The Exchanges

Q original, um, very, you know, simple 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?

A 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, when we, we started working on this, that laid this out. And 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 going to 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 and 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 local ad platforms entirely. We're undercutting Salesforce service. Now we're undercutting, you know, like these old school products, like quick base and so on. And it's just going to be so much easier to use, but then over time we can improve The ceiling, right? Um, and initially we're going to get some like, you know, lightweight, medium weight use cases, but over time we want to improve the data scale. So, you know, actually literally just making it possible to score hundreds of thousands of, of, uh, ro…

AI assessment note: “So that was always part of the, the master plan.”

Q some, like, obviously work on real, um, workflow, 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, any frameworks for how to give people intuition for what today's models can do, either like in Airtable, because you guys have to have the expertise, or in your customer?

A The short answer is we've been trying, uh, to do a number of things to kind of codify that 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 have one in LA. We have 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 master class 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 GP one to GP four, and obviously parameter count is not the NLBL and now, you know, smaller models are actually doing really well, but I think 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 8086 processor that has made this the time to actually pay attention and care and like t…

AI assessment note: “we actually now run this AI workshop program. It's a lot lighter touch”

Q 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?

A Well, um, you know, well CodeGen basically replaced the need for vertical software and will 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, you know, has some nice properties of being, um, 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's some really interesting things you can do with training at making the models better. Um, and there's some really interesting innovations happening out there, right? With, with not just the big companies, but like the startups, like the magics and the pool sides and 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 going to 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, it's…

AI assessment note: “fundamentally hard, like really, really hard to generate Really sophisticated end to end process”

Q 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, 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 does that meant operationally?

A I think product management is, first of all, just like a really hard discipline to do right or to do well. Um, cause 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, um, on a podcast, uh, somewhere recently where, you know, uh, this was, I think misinterpreted, but like, there's a big buzz around, uh, the 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 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 going to 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, uh, make sure it fills the technical capabilities. How do we make s…

AI assessment note: “splitting the role into two constituent pieces... product marketing and then program management”

Q 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?

A 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, chat to be key was kind of the thing that broke through and made this mainstream and even kind of like escalated the world 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 cop 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 …

AI assessment note: “we're all very centered on this idea of the chat interface as the main”

Q like, how do you actually take something and convert it into something that has, you know, real user value. Can you tell us a little bit more about that journey? Like, how did you first become aware of some of the things happening in generative AI? What made you decide it was different from prior ways of, of ML? And then, you know, how you thought about progressing with it?

A 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, oh, five through oh nine. 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 saying 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, it just has 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, bec…

AI assessment note: “I actually, like, really was interested in neural networks in college.”