← Lenny's Podcast: Product | Career | Growth
Lenny's Podcast: Product | Career | Growth
How we restructured Airtable’s entire org for AI | Howie Liu (co-founder and CEO)
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- [00:00]
A
If you were literally founding a new company from scratch with the same mission, how would you execute on that mission using a fully AI native approach? If you can't, then you should find a buyer. And then if you really care about this mission, like go and start the next carnation of it or people that.
- [00:13]
B
Work for you, how have you adjusted what you expect of them to help them be successful?
- [00:18]
A
If you want to cancel all your meetings for like a day or for an entire week and just go play around with every AI product that you think could be relevant to Airtable, go do it.
- [00:27]
B
Of the different functions on a product team, PM Engineering design, who has had the most success being more productive with these tools?
- [00:34]
A
It really does become more about individual attitude. There's a strong advantage to any of those three roles who can kind of cross over into the other two. As a pm, you need to start looking more like a hybrid PM prototyper who has some good design sensibilities.
- [00:49]
B
Do you see one of these roles being more in trouble than others? Today my guest is Howie Liu. Howie is the co Founder and CEO of airtable. I'm having a bunch of conversations on this podcast with founders who are reinventing their decade plus old business in this AI era to help you navigate this existential transition that every company and product is going through right now. Howie and Airtable's journey is an incredible example of this and there's so much to learn from what Howie shares. In this conversation. We talk about a very interesting trend that I've noticed that Howie is very much an example of of CEOs almost becoming individual contributors again getting into the code, building things, leading initiatives themselves, the something that we call the icco. We also talk about the very specific skills that he believes product managers and product leaders, also engineers and designers need to build to do well in this new world that we're in. Also how he restructured his company into two groups, a fast thinking group and a slow thinking group which allowed their AI investments to significantly accelerate. Accelerate. If you're struggling to figure out how to be successful in this new AI era, this episode is for you. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of 15 incredible products including Lovable, Replit, Bolt, N8N, Linear, Superhuman, Descript, Whisper Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chapierd and Mobin. Check it out lenny'snewsletter.com and click Product Pass. With that, I bring you Howie Lu. This episode is brought to you by LucidLink, the storage collaboration platform. You've built a great product, but how you show it through video design and storytelling is what brings it to life. If your team works with large media files, videos, design assets, layered project files, you know how painful it can be to stay organized across locations. Files live in different places. You're constantly asking, is this the latest version? Creative work slows down while people wait for files to transfer. LucidLink fixes this. It gives your team a shared space in the cloud that works like a local drive. Files are instantly accessible from anywhere. No downloading, no syncing, and always up to date. That means producers, editors, designers and marketers can open massive files in their native apps, work directly from the cloud, and stay aligned wherever they are. Teams at Adobe, Shopify and top creative agencies use LucidLink to keep their content engine running fast and smooth. Try it for free@lucidlink.com Lenny that's L U C I D L I Today's episode is brought to you by dx, the developer intelligence platform designed by leading researchers. To thrive in the AI era, organizations need to adapt quickly. But many organization leaders struggle to answer pressing questions like which tools are working? How are they being used? What's actually driving value? DX provides the data and insights that leaders need to navigate this shift with DX. Companies like Dropbox, Booking.com, adyen and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com Lenny that's getdx.com Lenny Howie, thank you so much for being here and welcome to the podcast.
- [04:15]
A
I'm so excited. Thank you Lenny. I've been a listener from afar for a while now.
- [04:19]
B
I'm really flattered to hear that. I'm also very excited. You've been on quite a journey over the last. Is it 13 years? Is it longer?
- [04:27]
A
Like, right. Yeah, right. About 13.
- [04:28]
B
13 years. I imagine there have been a lot of ups and a lot of downs. I want to talk about all those things. I want to talk about a lot of the lessons that you've learned along the way. I want to start with what I imagine was a very surprising down moment in the history of Airtable. This is something that unfortunately is something I think about when I think of air. I feel other people maybe feel this way is there's this tweet that Went super viral maybe a couple years ago at this point where someone just shared all this data and they're like, airtable is dead. They've raised way more money than they're worth. They're not making enough to get from underwater. Airtable Rip, what happened there? How much of that was true? How did that go?
- [05:07]
A
Yeah, so very, I mean, basically none of it was true. And I mean the surprising thing to me was how viral this tweet went when frankly, like I actually look back at this person's other tweets. I think they, they, they worked at CB Insights. And the irony is like that the whole point of that business is to have like good data, good data quality around private company data. And they just like literally had incorrect numbers by like a strong multiple on like what our revenue scale was, what our growth rate was like, you know, and, and if it gave me some consolation, I look back and like this person had also tweeted about other companies, like Flexport was the last like kind of takedown tweet. They, they had like, oh, Flexport's dead, you know, their valuation is too high and blah, blah. And so I think that the more surprising thing was just like this person has been tweeting a bunch of spicy takes that are not substantiated by real data or correct data. And yet this particular tweet went super viral. And that was the perplexing part to me. And then actually I think what really gave it legs was on the all in podcast, which is obviously super popular, you know, and I listened to it like, you know, they, they covered it. They were like, oh, like, you know, latest on, on this week's news, like, you know, this tweet about airtable. What do we think about this? And it almost, I think became like a way to talk about a broader theme of what happens to this last generation of highly valued companies, maybe Decacorn companies in this new. And at that point it was like kind of the reset moment for both public and private markets. They did also issue a correction though, all in did a follow up episode a few, I think weeks later saying like, hey, like, you know, we got the numbers wrong. Like, you know, we're revising our case and kind of view on airtable.
- [06:54]
B
What's that line about how a lie gets around the world some number of times before truth has even has time to get out of bed.
- [07:00]
A
Yeah, yeah, well, I think I learned about memes and duality very quickly in that experience. Not a very good social media person, but I think I learned a little More.
- [07:11]
B
Yeah, it's tough. Twitter's such an, the incentives are so misaligned. It's just, I need, I tweet something people want to share, not truth.
- [07:17]
A
Well, I mean, especially like, I mean I, I, there's a lot to like, I would say net net. I like the post Elon Twitter more than the pre Elon Twitter because it, it's just bolder and like I, you know, I guess I, I really admire bold product execution where you're not just kind of stuck to like the current laurels and they made so many changes. But like, I do feel like I get injected into my feed very sensational content all the time. And I mean it works on me. I'm like, you know, like, I can't help but to like click on it and engage with it and like, you know, but it does, I think it does result in like this kind of content, like really spreading.
- [07:53]
B
Yeah. Now in the key to running the show, I don't, I don't know if you saw this. There's a new, we don't need to keep talking about Twitter, but there's a new feature where you take a screenshot of a tweet and it has like a huge X.com logo, which watermark top, right? Yeah. Just to like, you know, people are sharing these tweets all the time. Yeah.
- [08:05]
A
Yeah.
- [08:06]
B
Oh man. Never a dull moment over there for sure.
- [08:08]
A
Okay.
- [08:09]
B
I'm going to go in a completely different direction. Something that I'm really excited to talk to you about, which is this very emerging trend that I've noticed that I feel like you're at the forefront of, of CEOs becoming ICs again. It's kind of this move of ICC, CEOs, CEOs getting their hands dirty again, building again, getting in the weeds, coding again. Feel like you're again at the forefront of this talk about just why you've done this, why you think this is important and just what that looks like day to day to you versus what your life was like a few years ago.
- [08:39]
A
The underlying reason for this shift, at least for me, is that as we started the company, I was very much in this mode. Right. Like I was literally writing code both on the back end, thinking about the real time data architecture of our platform, also the front end, the ux. And I would argue that in that founding moment, the initial product market fit finding and especially for a product that is pure software, we weren't building a operationally heavy business like a dog walking marketplace where the tech is only an afterthought. The tech was the product and in a very meta sense Airtable is the platform for other people to build their own apps. It's all about the tech, like the very intimate design decisions again, both architecturally and on the front end and the product UX choices like that is the product's value prop, right? Like you can't separate those two. You can't say like okay, like I researched the jobs to be done, here's the workflow, here's the process and then like okay, some engineer can just build it as an afterthought. Like it's those like little decisions and really be able to like be at the bleeding edge of what's possible both in the browser and with like you know, kind of the real time data architecture that made the product what it was. Right. I think the same is true for Figma which actually had a very parallel timeline to us. We both were founded around the same time, both spent two and a half years building the product hands on that early team before launching and when I think now to both the era in between that founding moment and then now as well as now the new kind of genai moment. Like I think there was a maturing era of both SaaS overall and Airtable specifically where you know, as you scale up and you kind of learn how to build, you know, teams and organizations and like you have to kind of like scale up stuff that's not actually those intimate details but process and people and so on, you kind of get you know, by default further and further away from those details. Right. And maybe for some businesses that's fine because like no longer is it about finding like the details that make for a magical new product market fit. And it is really just about scaling up an existing thing that works right. And using what I would call like more blunt instruments to kind of scale it up. Right. Like a more blunt roadmap, a more blunt, you know, kind of go to market execution strategy. Regardless, I think that now we're, we're entering this moment where like every certainly every software product in my opinion has to be refounded because like AI is such a paradigm shift. It's not even like just like the shift from desktop to mobile or on prem to cloud where that was more like a very one time and somewhat predictable change in form factor. Like I think AI is so rapidly evolving that with every evolution, like every new model release and every new type of capability that's released, it actually implies novel form factors and novel like UX patterns to be invented to fully capitalize on those capabilities. And so like to be continuous, continuously relevant and kind of refine product market fit in this era. I think you have to be in the details. Like there is no like you know, looking at it from 10,000 foot view and saying, oh, we're just going to throw a bunch of people at this problem. It's actually understanding like what is the right product experience and the right business model that backs it up and the right, you know, everything else to support that engine, to take advantage of the capabilities in our product domain.
- [12:07]
B
You have this phrase somewhere where you, you talk about being the chief tastemaker and to do that you have to do exactly what you're describing.
