDash, the types of support inquiries you get tend to be very simple. It's like, my food isn't here on time, blah, blah, blah. It's always a very simple and compact decision tree that you could probably automate even without AI. But I think for most types of real knowledge work that entail some amount of creative or open-endedness, you're going to need a system more like this where the AI steps are just introduced in between and they should feel like building blocks in the system, not a complete upending of how the work is done.

Interesting. I feel like there's this kind of trope right now that a lot of these AI-powered applications and tools have a lot of growth, but really low retention. And I think if I look at my own usage patterns, you know, I always want to try the latest stuff and I'm always just like, wow, that is like incredible. But so many of these things that I've tried that are amazing and seem really useful, I haven't stuck with myself. Do you, do you see that at all? Or does the Airtable AI tools have like a different character with, with higher retention?

Yeah. I mean, look, I think, I think this is kind of the, you know, the, the design of our approach, which is, I think the reason for a lot of the AI tourism effect where you get a lot of like, you know, top of funnel interest, but then very little retention is because when the AI is just It's all about the AI sizzle and not as much about first starting with like, what is the real business problem that you can solve in a meaningful way, in a recurring way that has permanent value, has sticky value, that's going to drive retention. If instead it's just about like the flash and awe of like a cool new experience that people want to try once, but then ultimately it's hard to kind of find value out of it in a recurring way, I think you're going to get the novelty, you're going to get the fun and excitement, but ultimately it's going to be hard to create that flywheel. I think by design, the way that we're implementing AI is more focused on delivering that meaningful recurring value. As a result, we're not seeing this influx of people just looking to kick the tires and try the product. I think generally the people who are coming in and using it really have a purpose in mind, right? And so our goal is to really use AI to both create a stickier product, stickier business, and a more valuable one, and ultimately one that activates better, right? I mean, literally with Copilot, we can drive a higher activation rate because people coming in who already want to build an app can now just lower the friction to do so from like maybe minutes or hours into seconds.

Can you talk about your product development process? Like, you know, from when you first had this idea to like the mockups to how are you like iterating on this as it's live in production and improving it?

Yeah. So, you know, we did this reorg in our product group about, call it 9 months ago now. And I think it's been really effective because in the past we were very feature-oriented. So even the way that our product org was structured was that we would have these feature-oriented groups. Here's the group that's responsible for improving search in the product. Here's the part of the product org that's responsible for improving the editing experience. It makes sense. There's a logical reason to have different groups working on different capabilities. But what I realized is that by nature, You know, this results in a more incremental roadmap because each feature group is kind of invested into improving that feature, but kind of like there's no overarching theme or there's no overarching kind of like structure that says, wait, but what are the bigger unlocks that we're trying to go after? How do we like, you know, not just build incrementally better functionality into the product, which is important. You got to make sure you have a polished rock, but also like you want to make sure you're building bigger and newer rocks as well. Right. And so we reorged around these major new pillars that, you know, one of which, for instance, is all about like the overall like onboarding and activation experience, the usability of the product, and the ease of which teams can grow and expand. And ultimately that's measured by like their self-serve revenue, right? So how easy is it for a new company to come in, start using Airtable? Of course, AI Copilot really helps with that. And then ultimately to start paying for us, right? And in some cases upgrade to enterprise later. So that's one entire pillar. And because it's defined in that way instead of in a feature way, they're able to think bigger about the bets they want to make. And thus, you know, we can have a longer-term view of like, okay, AI Code Builder is just the starting point, but what's the next version of it or what's the next unlock? Well, it might be, you know, we've, we've helped customers get from 0 to 1 very easily. Create that starting point of an app. But now we want to enhance Copilot to make it easier to take the app from 1 to 2, right? Which is to say, you know, if you want to create tweaks, if you want to add a new interface, if you want to add an integration, like we should also lower the barrier to doing that with AI. So like, I think that's a, that's a great example of like, the product roadmap has now evolved to really be focused around for each pillar, a clear North Star of where we want to go. And it's quite non-incremental, right? Like we have a separate pillar that's really about enterprise and scale. And what they're working on entails things as big as we're actually building a new data store into Airtable that will allow you to ingest multi-hundred million record datasets into Airtable directly, right? And so that's a huge, huge unlock. And it makes sense when the North Star of that group is really around the, hey, let's uplevel from a team or group-based, easy-to-use app builder to now also one that can scale up to these big enterprise-wide spanning deployments.

