What AI means for your product strategy | Paul Adams (CPO of Intercom)

[00:00] And this is a [00:01] like meteor coming towards you this is going to radically transform society [00:08] And I think if people don't explore AI properly, it will leave them behind. I'd start with the thing your product does. What's the core premise behind it? Why do people use it? You know, what problem is it solved for them? That kind of thing. So go back to basics and then ask, can AI do that? And for a lot, the ask is going to be, yes, it can. For some, it might be, it can partially do it. And then maybe for others, it can't do that, at least not yet. And then for some of it, it'll be like kind of replacement. AI will replace, it'll just do it. [00:38] augmentation it'll augment it'll help people but yeah i think that you got to map your product and what ai can do and what it will be able to do and then ask yourself okay what are we going to do [00:51] today my guest is paul adams paul is chief product officer at intercom a role that he's held for over 10 years [00:58] Prior to this role, he was global head of brand design at Facebook, a user researcher at Google, a product designer at Dyson, and his first job was an automotive interior designer. In our conversation, Paul shares some amazing stories of failure, including the story of him giving a huge presentation where he froze on stage and had to walk off, and what he learned from these experiences of failure.

[01:28] in on AI. Paul also shares some of his favorite frameworks and product lessons and so much more. This is the first recording I've ever done not from my home studio instead from a hotel room, so this is a fun experiment for us all. With that, I bring you Paul Adams after a short word from our sponsors. This episode is brought to you by EPPO. EPPO is a next-generation A-B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like [01:58] Miro, ClickUp, and DraftKings rely on Epo to power their experiments. Experimentation is increasingly essential for driving growth and for understanding the performance of new features. And Epo helps you increase experimentation velocity while unlocking rigorous, deep analysis in a way that no other commercial tool does. [02:28] statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out-of-the-box reporting that helps you avoid annoying, prolonged analytic cycles. EPPO also makes it easy for you to share experiment insights with your team, sparking new ideas for the A-B testing flywheel. EPPO powers experimentation across every use case, including product, growth, machine learning, monetization, and email marketing. Check out

[02:58] experiment velocity. That's geteppo.com slash Lenny. This episode is brought to you by Hex. If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of screenshots and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, [03:28] And now, Hex's AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you, all from natural language prompts. It's like having an analytics co-pilot built right into where you're already doing your work. Then, when you're ready to share, you can use Hex's drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. [03:58] .tech slash Lenny to get a 60-day free trial of the Hex Team plan. That's hex.tech slash Lenny. [04:06] Paul, thank you so much for being here and welcome to the podcast. [04:13] Thanks Annie, nice to be here [04:15] It's nice to have you here. I've heard so many good things about you from so many different people, so I'm really happy that we're finally doing this. [04:21] Also, you have an Irish accent, which is always a boost for ratings in my experience. So thank you for bringing that with me here. Yeah, that's nice to hear. [04:28] I wanted to start with a couple stories.

[04:31] So the first is your story of giving a keynote at Cannes [04:35] Can you share what happened there? [04:37] Some things happen in work every member of the time, and they don't really scar you. [04:42] This goes in the book that I've scarred for life. Uh... [04:45] Yeah, it's a good long story short. I was at Facebook just over a decade ago. [04:50] Loved it at the time. I think it was a great place to be at the time. [04:54] I did a lot of talks [04:56] for Facebook internally and externally, [04:58] Facebook had a keynote slot, always had a keynote slot at Cannes, the world's biggest advertising festival. [05:03] And, um, [05:05] The year prior, he was the speaker, he'd been interviewed, gotten a hard time on privacy, [05:11] It didn't go well as well as I'd hoped. So the next year they asked me to do it. Maybe it was the Irish accent, you know, that made the offer come my way. And... [05:22] Yeah, I got out in front of the stage, the world's biggest advertising stage, and [05:27] I'd say I was like three, four minutes into the talk. A talk I'd given, a very similar talk to one I'd given lots of times, [05:32] And I just froze. I couldn't remember what I was supposed to say. [05:37] It was the first ever time in my life I'd rehearsed to talk word for word. [05:41] Usually, I have talking points and I'm ad lib, and things get mixed around, and it's informal. [05:47] This was like media-trained, like, "Do not say the wrong thing," kind of talk. [05:52] Could not remember what to say. I had some version of a panic attack. I walked off stage. [05:58] I'm still mic'd up. [06:00] Cursed