- [12:14]
A
That's right. I mean I think that. And like, I would also say like it's actually now also hard to taste the soup without participating in like at least some part of creating the soup, right? And like meeting with AI, you can kind of look at the final product and say okay, this feels right or not or it feels like we're being bold enough and we're properly productizing these new capabilities. But I think to really understand the solution space of what's possible, you kind of have to be in the details, right? I mean literally you can't just look at kind of screenshots or a pre recorded video of a new product feature. AI is something you have to play with and ideally you're playing with both the like kind of packaged up, you know, app or solution that you've built with it, but you're also playing around directly with the underlying primitives. You're using the models either via API or via like a chat interface. Like you're really pushing them to the boundaries and like, because that's the only way that you really understand what these new ingredients. It's like as a chef you just gained access to like amazing new ingredients, but you have to like actually kind of get comfortable with them to put them into a new dish.
- [13:24]
B
We had Dan Shipper on the podcast, he runs this newsletter and podcast product company called Every. And he, they work with companies to help them become more AI successful and adopt AI and all that stuff. And I asked him what's the, what's the signal that a company will have success adopting AI and seeing huge productivity gains? And he said it's, does the CEO use chat, GPT or Claude daily? Yeah, and I feel like you're describing exactly right.
- [13:51]
A
Hourly, Hourly, like or you know, you could even like have a measure of like inference, like costs, right? Like the equivalent underlying like inference compute cycles, right?
- [14:02]
B
How many tokens they use.
- [14:03]
A
Yeah, I mean, I'm proud to say, like I am, I'm pretty sure I'm still the, I just checked this recently. But like I take pride in being the number one most expensive in inference cost user of Airtable AI. Not just within our own company, but I think for a long time I was globally across all our customers as well. I'm just, I'm like, well, I mean like I'm extremely intentionally wasteful. Wasteful in the sense of like, you know, I'll do something that costs like maybe hundreds of dollars of like actual inference cost. Right? Like for instance, you know, doing a lot of LLM calls against like long, you know, kind of transcripts of, let's say, sales calls to extract different types of insights, like here's the product apps identify or here's summaries, et cetera. And we, we also have now a capability that's basically like an LLM MapReduce. So effectively, even if you can't fit like, you know, the entire corpus of content into one LLM call, because the, the context window limitations will map through like all of this content and break it up into chunks and then like perform an LLM call on each one and then perform an aggregation LLM call on those chunks. Very expensive, right? Because you're basically running like a highly expensive model against a lot of data and then running it again on the aggregates of that. But like for me, you know, like hundreds of dollars spent on this exercise is trivial compared to the potential strategic value of like having better insights. It's as if like a really, really smart chief of staff and has gone through and read every single sales call like transcript that we've had in the past year and giving me like, you know, you know, kind of very astute product insights, marketing insights, like you know, kind of positioning insights and segmentation insights. Like that's invaluable, right? Like you could pay a consulting firm like literally millions of dollars to get that quality of work. So like, to me, I still think the, like the value versus the actual cost of AI when applied greedily but smartly. Like it's just, it's a crazy ratio and like more people should be like aggressively throwing compute cycles at these very.
- [16:10]
B
High value problems until somebody tweets how you're eating, costing the company so much on AI compute and you guys are going to be underwater. Just kidding.
- [16:20]
A
Like how we have personally taken down the cash flow profile of the business.
- [16:28]
B
So, okay, so CEOs, founders hearing this, they're probably like, okay, I Should probably start doing this. What does this actually look like? I imagine you still have a lot of other stuff. You got one on ones, you got all these like how do you actually, how have you changed your day to day to do this?
- [16:42]
A
Yeah, so I actually cut my one on one roster by default. And the idea is I'm not, is not that I don't want to spend time one on one with people, but rather that I found that the just like having more standing one on ones actually precludes me from, you know, engaging in more timely topics. Right. Like I like to think of you know, the best types of meetings as like very urgency driven and like you know, there's some timely topic like you know, you've, you've discovered some insight maybe. I talked to some new startup, right and you know, I learned something from their product or their approach and I want to bring that into how we're thinking about like a new feature at Airtable or even just like plant the seed with like, you know, some different like you know, EPD people within Airtable. Like I want to make most meetings very timely and very informed by like Real Alpha. Right. There's going to be some kind of value and insight to seed that with. Now in addition to that I'll supplement with like you know, when I'm in person, you know, with someone like I want to carve out time for like a, you know, a proper like catch up and like less structured, less, less like timely and just more of like you know, building a relationship with a human. But I actually find that like you know, having that it's almost a barbell approach where it's like, you know, if you're going to spend time with somebody in a freeform way like actually do it in a high quality, not like forced weekly ritual way like go for a longer lunch or coffee walk or whatever in person when you can. Maybe that's like a once every month or two kind of thing and then like the in betweens are either topical. So we do have standing meetings for you know, like now we have a, a weekly basically sprint check in on all of our AI execution stuff which now is like half the company or half the epd. Org is working on AI capabilities. We're trying to ship very quickly. Like you know, I basically want to always ask the question like how would an AI native company, like a cursor or windserve et cetera, like how would they execute, right. And are we executing as fast as them and, and taking advantage of like all the new stuff as well as them. So, like bringing that level of like, kind of intensity and urgency to like how I spend my time within, that's been the main, the biggest shift for me.
- [18:55]
B
What's a change you've made to help the company move faster and match that sort of pace?
- [19:02]
A
Yeah, so, I mean, we did do a reorg of the Org, so before we had. We've gone through a few different kind of reorgs over the past, call it four years. The kind of original state as we just kind of proliferated, I think by default or incrementally was that we had a bunch of groups that were each responsible for a feature or a surface area. So there was a group responsible for search within our table and there was a group responsible for mobile experience and so on and so forth. Right. And that has its benefits. Obviously that team can go and get really ramped up on that part of the code base, that part of the product. But it has the disadvantage of you tend to think incrementally when everyone's remit is actually a feature that they incrementally improve by definition, as opposed to thinking about a mission or a outcome goal that might need to coordinate dramatic changes across a wider set of surface areas instead of just each one incrementally improving. And so we reorged initially to basically different business units effectively. Right. So I know Airbnb has done like kind of the functional to gm, you know, back, et cetera. This was more like saying, look, we have an enterprise business and the mo, There is more about like scalability. Can we support like the larger scale data sets and use cases? Do you have the core capabilities needed to be able to like push out an app to maybe 10,000 seats or 20,000 seats for product operations? Right. So a lot of architecture, a lot of scale, that kind of work. We would have a, what we call the teams pillar, which is more about self serve, like kind of the product ux, like how easy it is to adopt the product onboard share, do all the kind of like basic functionality, an AI pillar, solutions pillar, and basically infra. And what we found though with that approach is that there was still, you know, there was more kind of holistic bets being made. So like, you know, the teams pillar could think not just about one feature, but like the overall onboarding experience. We're like really think about nuxt, you know, in a way that touched multiple parts of the product. But it still felt like it wasn't, especially as we started to execute more on AI stuff. Like it wasn't, you know, Allowing us to aggressively and quickly move as a AI native company would. Right. Like, I mean when you look at, you know, the cursors of the world, they're shipping like major new stuff every week and like, you know, it's not like, oh well, we have like this separate, you know, kind of roadmap for enterprise. We have this roadmap for, for this group and you know, it just feels like one, one cohesive product that's shipping at a breakneck pace. So we did this recent reorg where now we have the, what I call like the fast thinking group, which officially is called AI platform, but it really means like we want to just ship a bunch of new capabilities on a near weekly basis and each of them should be truly awesome value. You should drop your jaw at how awesome it is to use this new capability in airtable. And then separately we have the slow thinking group. That's not meant to be better or worse. It's literally you need fast and slow thinking in the common sense to operate.
- [22:12]
B
As that book behind me.
- [22:13]
A
Yeah, I love that book. But, but slow thinking is like, it's just a different mode of planning and executing, right? It's like more deliberate bets that require more premeditation. Right. Like we can't just like ship a new piece of infrastructure that has a lot of like data complexity. Like you know, our data store HyperDB that now can handle like multi hundred million record data sets. Like that's not something you ship in a week, right. In a hacky prototype. So we now have these two separate parts of the company. And I actually think what's really cool is they actually complement each other very well because the fast execution, the AI stuff that creates the top of funnel excitement, that also inspires new use cases and new users to come to airtable, including in large enterprise enterprises can use this stuff too. It's not just a SMB thing. But the slow thinking basically allows those initial seeds of adoption to sprout and grow into much larger deployments. Whereas I think a lot of the challenge for many of the AI native companies I've seen is that they can have like a very wide top of funnel, like get all of this AI tourist traffic, you know, a lot of interest, a lot of like, kind of like, you know, early usage. But then, you know, sometimes the challenge is how do you like turn that into more durable, you know, growth and get each of those adoption seeds to retain and expand over time.
- [23:33]
B
That is super cool. I've never heard of this way of structuring teams, the fast thinking Thinking fast, thinking slow. The Kahneman. It's so interesting for the fast thinking team. Do you find there's specific archetypes of people that are successful there? Is it a lot of like bringing in new people that are not just used to the way of working at our table? What do you find?