One of the things that we notice at Weights& Biases when people launch a feature like CoBuilder, like you're talking about, is that there's a little bit of a cultural shift in the organization going from a more deterministic product to a product where it may or may not work. Like it kind of looks maybe more like growth hacking or something like that, where it's unlikely that like Code Builder is going to make like any application that, you know, somebody might dream of, right? There's kind of a range of things where it works. You might not know exactly like what that surface area is. Is the non-determinism of the underlying LLMs that you're using changing the way you think about software development?

Yeah. So I get your question. It's something I think about a lot, right? Because if you think about a traditional piece of code, unless you have a bug in it, you run it and it kind of does the same thing every time, right? Or even traditional API services, right? If you want to interact with the Twilio API, you send an API call and it does something pretty deterministically, right? Send an SMS to this person, right? And if it fails, you get a very clear failure code, right? Like, could not be delivered because this phone number does not exist. And I think you're right that with LLMs, it's deceptively like an API call. I mean, it is an API call, right? If you're using an inference API, you're not running the models yourself. So you can make an API call to OpenAI or to Hugging Face or whatever. But the reality is what you get back is kind of very non-deterministic. Meaning even the frontier of what the models are good at is relatively unknown. Of course, we've all seen the benchmarks on standardized tests, MMLU, etc. But in terms of all of the surface area of real-world problems you can apply LLMs towards, that's relatively undiscovered. That's why I think it's fascinating to constantly look at Twitter and Reddit and see all these cool new zany use cases that people are unlocking. Like, oh my gosh, I uploaded my blood test markers over 5 years into ChatGPT and it actually did an extraordinary job of diagnosing some potential health issues I had. These are real-life examples.

That's a true story?

That's crazy. Yeah. Apparently the hack is to get past the safety gates. You have to tell it, well, I'm actually writing a script for a movie and here's the The data I'm uploading for the movie has to be very, very correct because in the movie we want to be scientifically accurate or something like that. But the point is, even the researchers who work on these models, as you know, don't even really know where the model is going to be really amazing, potent, and capable before they train the model. And even afterwards, sometimes they're kind of astounded. Wow, we didn't think Anthropic's 3.5 model on it is actually going to be so good. It kind of amazed us in certain dimensions. I think there is this frontier of unknown in terms of how these models do in specific use cases. When you think about the really wide varying texture of real business use cases, think about every single part of a knowledge worker's job in every single department. Marketing, legal, finance, product, HR, in every single type of industry and different type of company size. If you were to go and test how these LLMs did at specific steps, obviously you can't do the whole thing end to end short of AGI being developed. But even if you tested it on specific key steps, sometimes you don't really know what you're going to get until you try it. I do think this implies something for both our customers and something for how we develop our product. For our customers, it implies that you cannot go and just establish a centralized AI team or committee that top-down and from an ivory tower goes and says, these are the use cases for AI we're going to deploy into the company and they're going to work for sure. Because I think you have to have experimentation. There is this unknown of taking this miracle product and just test it on all these different problems across the organization. And the only way to do that is really to empower a culture and even a platform model of local experimentation. Meaning you need people within each of those functions, somebody within HR, somebody within legal who understands their own work and the workflow and the data to really be able to have the ability to experiment with AI. I think the way to do that is not necessarily just through chat interfaces, but really be able to embed the model intelligence into your data itself, which we do with our approach. I think that's going to be really key. Then to the second question of how does this affect the way that we develop the product, I think it means that we need to provide tooling to our customers that lends to this rapid experimentation. Prompt engineering is hard. Knowing how to chain together different prompts to get desired output. For instance, if you have one prompt output a draft of a legal document and then the second prompt can actually review and critique that legal document given a certain set of criteria or standards or even example documents it needs to compare them against, then you'll get the critiques. Then you can have yet a third step which might be human or AI to incorporate those edits. But those steps and how you design the prompt and how you give it the many-shot examples in each case are not always obvious to business end users. Very few people even know how to prompt engineer and leverage these LLMs effectively. I think a lot of our job as a product is to give people the tools and the UX to make it really easy to do this. Instead of having to go and Define that prompt sequence yourself to give you kind of a higher-order wizard to say, this is what I'm trying to do, and then have our product underneath the hood kind of implement the specific prompts, the specific like many-shot feedback loop. And in fact, we have the advantage of potentially being able to use the user feedback that people give us in the app itself, where they're reviewing in Airtable the outputs of the AI and they're saying, no, this is wrong, or actually let me override this. we can feed that back into a many-shot example set that feeds into the prompt even without doing fine-tuning to get better and better results over time. Just being able to abstract away a lot of the weird nuances and all the prompt engineering and LLM best practices, I think they're arcane and they're really hard to get to know and they're constantly evolving. We can make that all easier for our non-technical business users.