[06:01] Everyone started laughing. I was like, "She's really laughing at me." You know, "Oh my God, this is..." But I managed to turn around. I walked back out. [06:09] I'd kind of been disarmed internally in my head, and the rest of it went well. And I was famous that night, you know, out in Cannes afterwards, like on the – [06:18] whatever, the seafront, it's just like rosé everywhere. And yeah, I was famous and infamous for [06:24] My performance. [06:26] I feel like you live the worst nightmare that everybody has when they're thinking about [06:30] giving a talk, and I think what's interesting is you survived. [06:34] And I think that's a really interesting lesson. You could freeze in front of thousands of people [06:39] Walk off stage. [06:41] And then [06:41] And it works out OK? [06:43] Yeah, and it all happened kind of organically, I guess, or very naturally, you know. But yeah, ever since then, every time I walk, go out onto a conference talk stage, still today, [06:53] I asked myself, I have this tiny doubt in the back of my head. It's never happened since, but [06:57] Yeah, I think you have to go with it, with these things. When life throws you these, whatever, curveballs, [07:03] you have got to kind of adapt. [07:05] and [07:06] It's not that big a deal. None of these things are that big a deal at the end of the day. You know, you kind of move on, live and learn. So, yeah, but I still hope it doesn't happen again. I also hate public speaking, and I always fear this is exactly what's going to happen to me. [07:20] And so I think this is a-- [07:22] Nice to hear that even when the worst possible thing basically happens, things can survive. You can turn it around, yeah. [07:29] A second area I wanted to hear from is your time at Google.

[07:33] And there's a couple of products you worked on at Google. Both of them were not what you'd call big successes. [07:39] And then there's a kind of a transition to Facebook, which was also kind of messy. Can you just share a couple of stories from that time? [07:45] Yeah, similar to the kind of like, you know, walking on stage thing, um... [07:50] You live and learn and [07:51] I was at Google for four years and then I was on Facebook for kind of two and a half years or so. [07:56] And in both of those companies, [07:58] This is at the height of the social tech wave was at its peak. [08:03] Google were... [08:04] Very afraid of the existential threat posed by Facebook. [08:08] Facebook were very [08:10] confident they could pull off some kind of like new social advertising unit that would be like an AdWords or something like that, that would like, you know, destroy Google's revenue, eat them from the inside out. And so being there at the time was fascinating and moving to new companies. At Google, I worked on a lot of failed social projects, like you mentioned, Google Buzz, Google, then later Google Plus, [08:29] I think [08:30] a lot of the motivation for those projects came from A Place of Fear. [08:35] You know, it didn't come from a place of let's make a great product for people. Let's like really understand the things people struggle with when communicating with family and friends. Like that's really, really try and create something wonderful. It came from a place of fear. And. [08:48] And so during those times I kind of learned, I think, how [08:52] how not to lead in places. And by that, I should say, you know, at the time in Google, [08:56] There was other things happening that were amazing, like Google were building Google Maps. [08:59] An incredible product, one of my favorite products, I think one of the best products ever made. [09:03] They were building Android. I was in the mobile team, in the mobile apps team at the time that Android came out. So I can make an incredibly good product. So I just happened to be in the social side, which wasn't as good. And yeah, we...

[09:18] Google blows this kind of a privacy disaster. [09:21] and [09:22] Google+ is similar, and so kind of halfway through, [09:26] I kind of published research about groups and I've done a ton of research. An interesting kind of side note there is, [09:32] At the time, I was working in the UX team as a researcher. I was being asked to do a lot of tactical research, like usability study type stuff. [09:40] Can people use these products? [09:42] I ended up doing a lot of formative research as well in the same session. So I'd kind of say to the team, like, hey, I'll do the research. I'll answer your questions. But also, I'm going to do this other thing. I'm going to take 20 minutes doing that. And so what I used to do with people was map out their social network, all the people in it, their family, their friends, how they communicate. We'd map on all the channels. [10:03] We talk about what worked well, what didn't. And we did this with dozens and dozens of people over the course of maybe 18 months. [10:10] And the same pattern emerged every single time, which was, [10:13] People need way better ways to communicate with small groups of family and friends. [10:17] I kind of look back now and go like, [10:19] WhatsApp, you know, or like [10:21] It may be like iMessage if everyone's on Apple, but like really obvious in hindsight, but at the time not obvious and [10:28] And so we kind of tried to build a product around that called Google Plus. [10:32] Again, it was kind of motivated from the wrong, came from the wrong place and so [10:35] halfway through the research that I've kind of done, all this research, [10:39] have been made public through a conference talk, and [10:42] So, and Facebook, notice, got in touch. [10:45] One thing led to another, and I left and joined Facebook.