- [23:52]
A
We, we have a mix. So you know, we brought in, I mean we're always hiring, right? Like there was never a point in, in the company's life where we stopped hiring and that, you know, candidly, even when we had to do two riffs, right, that, that significantly, you know, kind of reduced our headcount. You know, we had just like way too quickly grown and overscaled the business at a certain point. But even when we did our rifts, we were still actively recruiting and hiring, you know, in I mean every major department, but especially in, in epd because you know, it's always been my belief that like you, you all like it would be arrogant to say that we have all the people we ever need. We already in the roster today, right. Like we're always going to need to find new fresh perspectives, new skill sets, et cetera. And so you know, we, we've continued to hire, I think we've learned as we've gone along of like, you know, what is the ideal type of hire. And you know, we've done some aqua hires and learned from that as well. But I think the fast thinking part, it really just requires a, a lot of like somebody who's able to operate with a lot of autonomy, right? Like know who's entrepreneurial in nature now. It doesn't mean like they have to literally be a former founder. I know some companies are, you know, like rippling for instance, does a lot of actual acquisitions and gets actual founders into the company. Like we found that, you know, that that's great and we've done some of that as well. But like also there are some really, really capable people who like we didn't literally have to like acquire in and yet they're just able to like think full stack about the problem and the user experience problem. Not just meaning the technical layers of the problem, but also what is the wow factor we're trying to create so tangibly. We're doing this new thing that's about to ship where not only can you describe the app you want to build and then iterate on it with our conversational agent Omni and it builds it with the existing Airtable platform capabilities, but we're also giving it the ability to actually do codegen to Extend those apps with like really final mile, very bespoke functionality or like visuals, right? So you could say like, hey, generate me a very, very specific type of map view with like this kind of like heat mapping and this kind of like, you know, icons and when you click it, do this. And like that's a capability that like, there's so much ambiguity in some of the design decisions around it. Like, you know, and you have to blend that design thinking with some of the technical constraints of like, what can the AI models actually one shot effectively. And if not, like, how do you add in like the right human workflow for approval and review and then reprompting and so on. So just so many different like design decisions and you need somebody who can like really think full stack about that kind of product and is not overwhelmed by that, you know, kind of open evidence, but like relishes in it.
- [26:39]
B
I was actually playing with it before we started chatting. I made a really cute startup CRM.
- [26:43]
A
Oh, that's awesome.
- [26:44]
B
Yeah. Started talking omni over here. It's like the colors are beautiful. That's what's standing out to me right now.
- [26:50]
A
I will say like, just as a note, you know, I consider myself like at my core like a product UX person. Right? Like that, that's my like passion and you know, everything else I've had to learn to kind of run this company is almost like what was a necessary part of the journey. But my real passion is thinking about product ux. Right. And I think of UX in a deeper sense than just like the cosmetic design, like what you could put into a framer kind of prototype. I think of it as literally what should this product do and how should it represent that and behave for the user that is the product, in my opinion. Right. And of course then you have to figure out like technically what's possible and how to implement it. But I think to me, what's under executed today in the world of AI products is there's so many awesome capabilities of AI and most of them are really under merchandise. And there's very poor actually visual or otherwise metaphors or affordances given to users to help represent or understand like what those underlying capabilities are. Right. Like, I mean, chatgpt obviously, like, you know, extremely successful products of not knocking it at all. But like you come in and you just get this like completely blank chat box right by default. And now they have suggestions underneath and so on. But like, you know, the product UX part of me is just like craving more visual metaphors or colors or Some kind of like use the canvas of a web interface and all the richness of interaction you create there to better represent or show all the different things that you can do with the underlying model. And so that's something we've tried to do with airtable is show all of the different states and use colors even to play those up.
- [28:44]
B
It's interesting how much of this connects with. I just had Nick Turley on the podcast, he's head of ChatGPT at OpenAI and, and he had these two really interesting insights that resonate directly with what you're describing. One is he has this concept of whenever something is being worked on, he's always asking, is this maximally accelerated? How do we move faster? Is the. If this is important, what would allow us to move faster? And I love that. That's one of the themes that's coming up as you talk is just this creating this very clear sense of speed. And you even call it the fast thinking team, like you are going to move fast.
- [29:15]
A
Yeah.
- [29:16]
B
And then the other one is just this insight that with AI you often don't know what it can do and what people want to do with it until it's out. So there's this need to get it out and that'll tell you what it should be.
- [29:29]
A
I couldn't agree more with both of those. And particularly on the second point, I think it's interesting. Clearly there have been companies that have both been successful in PLG and kind of more sales led kind of distribution for AI products. You know, the most notable ones I can think of are like Palantir with their AIP deployments. Like that's obviously very sales led. You're not plging into a Palantir deployment. But even, you know, like companies like Harvey and and so on, like, you know, they're doing very well. And like it's primarily from what I understand, like sales led. Right. You're not self serving into a Harvey instance at a law firm. And yet like to me the, the best way to get AI value out there is, is experientially right. And so like you can kind of get that in a sales motion. You can like, you know, show a demo, maybe you can get to a poc, but like it's so much more powerful when you just open up the doors and say anyone who wants to come and sign up and trial this product like can. Right. And I think, you know, it's, to me it's like, you know, kind of a real proof point that like ChatGPT is arguably like the most successful, you know, kind of PLG product of all time, right? Just in terms of like sheer scale of users, like they announced 700 million. Like Ma is Mauser.
- [30:40]
B
I think weekly active users, 10% of humans on earth use it weekly.
- [30:45]
A
That's insane. In like how many years, right? Like a few years.
- [30:48]
B
Three years? Under three years.
- [30:49]
A
Yeah. And so like, I mean literally that is just like the most insane ramp curve. And I don't think they would have gotten there if like you couldn't just come in and literally try the product out. Like, and you know, kind of as a little bit of a rebuttal of the point I made earlier where like, I think ChatGPT doesn't do a ton right now. And even earlier like they, they did even less to like expose all the different ways you could use it, but they just made it so frictionless to just try it for yourself that you as a user could come in and just literally ask it anything and see how it did. And of course like, you know, people in the early days tried to stump it and showed like, oh look, see, it's not that smart. Like it doesn't answer this, this hard question really well. But like clearly the magical like, you know, kind of nature of it still appeal to you enough. Everybody used it. And so I think I do have a view, like we've gone through that whole kind of arc of we started plg. I'd like to think airtable was one of the kind of PLG darlings of our era. And anyway I kind of started moving up market and doing more sales execution, although that was still always on top of usually PLG within an enterprise. But we started doing more and more sales execution. We still have that, that's still really important for our business. But I also think like me personally, like one of my goals is to shift my attention back into that kind of like, you know, builder led adoption and like literally showing in the product experientially not telling in like a deck the value that you can get from, from AI in our table. Right. Like, I think that's so key and it's, it's, you know, it's nux, but it's also more than that. It's not just like literally how do you onboard somebody into the product? You know, it's like literally thinking about the entire product experience itself. Right. And in our case like we just like made the entire product experience AI centric, right? Like it used to be that like, you know, we had kind of this like secondary thing that you could ask questions to the assistant. Sidebar. We now made our agent the default way of doing everything in airtable. And like, you know, it's like now the, the Airtable app, as you know, it is almost like an artifact that's manipulated by, you know, and kind of like can be tool used by the agent.
- [32:58]
B
Let me follow that thread. So if you go to airtable.com today, it looks like basically all the other AI app building sites. Now it's just tell me what you want to build. Thoughts on that as just like a thing everyone's starting to do. Is there. What do you think comes next? Is this, is it working?
- [33:15]
A
Well, there's clearly an incredible magic to vibe coding and app building with AI, right? And this is actually, you know, like a prime illustration in my view of that concept we talked about a second ago, which is, you know, as capabilities of these underlying models evolve, the form factor in the product, UX also needs to evolve with it, right? And so like the earliest models, like the kind of original ChatGPT, like GPT 3.5 kind of era models were not nearly as smart as the current models, right? And so like, you couldn't really ask it to one shot a more complicated chunk of code or certainly not like a full stack app and expect it to work. And so the right form factor for leveraging those models in a software creation context was GitHub copilot, right? It's like autocomplete a few lines of code at a time, right? But you know, you couldn't chat to it and tell it like, build me this entire app from scratch. Right? And I think that like, as the models got better and better, you saw that the new form factors emerge. Like, I think Cursor did a great job of like being an early pioneer of this more agentic way of leveraging the models to do more complex things and generate more, you know, kind of larger chunks of code. And now with Composer, you can literally just go into Cursor and build an app from scratch. Like build me a 3D shooter game from scratch and just watch it go and like create all the files and, you know, fill out each file and then like, you know, like the thing actually runs some of the time. And so to me, this is where the world is going. The models are clearly getting smarter. And, you know, if you think about the original vision of airtable, it was always about democratizing software creation. Like, we just strongly believed that, you know, the number of people who use apps far outweighs the number of people who can actually like build their own or manipulate apps and like harness like custom software to their advantage.
- [35:08]
B
That sounds very familiar. Very familiar these days.
- [35:11]
A
Yeah, exactly. And, and so like I think this is like, it's a different means to the same end. And so like it's almost like we have to lean into this because if we started Airtable today, like this is what we would be all in on. Now I think that the advantage that we have and like, I do think you have to be realistic to yourself, especially as, as a, as a company that predates Genai and now has to kind of find your new footing in the AI landscape. Like you can't fool yourself and just say like, okay, I'm going to throw in some AI stuff on the landing on the marketing site, you know, put in a couple AI features and call it a day. Like I think you actually have to take a clean slate approach to saying like, how would our mission best be expressed? Like if you were literally founding a new company from scratch with the same mission, how would you execute on that mission using a fully AI native approach, right? And then by the way, like, do you have useful building blocks that you can leverage from your existing product and your existing business or are you literally worse off having this legacy asset versus starting something from scratch? And I don't think the answer is always yes or no. I think it just depends on the product. And if you can't really introspect and say like, look, I think I'm better off doing this with the pieces that I have for my existing business and product, then I think you should sell, right? Like you should find a buyer for that company and then go and like, you know, if you really care about this mission, like go and start the next carnation of it, right? In my case, like I really, you know, thought about this and like really feel strongly that the building blocks that we have, like these no code components actually do allow us to execute better on this vision than if I had to start from scratch, right? Meaning like the problem with vibe coding, especially for building business apps. So I should clarify that. Like, you know, we want to democratize software creation, but specifically we are focused on business apps, right? We're not trying to be the platform where you create like a cool viral consumer game. This is for like your CRM, right? Or if you want to build an inventory management system as a small restaurant or a lawyer trying to build like a case management system, like that's what we've always been been focused on. And I think in this AI native world, clearly you should be able to generate those apps agentically. And yet if you have an agent that has to generate every single bit of that app from scratch from code, it's going to be very unreliable, there's going to be bugs, there's going to be data and security issues and then you're also going to have a context collapse as it just cannot manage all of the code that it's written. Basically as the app gets more and more complex, right? And what we actually have are basically these primitives that the agent can manipulate and use without having to literally write the code from scratch to represent. Like here's a beautiful crud interface on top of the data layer, right? Like ours is real time and collaborative and really rich and has collaboration on it. And by the way, here's all these other view types and a layout engine for a custom interface, you know, a layout, right, or automations and business logic. And so it's almost like in programming terms, like the airtable pieces in our Lego kit today can be used by this agent as almost like a more expressive dsl, like a domain specific language to build business apps instead of literally having to write everything down to like the SQL and HTML and JavaScript to build every part of that app from scratch. And so like if we can combine the best of both worlds, like we have these very reliable high quality Lego pieces now, an agent can go and like assemble them for you instead of you just using the GUI to do that. And by the way, if you do want to fall back to the gui, there's a really great kind of way for the non technical user to still understand and participate in what's going on. Whereas if you're not technical, you can't inspect the code underneath a V0 or lovable or replit app, right? It's just kind of opaque to you and if you can't re prompt it to get what you want, you're kind of stuck. This is much more akin to a developer using cursor can generate lots of code but then can still drop back to the IDE to edit and manipulate it to the final kind of production ready state. So that's kind of the play that we're making. And if I didn't fully and truly believe we have a better shot at doing it with our existing product, I wouldn't be running this company in its form.