And do you use those techniques yourself internally also to actually build the product?

So we are trying to be very, very good customer number zero users of our own product, right? And even start to push the limits of like the next generation of capabilities. Like we want to be able to kind of dogfood or do in a hacky way, ourselves so that we can learn what works, what doesn't work. So the short answer is yes, and increasingly so. I think, you know, what we're learning is sometimes it's really helpful to have like almost a rough and dirty version of a capability. Let's say we want to be able to use AI to suggest AI use cases within an app, right? So imagine like if you work in legal at Airtable, and you already have a lot of like legal contracting workflows, in Airtable, Airtable could suggest to you, hey, why don't you add these AI steps or AI fields to automate these things that I've inferred you can do based on the structure of the contents of your app and even what you're doing in the app, right? And, you know, one thing we want to do is like start experimenting with, let's just build a rough and dirty version of that and put it into the product for ourselves, or maybe for a very limited set of alpha customers externally. And let's see how they use it. And let's see Even without looking at their actual customer data, let's get their feedback on is it working, is it not? Then we can tune and change the prompts for our own meta features to see how they work. The short answer is yes. I think to your point about a lot of these things are unknown and you have to experimentally try them, we want to lend ourselves to a more experiment-capable way of using our own features.

Do you feel like you've collected a set of best practices internally for making high-quality prompts? And how big is the delta between your first guess at a prompt and where it ends up in industrial use?

So I think we are collecting a lot of those best practices, and it's not just what's the best way to write a single prompt? And of course, there are some very known best practices. You can tell it, hey, the system prompt, you are this identity, and then it will guide it to play a certain style or leverage a certain type of knowledge more so. I think of it as almost creating a bias towards a certain neural pathway in these massive models that can play so many different roles and have been trained on so many different ways of thinking about the same question. To the many-shot examples, to the stylistic guidance, or even forcing a structured output in JSON mode with some of these newer models that have that capability. I think a lot of those are already out there. They're well understood as a consensus across the LLM development community. I think where we're finding more interesting learnings that maybe aren't out there relate to solving real messy business problems with these prompts. In practice, how much do you need to break down a real business workflow like this marketing content generation from end to end? It's not just about a one-time generate the thing like you could do in Jasper or ChatGPT. I think it's really about breaking it down into all the different steps. That is kind of like an overall workflow design with AI problem that we are gaining more and more know-how around. We want to productize more of that in addition to gaining just an internal expertise around it so that when our customer-facing teams go out and talk to customers, they can actually articulate and show in a demo a best-implemented version of that. But I think it's fairly early. I think there's also evolving capabilities with each model that kind of make some of these old techniques either obsolete or different. For instance, as the context windows have radically expanded from, I think the best ones had a few thousand as of 2 years ago, to now 200,000 or even 2 million in the case of the biggest ones like Gemini Pro, you don't have to do things like chunking if you're dealing with a really large input data source. It could be an earnings call, it could be a massive document or a set of documents, you don't have to break that down into different chunks anymore. I think there's a lot of things that are changing as we speak. Our goal is really to be just so immersed into both the cutting edge of every new model and then more importantly, being very hands-on in bringing those models to real customer problems. I think that's perhaps where we have the most durable advantage is we do have this really large customer base of people who have built all of their own use cases on Airtable, gotten their data in Airtable. And now every time we introduce a new model's capabilities, we're kind of like getting to learn and experiment with them with this large customer base. You know, how do you actually, what are the best practices for using this new generation of model in the context of real work? So, you know, I think it is a way of thinking, a way of learning. that we're really trying to be fleet-footed with going forward.

Well, it's a great segue into a bunch of questions I wanted to ask you about the overall AI market, not necessarily from exactly Airtable's perspective, but from your perspective as someone kind of sitting at the intersection of AI and business. Do you have a perspective on open source models versus closed source models?

This is an area where we have the luxury of being able to be somewhat agnostic. The short answer is I have no idea. It seems like the open-source models are catching up and obviously it's very timely to discuss this with the LLaMA 400 billion parameter model coming out. It seems to be a really great model. But at the same time, who knows? Maybe GPT-5 comes out in 3 months and it just blows us all away with an even greater degree of reasoning intelligence. I think that's the next frontier, not just being able to answer questions that look more like a standardized test like MMLU, but actually there are these new benchmarks where we care about the AI's ability to reason and answer questions that don't just look like other pieces of knowledge it's seen. It's like the SAT analogy type of questions, but instead really require a deeper understanding of what's being done. It's possible that maybe the closed-source models will continue to hold an edge on that form of use case, which I think will have pretty important implications. If you just look at the typical standard test performance, it does look like there's a plateau. Obviously, you can only get so good. You can only get close to 100% on a standardized MMLU-type test. But I think what is interesting is that if you can get closer and closer to the open-ended reasoning capability of a human, Then you can start to see more real hard work problems get tackled by AI. I think of it as a benchmark of if you froze AI model development today and all we had was what we have, and then you spent the next 20 years just trying to figure out every single possible business use case to deploy these AI capabilities into, how much economic value could you create? First of all, I think you could do that today, just freeze all the model development. We've only exploited maybe 0.0001% of all of the potential economic value gains you can get from today's AI models. A lot of it is about actually situating it into real data, real workflows at the edges of, and the really interesting nooks and crannies of every part of every company. Then the secondary question is, with every new model generation, and the benchmark I care about is if you could play that thought experiment out, how much more economic value could you unlock with this next generation of model? And that's where seemingly marginal improvements in the current benchmark scores, I think, can actually belie the fact that really major advances in reasoning capability will actually unlock an even broader set of really valuable business problems that can be solved.

Okay. What do you think about AI as it benefits kind of big entrenched incumbents versus startups? I mean, you can imagine incumbents are successful because they have the kind of workflows, they have the data, and now they can add in AI maybe easily. But, you know, startups might be better positioned to use a new technology. Who would you bet on?

Yeah, I mean, I think this is where the driver behind the seat of the car matters the most, right? It's like you could have one car that has certain advantages. It's kind of the traditional winning team in F1, but you've got a new upstart and it's got some fancy new technologies. But ultimately, I think in either case, the driver matters. Meaning, I don't think the big existing incumbents are going to win by default just because they have the data and the distribution. I think they have to be nimble on their feet and they really have to think about What is the most disruptive way to apply AI to their existing product? It's not for free that you just flip a switch and then you get AI value in your product. As we've talked about for a lot of the past conversation, I think it's in really figuring out the nuances and the nooks and crannies of how you deploy AI into real-world business problems that the value is unlocked. I don't think big companies get that for free and they have to be very deliberate and smart about thinking, what is the way we want to infuse our product with AI? On the other hand, I think the net new AI startups are also going to have some real challenges. They're fighting this race of, as you pointed out earlier, they've captured a lot of early hype. That may parlay into some top-of-funnel AI tourist traffic that helps boost their starting point. But then there's often a very leaky funnel, right? And there's also questions of how durable these advantages are. What's to stop the next company from coming in and doing the same thing or the incumbent from doing the same thing, right? And ultimately replacing them with a feature that makes their entire business obsolete. And so I think it's kind of a race between the incumbents to disrupt themselves and the upstarts to build durable advantages. And, you know, I think the answer to all of that is it just comes down to really good execution on either part. So like I would bet on, you know, the companies that have the most hungry and disruptive teams who are thinking really strategically, but also like staying very, very close to the details of what's happening here and now and not being head in the clouds. Right. And, you know, that's easier said than done.

Well, okay. So, you know, a lot of these companies, both the incumbents and the startups.