- [39:25]
B
Today I'm talking to a lot of founders that are going through the journey you're going on, which is we've had a business for a decade, AI emerged and wow, we gotta Figure out something that works that could work even better. And so I'm trying to pull out the threads that are consistently working across these journeys because I think a lot of companies are trying to figure this out. So one that you just touched on is just if you were to start today, what will you do? Like, what would that business be? Plus, how can, how can do we have an unfair advantage with the thing we've done in the past that feels like an important ingredient. And then the other, circling back to stuff you've shared already, there's just like creating a sense of urgency and pace and getting people to understand this is how things move in AI and we need to create this fast thinking team. I love that metaphor and framing. And then there's the point you made about just talking to AI regularly as the founder feels like an important element. Just like to truly be this icco, talking to AI, working with AI regularly. Just on that note, a little bit more just to give people a sense of what this looks like day to day. So you're talking to Omni all day, trying to under flex the power of what you can do and iterate on it. Is there anything else you're doing day to day that helps you figure out what to do for the business?
- [40:38]
A
One, I try to use as many different AI products, including not Airtable, right, Like as I can and both literally for the novelty factor. And just like, you know, some new cool demo comes out, like Runway released their like immersive world, you know, kind of engine, right? And, and so like I'm going to go try, try it out, right? Like when Sesame AI put out their like cool like kind of interactive voice voice chat, you know. You know, demo. Like I tried that out because like, even though we don't have a direct and near term, like, you know, kind of need for like really realistic and interruptible, like kind of voice mode where it's not as core to our capabilities. Like I just want to understand and like get a feel for everything that's out there, right? And I try to invent little like kind of almost like side projects of my own to have a real kind of reason to use these products. Like, you know, oh, cool. What if I were to take like a. What if I were to like try to create like a funny little like you know, like a short, a funny video short, right? Using a combination of like hey Jin. Avatars with like a script, like a comical script generated by AI, right? And maybe it'll be on like an interesting topic. So I'll do like Deep research on the topic with ChatGPT and pull together the results, have it compose like, you know, kind of a little.
- [41:58]
B
Did you actually do this?
- [41:59]
A
Is there something like, that's literally an example of something like just, you know, a fun weekend project. And like, to be honest, like these things only take you like an hour, right? If you're, if you become kind of pretty, pretty proficient with using the products, like, they're all so easy to use. Like, you can literally do the deep research thing. You know, kick off query, make a coffee, come back in 20 minutes. Okay, like, let me, let me prompt it to like generate me some dialogue. It's a little bit like what Notebook LM does for you out of the box. But sometimes I like to just like do it myself, right? And then, okay, let me take the script and like cut it up and like, you know, turn it into a hey gen avatar and then download the video and like play it, right? Like, and just for fun, right? I'm not like trying to make, make that into an actual like, you know, kind of YouTube, like video business. But, but I think like coming up with like these different like fun weekend projects is a really useful construct to like force myself to actually try these products in a more than just like a twitch click way. And you know what, what it gives me is like a, like it's not just understanding the models, which is also very, very important, right? Like GPT5 came out yesterday. Been playing around with it a bunch, just on a variety of different personal use cases. But there's a difference between just understanding the model but then also understanding the product form factors in which they can be placed. Meaning when you apply the model in a more structured way, when you apply the model with different tool calling than maybe what ChatGPT has in its kind of like out of the box form, you know, when you apply it with like, you know, kind of a more agentic workflow, again, that might be different from like what ChatGPT gives you out of the box. Like that's when you kind of learn like, you know, you really get to inspire yourself on like what are the products form factors that these new models can take. So like, and plus by the way, like, I find it to be really fun. Like there is a, to me like a delight and entertainment value to just using AI, period. Because like A, it's not like perfectly predictable. So I think the element of like you're not quite sure what you're going to get, it's like a box of chocolates and B, it always blows My mind just to think about, like, wow, five years ago we didn't have any of this stuff, right? AI was like, okay, we can do predictive analytics. It's like there's some basically very advanced kind of regressions that we could run with AI, but it looked nothing like this in its current form. And it's just actually super fun, in my opinion, to get to play around with all the different types of products that come out. So I think that is a big part of it. Because on the point about the pace of the world moving so much faster in AI than any other landscape, it's, you know, in SaaS. In the mature SaaS era, it was important to study your competition, right? Like if you were building a SaaS company, you'd be crazy not to follow Salesforce, right, every year and see what the major releases they're putting out are or servicenow or so on. This is the equivalent of that. But there's major new releases and products and so on every week, right? Not like every year. And so I just think you have to stay abreast of all of it all. And combining this with our point earlier of like, a lot of this has to be experienced, not just like read like you can't just read like the write up on TechCrunch or, you know, even a tweet about like a new capability. Like, you kind of have to try it to really get a sense of like, what it is.
- [45:33]
B
Today's episode is brought to you by Anthropic, the team behind Claude. I use Claude at least ten times a day. I use it for researching my podcast guests, for brainstorming title ideas for both my podcast and my newsletter, for getting feedback on my writing and all kinds of stuff. Just last week I was preparing for an interview with a very fancy guest and I had Claude tell me what are all the questions that other podcast hosts have asked this guest so that I don't ask them these questions. How much time do you spend every week trying to synthesize all of your user research insights, support tickets, sales calls, experiment results and competitive intel? Claude can handle incredibly complex multi step work. You can throw 100 page strategy document at it and ask it for insights, or you can dump all your user research and ask it to find patterns. With Claude 4 and the new integrations including Claude 4 Opus, the world's best coding model, you get voice conversations, advanced research capabilities, direct Google workspace integration, and now MCP connections to your custom tools and data sources. Claude just becomes part of your workflow. If you want to try it out. Get started at Claude AI Lenny. And using this link, you get an incredible 50% off your first three months of the pro plan. That's Claude AI Lenny. For people that work for you across Airtable, say, The product team, PMs, maybe engineers, designers, how have you adjusted what you expect of them to help them be successful in this new world?
- [47:02]
A
One is, you know, really, really, really stressing this idea of, like, go play with this stuff. And I mean, when I say play, I really mean play. Like in. In the psychological sense of, like, you know, it's. There's a difference when, like, you go in and you're kind of just trying to check the box and like, get a job done, right? There's a difference when, like, you come in with a curiosity and like, you're kind of like exploring, right? And it's both more fun and energizing. But also, I think, like, you learn more through that, right? And so, like, I've really tried to stress the value of play with these AI products. And I kind of, you know, try to lead by example by like, literally going and like, sharing out links or screenshots like, you know, of the things that I'm doing in these various products. So, like, you know, as an example, you know, like, I will go into, you know, like one of the prototyping tools and show like, hey, like, you know, I built a marketing landing page for, you know, this new capability we're launching. I kind of created like a landing page for it in replit, let's say. And now I'm sharing that link instead of, you know, what typically, like, we would have done in the past is like, okay, we're going to write a doc about it and then share the doc. I'm just going to show you like an actual landing page with like, visuals and everything in there, right? Or like, I'll share, like, you know, the actual link to my deep research reports. Or like, instead of me writing a perfect memo on a topic, like, I'll actually just like, prompt my way into getting like a chat thread or a chat output that basically covers all the content that I care about and maybe even, like, ask it to like, okay, summarize this all into like a final, you know, kind of like memo output and then intentionally share that rather than expose the fact that, like, I'm using AI in this way. And here's literally how I'm prompting it so you can follow along as well, you know, but really trying to encourage everyone to, like, go and just play with these products and I've even said, look, if anyone wants to just literally block out a day or frankly even a week and like, like have the ultimate excuse, like you can use like, you know, you could say that I told you to do it right? Like if you want to cancel all your meetings for like a day or for an entire week and just go play around with every product, AI product that you can find, that you think could be relevant to Airtable, go do it like period. So I think that's the most important thing is like this display, this experimentation. I think there's also a lot of other kind of shifts in how we execute prototypes over decks. I want to see like actual interactive demos because again it's hard to, to, you know, in a deck or in a prd you could say like, okay, well we're going to make Omni really good at handling this kind of app building. Okay, Those are just words. The real proof is in the pudding of like, okay, let me try it out on a few like realistic prompts that I can imagine. And in a demo, in a real prototype you can like instantly, you know, try it out on realistic rather than golden pathy scenarios and see how it feels too. Like is it, does it feel too slow? Like do we need to expose more of the reasoning or steps kind of that are happening behind the scenes, create a progress bar or something like that. But it's really hard to get that feel of the product with anything but a functional prototype that really does in an open end way use the AI to do whatever you put in. So I think it's more like a like experimentation playground. It feels like how we need to execute versus I think in the past it sometimes felt like a more like deterministic resourcing and like kind of timelines view of execution. Right. Like we're going to put this many people on this problem and this is the eight week timeline to this milestone and we're going to ship in a quarter from now. And like I think now the whole thing is just like a lot more experimentation and iteration driven of the different.