So Who's going to win? I mean, I honestly think it's company by company, right? So, you know, as an example, I think we both know Scott Belsky from Adobe and like, I have a huge amount of respect for him. And, you know, like with him working on a lot of the AI capabilities of Adobe, like I'm pricing a lot of like upside into Adobe, right? You know, and, you know, on the flip side, like there are some startups that I really admire because I think they're thinking about the problem in a way that is not just short-lived, but really looking to accrue longer-term advantages. In some cases, they've made pivots. Tome, as an example, started as more of this AI slideshow tool, which is really cool. It was a very magical experience. You come in, you tell it what you want, and it makes you a slideshow complete with imagery and text and everything. That was really magical. But I think they were really smart to adjust course into going deeper into a stickier business problem, which is about actually giving salespeople an entire customer intelligence tool to go and do research and then produce a presentation. Obviously it's still early, jury's still out on both of those examples. But for me, I really want to bet on specific people, specific teams, and I think there's going to be winners on both sides.

Okay. In a world where it's easier to make apps, and I would include, you know, there's Airtable making it easier to make apps. There's also like, you know, kind of software development, you know, getting easier through like all these different tools. Like you can make like, you know, a nice webpage on Vercel a lot easier than you could in the past. Do you think that endangers vertical-based SaaS applications? Like, I don't know, like a Salesforce or something like that? Are people just gonna make their own internally because it gets so much easier to do that?

So the short answer is absolutely yes. I mean, I think this is already the premise of no-code, right? Like, why do you need a bunch of fragmented, like not that great apps, especially like on the low end of things, like, or very narrow market side of things like CattleMax? Why do you need the like PR-specific journalist database that has like kind of a CRM workflow layered for pitching them when really that's just like something that should be a more flexible workflow built on a flexible platform, right? With more modern UX and more modern capabilities. So I think this radically accelerates that disruption that we were already seeing with platforms eating more of these niche vertical software products. I do think that the vertical software products that will continue to be strong are those that are in a very weird industry that's still very large but has so many domain-specific requirements. So like take Veeva for instance, right? I mean—

I was hoping you'd say MLOps.

Yeah, yeah, yeah. I mean, for sure, like exactly that. And especially those that have a 3-word name, ideally ending with -izes. Those are going to be the ones that do really, really well. But in all seriousness, I think, you know, the more the depth of weird and unique requirements or dynamics of an industry, I think the more a vertical software player will remain the incumbent and succeed. And if anything, have more time to buy themselves before they need to disrupt themselves and really think about how to implement more AI and all that stuff. But end of the day, I think it is going to accelerate more broadly this shallow set of vertical software companies that really, I see them as almost like a temporary blip in the history of software where early-day software was all very either horizontal or custom-built, right? You had like your big 3, like word processor, database product, dBASE, you know, and spreadsheets. And then everything else was like custom-built software for your own company, right? You'd buy Oracle as a DB and then build your own application on top of that. And then I think we saw a world where, especially with SaaS bringing down the economics of building and not only building but distributing software, You started to see all these more niche players spring up, but I think they always had disadvantages of never quite being the same level of quality as like a product that just had more flexibility and more investment overall. Right. And so I think this does turn the tide back towards, okay, like, why am I using this, you know, very vertical piece of software that's brittle, doesn't quite meet my needs. And, you know, it's just kind of a one-off that also is siloed from the 50 other products that are in my ecosystem.

Do you expect software developer as a function to continue for the next 20 years?

So I think so. And the reason for it is both no-code built internal business apps and the types of apps that you would build with traditional software development, both for internal company use as well as external-facing apps, I think will only continue to proliferate more as the cost of building gets lower on both sides, right? Obviously, you have developer copilots that augment developer productivity, make it easier and faster to write code. And then I think separately on no-code, you see this radical disruption and lowering of the cost further to build apps that way. And I think in both cases, You know, it's not really like a zero-sum game of which wins. I think it's more of an overall pie growing because traditionally with any product or good or service, as you radically lower the cost of that thing, people buy more of it, right? Especially when there's no finite, you know, kind of upper limit to the number of useful apps or the amount of useful software that can be created. And I think we're still at the very, very tip of the iceberg of software eating the world. Software is literally a way to manage and represent and make more efficient or automate the informational workflows of the world. And that is like, there's so much more software that we could build that would create that economic value in a world that especially is moving every value chain more and more towards these informational workflows away from the physical and hard good processes. You know, I think, you know, it's only going to accelerate the amount of software and useful and value-driving software that will be created on both sides.

So where in the AI hype cycle do you think we are? I mean, I feel like you've said a whole bunch of really optimistic statements that makes me think that you might think we're sort of on our way up if we don't even need better LLMs to like, you know, fit many more business use cases. That would sort of imply that, um, you know, the, the AI funding train and excitement is just going to kind of grow over the next few years. But I guess like from my vantage point, I worry that I see a lot of pretty bad products with AI baked in that I, and I see companies sort of investing in AI without a use case in mind, which that really scares me and sort of says like peak bubble to me. Like where do you land on that?

So, you know, I think we are still at the very, very early innings. of realizing all the value with AI. So long-term, I'm still very optimistic about what can be done with AI. I think this technology is real. And again, even without banking on some major new breakthroughs in terms of model capabilities or architecture, I think you could take what we have today and just actually create tons of economic value with it, like trillions of dollars of GDP. Yet at the same time, I think today there's a lot of hype. there's a lot of funding and there's a lot of valuation increases that are not quite being fully justified or not at all justified by like the real value delivery to customers. And either that's because you have companies that, you know, again, like they are just raising on the promise and the hype, but have not actually delivered meaningful value. Or even if they're making revenue from customers, sometimes that revenue, like not all dollars are created equal, right? And I think there's a fair amount of like frothy dollars from even customers who are just looking to spend on AI products to be able to have an AI strategy. And yet, I just saw a tweet today that was somebody saying, I'm talking to a customer who is saying Microsoft Copilot is their main AI strategy, but they don't have any demonstrable ROI. They have no measurable impact from this. And there's no way to really say concretely, this is actually improving the output of the business. And maybe you could track if it's a public company, like, okay, well, does the earnings call next time and the next time actually going to be better or not? Obviously there's a lot of confounding variables, but I think that's the difficulty right now is you have a lot of hype and dollars being thrown out of the system by both the investors, but also by customers without fully applying this rigorous bar of, wait, what is the real business use case and business impact here, right? And I think that bubble is going to collapse on itself because at some point the customers are going to wake up and realize, oh wow, we're spending a lot on certain AI tools or vendors that we have nothing to show for. We have very little to show for in terms of real business return, right? And so we're going to pull back, we're going to rationalize the spend again. And I think that's going to cause an adjustment on the investment side because You know, it not only does it become harder to invest in companies that are doing zero revenue, but like even the ones that are doing revenue are going to see kind of a slowdown if they're not providing meaningful value. All that being said, I think it's going to be sort of like the Web 1.0 bubble collapse, which is, you know, obviously to say it, you know, kind of wiped out a lot of the frothy companies. I mean, you know, I was still very much, you know, a, I think I was in middle school, high school. during a lot of that. But getting to see it from that vantage point, there were just all kinds of products that probably should not have existed. In hindsight, we all kind of knew it. I signed up to be a beta tester for a smell printer company who was going to send me as a not even 18-year-old high schooler a beta version of their smell printing product. Turns out it's not even possible to really print all the smells in the universe with a printer. But you had a lot of these silly products like that. That, you know, once the rationalization cycle, you know, switched in, went away entirely. And yet you had really good companies like Amazon that actually were born in that era, right? And, you know, and kind of because of that rationalization, arguably became even better companies and they survived because they were actually good businesses and, you know, they were well run and they had like a path forward. And so I think we are going to see some of that. And, you know, maybe also like the crypto bubble, like as a lot of people in crypto say, like, well, actually having a little bit of a crypto winter could be a good thing because it cleans up the system so that, you know, what remains are really the meaningful and value-driving types of companies that have real purpose, right? Versus are, you know, basically benefiting from capturing all these frothy dollars coming into the system. All right.

Well, I think that's a great place to end, Howie. Thanks so much for your time. Yeah, for sure.

Thanks for having me.

Yeah, that was awesome.

Thank you.

Thanks so much for listening to this episode of Gradient Dissent. Please stay tuned for future episodes.