- [50:57]
B
Functions on a product. NPM engineering design. Who has had the most success being more productive with these tools and how do you think this will impact each of these three functions over time?
- [51:08]
A
What I found is that it really does become more about individual attitude and maybe some like, you know, polymathism. Like you know, there's a strong advantage to any of those three roles who can kind of cross over into the other two. Right. Like kind of the hybrid unicorn types. Right. So if you're a designer who can be just technical enough to kind of be dangerous and understand a little bit of like how these models work and you know, like, how does tool calling work and, and all of this stuff. Like then you can actually design a concept or even prototype a concept, including in these prototyping tools. That, that's much more interesting and maybe realistic than if you're just stuck in kind of the flat. Like, let me put something in a static design, right? Concept, right? Because I think, you know, designs have to be more interactive. Like the, the whole, the, the, the value of the product and the product functionality is in the interaction of it. Right. Like, you know, think about the design of ChatGPT. Again, it's like, you know, it's the most basic design could possibly imagine. The real design actually is happening underneath the hood in how it responds to different queries. Right. And what happens after you fire off a prompt. Right. So, you know, I think like, I found that there are people within each of these functions, like there are engineers who are very good at thinking about product and experience and like, you know, kind of can, can go and prototype out like the whole thing. There are designers who can kind of do, do the same even if they can't literally code, they can prototype something out, like literally using a prototyping tool. And I think that's where like, AI tooling is also giving more advantage to people who can think in this way by equipping them with an alternative to actually having to go through the long hoops of learning CS. Right. And the PMs as well. I think, like there are some PMs who are like really getting into the technical details and studying up on like, you know, how does this stuff work? And actually getting hands on rather than seeing the role as, you know, kind of writing documents, writing PRDs.
- [53:08]
B
Do you see one of these roles, I don't know, being more in trouble than others? Just like you need fewer of these people in the future potentially.
- [53:16]
A
I think overall you can get more done with fewer people. And that's not to say like, you know, we want to go and like, like make the team smaller, but rather like, like the really cool thing for, for us and I think a lot of other companies is it's not like you have a finite set of things you need to do and execute on from a product standpoint. And okay, like now I can do that with a tenth of the people. I mean, you could do that in a lot of cases. But like, for us, maybe it's also because we're A very meta product, right? Like we are the app platform with which you can build now any AI app. With AI, right? The apps themselves leverage AI capabilities at runtime, whether it's to generate imagery for a creative production workflow or you know, kind of leveraging deep research or AI based like, you know, kind of crawling of the web to search for companies that match a certain criteria for your deal flow app, right? Or something like that. Like we can effectively leverage all of these different AI capabilities in this, this kind of like app platform because by definition we're enabling our customers to build apps that have this wide range of AI capabilities. But because of that it's like we have a, you know, kind of almost infinite like set of possible AI capabilities that we could execute on, right? And I'm always telling the team, like, look, like the great news is like we have, it's like we have all these fruit trees and like there's so many crazy low hanging fruit, right? Like, and you got literally like massive watermelons, like literally sitting on the ground, right? And all you have to do is like kind of walk over 20ft and pick it up instead of having to climb the really tall coconut tree to grab like a hard coconut from like 50ft up. And so like there's so many watermelons on the ground, just go out and like start finding the biggest ones and attacking those, right? And like, and what that means is that like if we can build this culture, and I do think like it's a learnable way of operating, like I, I, I really like to believe in like the like the growth potential of like any human, right? Like, and any individual, like I think if you really have a growth mindset, and that's why one of our most important core values is growth mindset, right? Like if you really have that growth mindset, I think like, especially if you're willing to put in the nights and weekends hours or in my case like I'm literally telling people, like, take a full day off, take a full week off and learn this stuff. Like you can, you know, become more fluent in this way. And I think then what we get is like a team that can just go and work on more things in a much more leveraged and fast way way, right? So I like to think like, you know, people who are willing to jump on the train are just going to become more and more effective. And it's not like, oh, like as a pm my role is becoming entirely irrelevant, right? Like, no, it means that as a PM you need to start looking more Like a hybrid PM prototyper who has some good design sensibilities. And by the way, like, I think some of the best eng, PM and design cultures respectively, over the past even few decades have always been multidisciplinary in nature. Right? Like the original PM stack at Google required the PMs to actually be somewhat technical so they could understand the engineering, you know, kind of limitations of, of like the product, you know, designs they wanted to make. And they had to be kind of designy, right? Like, I remember my, my co founder Andrew, when he was in the APM program was like always reading books about like design, like even down to like visual design and color theory and that kind of thing. Right? And so I think it's just a reminder that you know, like designers as well, like the, you know, some of the best designers, if you're a designer at Apple, like, you know, including hardware designer, like, you have to understand some of the technical capabilities of how this stuff works, right. And if you're an engineer, like I think some of the best engineers and maybe Stripe always had a very good engineering culture of engineers who could think about the product and business requirements. In fact, like, you know, on any given product group at Stripe, my understanding is that like, you know, the DRI isn't always the pm, right? Like, as is traditionally the case in kind of that, that triangle. It's like, you know, sometimes it's actually the engineer who's taking the product lead and saying like, this is what we need to build.
- [57:24]
B
So what I'm hearing is essentially, if you want to like, the trend across product engineering and design is each of those functions needs to get good at one of the other functions, at least. Yeah, ideally you can do them all, but if, if you can just do one additional. So a PM becomes better at design and Engineer becomes better at product management.
- [57:43]
A
Well, I would actually go further and say, like, I think you need to get like decently good at all three. Like there's just a minimum baseline of like, if you're any one of those roles, you need to be like minimally good at the other two and then you can go deeper into your own kind of specialty. Right? Like, you know, you could be a designer who's really good at thinking about UX and interaction design and then just like good enough to be dangerous on thinking about like what's technically possible and like what is the product, you know, kind of, you know, kind of story around this, this feature.
- [58:17]
B
I love that. And to do that, one piece of advice that comes up again, again in what you're, what you've been describing is using use the tools constantly to see what's possible. And that will teach you a lot of these things.
- [58:28]
A
I think use well, use the tools gives you exposure to what's possible, right? It's kind of like if you want it to be a great industrial designer and let's say like, I mean the chair is kind of the ultimate like hello world of like industrial design, right? It's like the like canonical design object. Like you wouldn't just sit there in a vacuum and with no familiarity with like the materials that you can use, plywood, steel, whatever, or like existing form factors of chairs. Trying to invent the world's best chair, a vacuum, right? Like you should go and first do a study of like all of the best chairs out there today. Like go look at an E chair, sit in it, like try to examine it to kind of reverse engineer how it was made, right? And like, you know, and just look at the prior art for that type of product. Like that's how I see the go out and play with these products. And also I think like actually going and designing or implementing or executing is the best practice. So like you can't just only go and look at other people's shares. Like eventually you have to go and like actually try building your own and then try building another one and another one and another one. And so I think that's where like, you know, when I think about how I honed my own product UX sensibilities, like I never like, I mean, you know, and at that time like that I was in school and kind of learning about this stuff, like there wasn't really any good curriculum for ux, right? It's not like there were like great, you know, college classes to learn product ux. I mean even CS was like very academic in nature at that time. It wasn't applied software engineering, like build an app or whatever. Maybe now at like some of the schools like Stanford, mit, they have like actually UXE type courses, but it's, it's still a rarity for most people to have access to that. And so like the way I learned like all of my product sensibilities was just like trial and error and like also using and studying other products, right? And then going and trying to build like my own weekend project ideas, right? Oh, I want to build like a Yelp style app with a map view and then also a list view. And I want it so that when, when you pan around in the map for it to automatically update the list View and maybe there's some UX improvements I can make on top of that. But I can also like test my technical skills to, to figure out like, which parts of this are hard to implement and like, how do you make it work and what are some of the design changes or affordances that you can use to kind of like map to like the technical possibilities to do that.
- [60:44]
B
I loved your piece of advice, which I forgot to double down on, which I also find really powerful. The best tip there is find something to actually build that is useful to you and fun. Like pick a project that's like, oh yeah, this would be fun to do. Have like a problem you're solving that forces you to actually do this thing for sure.
- [61:00]
A
And look, I think that can be like night and weekend projects. It can also be like the daytime job projects, right? I mean like, I am basically telling our teams on the AI platform group especially, like, look like, you know, in that low hanging fruit metaphor, it's like, I'm not being prescriptive with you on like, which watermelons you should pick, but like, you should go and like, and we do have different like pods within that group. But one of them, for instance, is what we call the field agents team and they are responsible for the agents that work within your app. So this is not the agent that builds your app, but these agents that run on a customer's behalf to do like web research on your customers. Or they can go and analyze a document and like, in the future maybe do things like actually generate a, like, prototype, like of, of a, of a feature, you know, from a priority or from like a feature idea. And you know, I'm telling them, like, look, like there's a almost infinite number of things you could, like superpowers you can give these field agents, I'm not going to tell you which specifically to do now. You can ask me to weigh in for sure. But like, you should go and like, you know, just experiment and prototype like a few different versions of like a few different directions. We could go like, what if you prototyped what it would look like to have a deep research implementation in field agents so that like for any given row of data, let's say in your case it's podcast guests, you can just click a button or click a button on mass across the entire, like every speaker you have lined up to do deep research, like powered by ChatGPT's own deep research on each of the speakers and have them all laid out side by side in this table, right? Like go prototype that and See how like, you know, see how it feels and looks like. And so I think some of this stuff can also be like in your daytime job, especially if that daytime job is literally to go and build AI functionality.
- [62:47]
B
I actually tried to do exactly that. The problem I ran into, I wonder if it's changed is there's no API for. For chatgpt Deep research yet.
- [62:55]
A
There is now. There is now.
- [62:56]
B
There is. There we go.
- [62:58]
A
Ends up being. And I think they only recently exposed it. It ends up being like something on the order of like a dollar plus per research call, which.
- [63:05]
B
What a deal.
- [63:05]
A
I mean again, exactly. I mean some people would say, oh my God, that's so expensive. And you rack up 50 of those. You've cost $50 a month. I think it's like, well, it just saved you like hours of research by human.
- [63:16]
B
Not only that, I actually have a researcher that I pay to. He'll give me background on guests. That was like four or five hundred bucks and the dollar sounds great. And I, I've been doing this manually.
- [63:29]
A
Smart. He would be using deep research and then just collecting the.
- [63:33]
B
They might just be. Oh man. Okay. There's one more skill I wanted to talk about real quick. This comes up a lot in these conversations is evals.
- [63:43]
A
Okay.
- [63:43]
B
The power of getting good at evals. I know there's something you value highly. Talk about just why you think this is something people need to get good at.
- [63:50]
A
Yeah, I mean, and I listened to your episodes with and Mike who talk about this. I think it's like interesting that Both heads of OpenAI and Anthropic have converged on this point. I mean, look, I think I would add a slightly different or additive take though, which is I think for a completely novel product experience or form factor, you should actually not start with evals and you should start with vibes. Right. Meaning like, you know, you need to go and just kind of test in a much more open ended way. Like, like, does this even work? Like you know, in kind of like a broad sense. So like as an example for our custom code generation capability, like instead of defining evals that get repeatedly tested and you know, as you vary like the prompt or the model or like the agentic workflow used to generate these outputs and you have to define like, you know, what does good look like? Right. By definition for the eval, I would first start with a much more open ended and like ad hoc style of like just throw stuff against the wall, like try different prompts and see how well it does. And to me evals are More useful a once you've converged on the kind of like basic scaffold of the form factor and you kind of know what are the, the use cases you want it to work well for and what you want to test against it. Whereas in the early days, especially if your product market fit finding either for an entirely new company or for like a new, a pretty dramatically new or bold new capability that doesn't really have like it's not an incremental improvement on something that exists in airtable today. Like I think you have to just be a little bit more creative initially in like throwing stuff at it, seeing what works to understand. Okay, like let's use an example. You know, we're implementing this new capability that can use basically a long running AI crawler agent that goes and researches the web for a specific type of object or entity, right? So it's a little bit different from deep research, similar to deep research, but what it actually does is instead of outputting like a, you know, kind of a report, it's actually going and compiling a list of things. The things could be companies or people or anything else, right? Like find me every Marvel movie, right, ever made. Find me every like you know, kind of DC comics like spinoff, right? Like series, right? Literally anything. And you know, you have to go in at first, like just try out a bunch of random like, you know, use your own brain to think of like what are all the, like what's the range of use cases I can test this against, right? And then you get back some results and you're like okay, well like it's clear that like where it does really well are these types of searches, right? Like people and companies with this kind of parameter. And I think to me like evals are useful once you have like a sense of like what is that cluster of useful use cases you can start then more like programmatically like measuring the changes that you're making to improve the output for that. Right? But by that point you've probably already scoped the product and maybe the way we would merchandise it in airtable is not a completely open ended capability. But hey, here is a specific capability that can research one of these X number of entity types including people and companies. And here's even the filter conditions or criteria that are more explicit that you can define to give it the prompting to search for that thing, right? But I kind of think it's more useful as a way to iterate your way to improvement and you can start really testing stuff empirically. You can a b test Especially if you have the scale of a really large product like Anthropic or OpenAI. You can just test everything and see, oh, this model actually performs better than this one, this product performs better than this one. But I think early on you don't have that luxury and you're in a much more open ended discovery process that is very wise.
- [67:52]
B
Evals can constrain you too early. I think about just the double diamond. I don't know Ido kind of framework of like be con. Divergent first and then converge and then maybe.
- [68:02]
A
Exactly. I hadn't heard that before, but that, that completely resonates.
- [68:06]
B
Okay, let me try to reflect back some of the advice I've been hearing about how to shift a company to be successful in this new world. And let me see if I'm missing anything that you think is really important. So one is there's this sense of just like reset the expectations on pace and urgency and help people understand In AI things move incredibly fast. This is how we need to operate. And then there's also a piece of get stuff out so that you can learn how people use it and what it's capable of versus polishing it endlessly. Forcing people almost, I don't know forcing is the right word. But encouraging people to play with the latest stuff and like giving them a chance to take days off to or block out calendars, cancel meetings, just like stay on top of this stuff. Yeah. To play as you talked about it and then sharing things they've learned. Get the vibes of what's possible. There's also this idea of just rethink. Okay, if we were to start today in this world, what would we do to achieve the same mission we have achieved, we are trying to achieve. And ideally it leverages this unfair advantage we have with things we've been working on for a long time. And then there's just like talk to AI constantly. Every hour.
- [69:15]
A
Yeah, multiple times an hour.
- [69:16]
B
Multiple times an hour keeps going up. Is there anything else that I missed there that you're like, you need to do this too to be really, to.
- [69:23]
A
Have a chance, I think just to really, really try to break down role silos. And I think that's true certainly for EPND in the typical EPD triangle. But I also think it's broadly true even for like non product roles. Right. Like I think it's true in marketing. Right. Like I'm seeing, you know, something, you know, something I'm really pushing for in marketing. And I think our marketing team is like, you know, really leaning into Actually is like, you know, if you can just do all of the thing yourself, like traditionally, you know, how a marketing team might operate is like, okay, you have one person who's kind of responsible for executing the performance marketing, you know, kind of part of a campaign, right? Like they literally go into the Google AdWords interface and they're like tweaking the parameters of targeting and you know, budget and like, you know, kind of conversion tracking, et cetera. And then somebody else is actually responsible for like coming up with the specific ad copy. Right. And somebody else yet was responsible for coming up with like the seed content or positioning, you know, guide like written by a PMM that feeds into the ad creative and you know, so on and so forth. Right. Like maybe they're promoting some like new demo asset, right, that somebody else. Yeah, created. And I just think that like, you know, in the same way that you can collapse the roles in EPD and like the ideal person, maybe they're very specially, you know, specialized and deep in one dimension like engineering, but they're well rounded enough to kind of like be dangerous on the other two. Like I think that's kind of true in almost every other function, right? Like, you know, like sales as well. Like I think you should, you know, start to be able to play more of an SE role. Like traditionally salespeople didn't necessarily know the product that well and like, you know, kind of relied on the SE to come in and be the product experts. Like I think it's really hard to sell any kind of AI product now without actually being fluent in the product and be able to demo the product, right? So like, you know, AES need to be like SE fluent as well. So I just think that that concept of like collapsing roles, you know, everybody needs to like become more full stack to do the thing. Like being more outcome oriented, right? Like your outcome as an AE is to like show customers, you know, convince customers of the value of your product and close deals, right? Okay, well in order to do that, like you used to have dependencies on having assets created by marketing and like you know, an SE to help you demo. Like can you collapse more of those dependencies so that if you had to, you could do it all yourself? Right? And I just think that's a new way, like it's a new operating mentality overall. For every AI native company or company that wants to compete in this new arena that is, that is a great addition.
- [72:07]
B
It almost feels like you go back to startup times when everyone's doing a bunch of stuff. There's no, like, here's the head of product, here's the head of engineering. We're just doing stuff. Totally needs to be done. Totally. Yeah. I'm kind of seeing it as this is like upside down T where there's like the thing you're really strong at and then you just have to, as you describe the minimum of being good at engineering design or an SC by the way, sales engineering, imagine is what that stands for that you just like they're adjacent roles. You need to start having a baseline. The baseline is increasing of how much you need to understand that everyone's Venn diagrams are kind of converging.
- [72:41]
A
Exactly.
- [72:42]
B
Amazing. Okay, let me take a step back and kind of zoom out and think about the broader journey you've been on over the past decade. Plus, let me just ask you this. What's the most counterintuitive lesson you've learned about building airtable building and company building teams that maybe goes against common startup wisdom?
- [73:02]
A
You know, I heard, you know, your interview with, with Brian Chesky and then later he talked about founder mode in that kind of YC retreat. And the points there really, really resonated with me, you know, and I feel like maybe less eloquently, I kind of like deduced, you know, some of the same principles just, just in my own experience, which is like, I think when you're scaling up and this relates also to what we talked about before around like the early days of building a company, you're like in the details, you're finding product market fit. You kind of have to be like, you know, pretty versatile, right? Like, you know, all these decisions from a technical standpoint to design to even commercial and like, what's the freemium model going to be like? And like, you know, how are we going to market this product? What does the website look like? Like they're all very intertwined, right? You can't like compartmentalize and then like, you know, almost like factory produce, you know, kind of each of these things separately. Like you, they're all intertwined, right? And you have a very small, tight knit meme that's like a tight knit team that's thinking full stack about all of this combined. And you know, obviously like that's the only way in my opinion to create like that, that magical product market fit in the first place. And then I think as you scale up, you know, the default guidance that you often get from, you know, like operational experts and you know, kind of like larger scale, you know, kind of company investors is like, okay, you got to Kind of industrialize the process of all of this stuff, right? It's kind of. It's kind of like going from like a bespoke artisanal, like one person made an entire, you know, item of clothing to like, we gotta like, factory produce this thing, right? And you know, what that means in a organizational context is like, you then create these different fiefdoms, you hire all these execs, and like, you know, each exec kind of like just manages their own swim lane, and there's relatively looser coupling between all of those different groups, right? So then you got sales and kind of executing on its own thing. Marketing is executing on its own thing. Product's executing on its own thing, rather. And even within product, there's different product groups and surface areas that are each kind of executing on their own thing. And you know, using the factory metaphor, like, there's. There's an argument that that's actually kind of an efficient way to scale up production for each of these different swim lanes, right? Like, each one can kind of operate, you know, like in a. In a more autonomous and like, you know, purely, like, scale up, you know, focus, kind of. Wait, how do we produce more of this thing if the thing happens to be within one product group? Improving search, that's our main focus. We're just going to, you know, go and ship, ship, ship more stuff to improve search. And, you know, so it's not completely crazy, like, you know, why, why people give this advice, but I think what you lose is the magical integrated value of holistic thinking, right? And making the bigger picture bets, right? And I think Brian talked a lot about this on his episode with you, which is like, look like in a company that is really serious about product, first of all, like, you know, I really liked his point about, like, the CEO has to play a CPO role, right? You have to care about the product. Like, ultimately, like, the product is the thing, right? And you can't just coast on scaling up, go to market around the product forever. Like, you got to keep innovating in the product. And by the way, the best way to innovate on the product is not incrementally split over all these different, you know, different little surface areas. But actually to have like a bigger, you know, kind of more step function vision of how this product needs to make a leap, right? Or what's the next big, like, you know, kind of either act of the product or new capability of the product or reinvention of the product, right? And so, like, I think if you really care about doing that from a product execution standpoint and almost like refinding new product market fit on a regular basis, I think it necessitates a completely different operating and leadership model throughout the organization. And all of the stuff we just talked about in terms of how to operate in the AI native era, I think is actually exactly the same as how you need to operate in this constant product market refinding of fit state. So could not agree more with that concept of kind of you gotta think ambitiously and move the organization holistically towards these bigger outcomes, but also ship and learn and experiment a lot more in this era. And then maybe the meta learning I had from all of the above is that the specific advice obviously was like, okay, go scale up in this way or go hire these types of people, experienced operators, et cetera. Obviously there's some truth to that, right? Like, you know, the people giving this advice are not incompetent. You know, they had some reason for giving it. And in certain contexts, that is the right thing to do. But I think, like, my meta learning is, you know, it's not enough to just like, trust the recommendation, like, here's the action you should take from a lot of people because everybody has different priors and it's almost like we're all our own LLMs, right? And like, we all have different training from a different corpus of data informed by our own experiences. And maybe you're trained on like the like, you know, kind of servicenow or the, you know, kind of oracle, you know, kind of, you know, training corpus, right? And, you know, this person's trained on the Facebook corpus, and I'm trained on like, you know, the airtable one, right? And I think what I've tried to do more and more is like, not just like, ignore advice from smart people, like, obviously that's not the right answer, but to kind of take their. It's almost like in an LLM, you can now, with a reasoning model, actually inspect the chain of thought, right? And see how it's thinking. Why did it come up with this answer, right? And to me, that, like, chain of thought, like, why did you recommend this is actually more informative than the actual, like, just do this recommendation, right? So the answer might be like, hey, like, you know, at so and so Company, this is how we eliminated the PM role entirely, right? For Brian, like, at Airbnb, like, made sense. Like, we're no longer having PMs in their traditional form. Now we have program managers and product marketers. But, like, more than the actual decision, because I don't think it's a one size fits all. Like everybody should do the same. Why did you do that? Right. And the why actually was very informative. And then be able to take that and say like, okay, like how would I apply that? And maybe it yields a different outcome. But the reasoning actually is very informative.
- [79:19]
B
It's interesting how this idea of founder mode is not so different from this icco trend that you're following. And it's. Yeah, yeah, it's like being in the weeds, being in the details, trying things yourself, not delegating to execs.
- [79:33]
A
Yeah. You know, and like I think anything taken to an extreme can be problematic. Right. So like there is a world where like, you know, you are so in the details and in every detail that you're basically just micromanaging and you're, you're kind of creating like you know, kind of the euphemism for that. And that's not really what founder mode is about. Right. Like that's not like the, the Brian conception of founder mode is to like micromanage everything and like not trust anyone. But I think it's more about like finding that right balance of being unabashed about caring about the details that do matter and where the tying together of details across, across different groups or departments actually is the only way to yield a non incremental outcome because otherwise each person is just optimizing within their own domain. Right. But you'll never get to the global maxima or the global breakthrough. And you know, I think like the really cool thing about you know, CEOs as ICs and frankly any leader playing more of an IC like role, being in the details is I think for the right type of person, it's actually more fun that way. Right. Like, I mean to be honest, like for me like the times where I felt most disintermediated from what I felt was like the substance of this company was when I thought that I was almost like you know, forcing myself to step away from the details because I thought that's like what you know, a at scale CEO was supposed to do. Right. Like I mean there's you know, some like, you know, famous CEOs who have talked about like the, the less decisions I can make, the better. Right. Like the less details I'm exposed to, the better. Right. Like I just want to inspect at the topmost layer how this business is running and if the everything underneath it is going smoothly then like I'm able to do that. Right. And everything looks good and I Just think that's a maybe again it works in a certain type of very mature type of business. Like you know, even then though, like I can't imagine that like at a CPG company like in Procter and Gamble, you wouldn't want to have a CEO who still actually goes and tastes the soup and like tries the products and sees like literally the details of like what the new product innovation pipeline looks like as well as like how it's being experienced on the shelves and so on. Like, so I don't know, I guess, like, I guess I'm just more and more skeptical that, that like hands off, you know, pure delegation, you know, and process management role ever works as a CEO. Like maybe, maybe you just like you go through a long enough period of like where the business is coasting that like nobody notices. But I gotta say like, for me, like it's just much more invigorating to get to play that role. And I think for, for the types of operators and leaders that I most admire, like, it's, it's like that's what makes the job interesting. Like they don't want to have like a automated away, you know, kind of role as a leader.
- [82:22]
B
If you could go back in time and whisper something in a decade ago, Howie's ear, that would have saved you a lot of pain and suffering over the last decade, what would that be?
- [82:33]
A
Don't step away from the details that both you love. Like, I mean first of all, like if your passion is like building product and product design, even if it feels like at times the company needs to do all this other stuff, like scale up, you know, go to market and operations and like just have like a large people organization that itself creates a lot of, you know, kind of, you know, need to, to do things and manage and like there becomes a new job invented just to like manage a larger group of people, right? And like, you know, obviously you're going to have to do some of that. You can't just completely eschew all your responsibility as like an at scale CEO. But like don't lose the like the essence of like the thing that you love doing and that you know, like really made this product happen and gives you know, this company as many companies that like were founded on like a, you know, kind of a magical product market finding insight, don't like step too far away from that, right? And always make sure that is still your like number one. Even if like other stuff has to also add to your plate.
- [83:45]
B
I think people don't talk enough about this. How Someone starts a company that's an idea they have, they're excited about, it takes off and then you're stuck on that for a long time. And then even if things are pushed in a direction you're not as excited about. And so this point about just remembering what you actually love about it and coming back to that is so important because that's the only way to keep doing this for a long time.
- [84:05]
A
I think that's so true. And to me, that's why there's always been a difference between entrepreneurs who love the actual of building a product or, you know, the business too, versus those who saw a, you know, just purely business or financial opportunity that they felt like they couldn't pass up, exploiting or going after. And look, no knock on people who are more the latter. And like, there's entire industries where, like, it's all just about alpha generation, right? Like, you know, you could go into private equity business and so on. And it's just purely, it's rationally about, like, how do I find the alpha. And I think that like, you know, the. Some of the best companies, production companies, at least in my opinion, are like, you know, run by those people who like, actually just love the product, right? I think you get a feel for that from some of the AI companies. Like Sam, I think genuinely just loves, like working on AI, right. Like, if you could spend 100% of his time on like just being close to the AI and the research, I mean, he won and he's even said as much, right. Like, you know, but, but ranging to like the Brian's with Airbnb, like, like it's pretty clear, you know, that, you know, people like this are not motivated. Like Airbnb was not founded because, like, oh my God, we would want to make a lot of money off this, like, arbitrage opportunity against hotels.
- [85:25]
B
They just needed to pay their rent.
- [85:26]
A
Yeah, well, that, that and like, I think they loved the, the product and I think they also loved the way in which they built the product, right? Like, you know, the design centric nature of that product and company and culture. Like, you know, and, and that's what gives you, like, the continued joy of, of working on, you know, what could be the same company for a very long time.
- [85:45]
B
Howie, is there anything else that you wanted to touch on or leave listeners with before we get to our very exciting lightning round?
- [85:51]
A
I, I just want to reiterate, you know, especially for, for listeners here who, who are in, you know, an EP or D role and especially in the P roll, like you know, I really do believe that this is not a, like you either have or you don't like in terms of the skill set needed to be relevant and AI native. But I do think like it's a call to action to go and bolster your skill sets where, you know, where they may be, you know, less refined right now. Right. Like, I think everyone, like even programming, I really believe like everyone could learn how to be a software engineer if they wanted to. Now like, obviously like some people, just as with like great writers are never going to be like, you know, a published author, right? Or like, you know, the, the Hemingway, right. But like everyone can gain a good enough proficiency of software engineering if they really wanted to. You could take that boot camp, you could do like some like, you know, coding, you know, kind of exercises on the side, et cetera. And the point there is that like, you know, sometimes I think we treat these disciplines like, you know, hard, hard skills that like, if you're not already, if you're already halfway into your career and you're not already an engineer, if you're not already a designer, like, okay, well, you can never be one. And I just think like, you know, our brains are malleable. There's a lot of great curriculum out there to learn and you know, a lot of it, like I said, just comes down to also like trial and error and like building projects, maybe nights and weekends projects even to learn this stuff. But like everyone can learn how to be a versatile, you know, kind of unicorn, like product engineer, designer, hybrid in the AI native era. And, and like the only thing stopping you is like just going out and doing it.
- [87:30]
B
That is a really empowering way to end it. And I just to double down on that. It's never been easier to learn these things. Like there are super intelligences that you can talk to that do a lot like as they're building can help you learn.
- [87:43]
A
I mean, like I literally, I mean I go into ChatGPT sometimes and I ask it like, you know, just like, hey, like how would you build this app? Like, or you know, like I'm just curious, I'm like, like how would you build Manus, right? Like the agent, open ended agent. Like literally how would you build it? You can ask it questions and it's like having like an amazing, brilliant software architect, software engineer, product manager, designer, expert tutor that you can literally like there's no dumb question. They have infinite patience. They're literally on and awake like 24 7. Like it is the most incredible time to like learn this stuff. To your point. And then of course, like the interactive tools to go and actually build stuff. Like anyone can download cursor and just start like asking Composer to generate some code for you and then looking at the code and trying to figure out what it does. And you know, to your point, like, it. You know, when I think back to the earliest era that I experienced of building apps, like, you know, first I learned C, then I learned php and JavaScript and like even building kind of JavaScript, like single page apps in the early days, like 08 through 2010, it was a dark, dark art. I mean there were some. You just had to go and learn some of these things. There wasn't great tutorials for it. You had to reverse engineer certain things. There were just weird things. Like if you wanted rounded corners in your ui, you literally took Photoshop, opened it up, created like a rounded corner in pixels, and then turn that pixel up into an image that you dropped onto the page and at exactly the right position to be at the edge of like a box. Like crazy stuff, right? I mean, everything was like so much more arcane at the time and now it's just, it feels so much more fluid and accessible and like the gap between the arcane tech that you have to wade through to build something has just been minimized so much. It's like the, the effort and like abstraction between you and like the magical, delightful actual building of the thing that you want has been so minimized. So it's never been a more exciting time to be a builder.
- [89:47]
B
You remember spacer gif to create the line stuff. You just.
- [89:53]
A
Yeah, I remember.
- [89:54]
B
Invisible one pixel thing that you just stick in places. Yeah.
- [89:57]
A
No.
- [89:58]
B
Oh my God. What a time to be alive, Howie. With that, we reached our very exciting lightning round. I've got five questions for you. Are you ready?
- [90:05]
A
Yes.
- [90:05]
B
Here we go. What are two or three books you find yourself recommending most to other people?
- [90:10]
A
You know, I've been trying to read fiction more, partly because I think it's just like a really nice mental reset. I will say like three body problem. Like for anyone who hasn't read it, like, it's a mind expanding book. Like I like sci fi and fiction that like kind of opens your brain. So maybe this is my cheat card. But you know, it's a three book series. Those are three great books.
- [90:30]
B
I love that series. And my tip there is it gets good one and a half books in is my tip. So just keep reading. That's where it's like, okay, now I'm in.
- [90:39]
A
I liked Even the first one. But I do like it. I felt like it was like Inception where every book, every subsequent book was like you dropped into another like you, you incepted into like another layer. Right.
- [90:53]
B
Awesome. Okay, what's a favorite recent movie or TV show you've really enjoyed?
- [90:57]
A
TV show. I just started watching the studio. The. It's like the Seth Rogen, Rogan Ron. Yeah.
- [91:04]
B
So stressful.
- [91:05]
A
Yeah, it is very stressful. And you know, I just kind of like. I mean Silicon Valley was like too close to home when it came out. So like I watched it, but it was like just cringy. The studio is kind of fun to watch because like it's, it's, it's a little bit of that like inside baseball of Hollywood. And yet like I'm not in Hollywood so it's like entertaining to watch. And it's just, you know, it's, it's, I thought smart and funny show and because I split time between LA and sf, I also feel like it's very real to me. I see a lot of the literal characters out there in the world that it's characterizing.
- [91:43]
B
Do you have a favorite product you recently discovered that you really love? Could be an app, could be gadget, could be a clothing.
- [91:48]
A
So, okay, so I'll give two because I feel like I have to say some kind of software product, right? I mean, I'm a really big fan of Runway, the product and the company. I just think like, you know, every new model they come out with, they just came out with a new one just I think like two days ago. That gives even more like controls and refinement on like creating exactly the video scene that you want. And so like I think just the photorealism in what you can generate now. And they also built this cool demo thing that's an immersive world generator I mentioned before. I think it's just cool to see. I also like the underdog story. I'm clearly Google's gunning in the space has VO3 and so on and as is OpenAI. But I love the underdog story of this sub 100 person company still punching above their weight and building really awesome video experiences. Right. So that's the software one. And then a very, very kind of nerdy real world answer on product is I kind of just recently got into like this whole cottage industry of artisanally produced, you know, basically clothing, you know, by like small scale like Japanese manufacturers that use like, like literally like 100-year-old looms to, to make clothes. Like the old fashioned Way like you know, or, or the old fashioned industrial way. Right. Like they have these like loop wheeler machines and they spin the cloth in like a very slow pace. So it's completely impractical from like a production scale standpoint. But you know I just like, I've gotten like some of these T shirts and they like I just love the. I guess you know, in a world where it feels like everything is becoming so much faster moving and like, you know, even tech from five years ago is obsolete. Like I love a little bit of the throwback to like, you know, old things sometimes can be even more cherishable in this new era. Right. So like maybe that makes me a hipster but like I love the, you know, the, the vintage, the retro. Increasingly these days I feel like anything.
- [93:57]
B
That starts with artisanal, small batch Japanese is going to be really good stuff. Is there, is there a brand you want to share that is that. Or is this like you want to keep it?
- [94:06]
A
Yeah, actually. So Self Edge, which actually has a, a storefront, like the main storefront Valencia street in sf they carry a lot of these items. Like that's kind of their whole mo and they have like jeans and like T shirts. So I've gotten a lot of, I mean they, they basically curate a really good selection of different actual makers. Like one of them is called Studio Dartison. Another one's called actually it's cool. There's this company called, I think that the umbrella company is actually just Toyo T O Y O Manufacturing which sounds like it's a big like, you know, kind of like large scale conglomerate. But it's anything but it's like a really small scale Japanese, you know, kind of like vintage manufacturer of clothing and. But they have a few sub brands. They actually bought the rights to this like American post war brand that was kind of like Hanes, like one of the like big like four or five like you know, kind of menswear, like you know, kind of undershirts and athletic wear brands called Whitesville. I don't know where the name came from but you know it, it, it basically it's a bunch of like basic clothing like T shirts et cetera. And, and they, this Japanese indie company, Sleep, bought the like defunct, you know, basically name, you know and, and now like is reproducing clothes almost made to the exact shape and spec and even with like the exact recreation of like the graphic packaging on these TE's. But like you know, today. Right. So I just think there's something really funny and ironic about like you know, they've taken like an American post war aesthetic and literal brand, but like it's actually like a indie, small scale Japanese manufacturing approach to, to, to making those clothes.
- [95:51]
B
I feel like we just tapped into what could be a whole other podcast conversation about clothing and craftsmanship. But let's, I'm going to pull us out of that.
- [95:59]
A
The next franchise or just Howie and.
- [96:03]
B
Lenny talking about clothing. Okay, two more questions.
- [96:06]
A
Yeah.
- [96:07]
B
Do you have a life motto that you often find useful in work or life share with friends or family?
- [96:12]
A
I stumbled on this, this, this guy, Paul Conti, who I think he's an md but also like a psychologist and he has a book, you know, but also like he did this long form podcast with, with Andrew Huberman. And you know, he actually ends up talking a lot about like just how to think about like your life outlook and like kind of your framework for thinking about life, but grounded in a kind of like scientific and like, you know, kind of neurological and cognitive science basis. And you know, I found one particular point really, really powerful. It stuck with me, which is like, you know, if you live your life in a way that's, you know, foundationally built around humility and gratitude, right, and, and look like, you know, everybody has different circumstances. Like, you know, I think like I, I fully own that. Like, you know, even though, you know, I didn't come from money, like my family was, was very, very financially modest, like growing up, like I still had an incredible resources and opportunities that, you know, afforded to me even just by virtue of growing up in the U.S. right? Being born in and growing up in the U.S. like, you know, but also like having access to a computer and the Internet and like even all the free resources I could then access and learn about from there. But you know, like, I still feel like, you know, whatever you have or don't have to start with, like if you kind of approach the world and you know, kind of the future with a spirit of humility and gratitude rather than, I guess the opposite of that. You know, it just, I think I felt like it, it makes like it kind of like becomes a self fulfilling prophecy, right? Like, you know, you're, you're open minded, you're kind of grateful and then like more opportunities actually come your way, right? And maybe it's because of the energy you're putting out into the world and you know, and other people and like you're kind of attracting like, you know, good opportunities and good people and good things. But I, you know, I think like, you know, there's a lot of other parts of like his framework. But like, the one that, you know, is easiest to remember is just like, how do I approach each day? Even if, like, I'm going through a tough moment and you know, maybe we had to like, you know, I had to fire somebody today or maybe like, you know, I got disappointed because we lost a customer deal or something broke or whatever, you know, you know, but like, to still try to look at the entire situation from an overall, you know, feeling of humility and gratitude, I think just really does shift your, like, you know, it spills over into everything else for that day and maybe even for like, you know, the whole lifetime.
- [98:52]
B
That super resonates. That is really powerful advice that's hard to internalize, but important.
- [98:57]
A
Yeah, easily said. Hard to practice.
- [99:01]
B
Where can folks find you? What should they know about airtable and how can listeners be useful to you?
- [99:06]
A
Okay, so I am on Twitter howitl. I don't post that much, but I am a lurker so I listen and watch and you can always DM me there. You can also email me directly howieirtable.com anytime you can have ideas, feedback, etc. On airtable. Just go try it. The whole point is we want to make this an experiential product. That's why we're really leaning into the PLG roots we talked about. The homepage literally says like, just start building right now. What do you want to build? Go like it starts building and so use the product, give me feedback. And you know, if you have ideas of your own and you want to rip on them, like I love because my passion is thinking of a product and like product ux, especially in the AI era, if you're working on or you know, thinking about something interesting in that space. Like, and even if it's just purely to like riff on a concept, like that's, that's something I enjoy doing and maybe I get to learn and sharpen my own skill set from. So feel free to reach out and yeah, I mean, tell your friends and family to try airtable as well. That's the main thing.
- [100:08]
B
Sounds like you're looking for people to nerd snipe you. And yes, Howie, thank you so much for being here.
- [100:14]
A
Awesome. Thank you, Lenny.
- [100:15]
B
Hi everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show@lennyspodcast.com See you in the next episode.