[10:48] which was an amazing thing for me personally. Facebook was amazing, an amazing place at the time and exciting. And they were trying to do things for... [10:57] The other reasons, the kind of good reasons, like, hey, let's build an amazing product for people. And this was during Google Plus being built. You basically shifted. Yeah, midway. I'm stressed to even tell you that. The project hadn't been launched. It was still under wraps. It was highly confidential. Google had done a lot of things at the time that were the first for them. I don't know if they've done them since, but things like, [11:18] Everyone worked on Google+ to send to a different building. [11:21] that building had a different key card, [11:23] If you didn't work on Google+, you could not get in. All sorts of counter-cultural things, [11:29] at the time, and as a result, there was a lot of, you know, [11:33] antagonism internally for Google Plus, [11:35] So when I left in the middle of the project, kind of leaving with all of the plans in my head, [11:39] to the enemy. [11:41] Some people saw me as a trader. [11:43] understandably, [11:45] Other people thought I was enlightened, you know, how to fancy you talk to. But it was the right thing for me to do, but at the time, [11:53] It was a hard thing to do. [11:56] I know there's also a lot of scrutiny in what you [11:59] Took with you. [12:00] and the process? Yeah, when I left, Google kind of assumed that I was one of the spies [12:06] you know, I was quarantined, I told them I was leaving, [12:10] They, you know, forensically analyze my laptop, like all sorts of stuff like that. [12:15] So it was pretty intense. [12:18] Looking back, I

[12:20] can understand why that happened. [12:22] But the root cause for me is that the project has been run from a place of fear, competitive fear, which I don't think leads to good things. [12:32] So one of the themes through the stories you just shared is [12:35] Let's say failure is, I don't want to make it that harsh, but just things not working out. [12:40] And I'm curious as a product leader how important [12:44] You think that is for people to go through? [12:46] if you think that's something that is almost a good thing. And I guess just is there anything there that [12:50] you find helpful as a coach, as a mentor. [12:53] That's the one [12:54] two people that are trying to [12:55] become [12:56] Basically you. [12:57] Very, very. It still is, you know, like... [13:02] I've personally failed so many times. There are two stories, and the Google one is long, deep tentacles. There are two stories. I've failed a ton of times. At Intercom, I remember when I was at Facebook, I was very happy. [13:16] Owen and I knew Owen and I was the co-founders of Intercom and [13:20] They're trying to persuade me to join Intercom. It was a 10-person company at the time. [13:24] But Owen said something to me at that time, which has stuck with me ever since. He said, [13:29] You know, at Facebook, you can design the product, but at Intercom, you can design the company. [13:34] And that was extremely appealing to me, a great pitch. He's like, just design the company with us that you want to work in. [13:41] And so part of that was a company that embraces failure. [13:45] that says it's okay to try things [13:48] I'm a big believer in big bets, high risk, high reward.

[13:51] I don't get as excited about incremental things. Now, having said that, there's of course a place for that too. [13:58] especially as companies get bigger but i i i get excited about like big big bets and if you make big bets you're going to get a lot of it wrong [14:05] So a lot of the principles that we built here at Intercom are in building software [14:09] Like we have a principle called Ship to Learn. [14:11] and [14:12] We've actually changed it since. It's over on the wall here. [14:15] "Ship fast, ship early, ship often," is what it says now. You say ship to learn. "Ship fast, ship early, ship often." [14:21] It's like in that idea is the idea of failure. It's not gonna go right. [14:26] It's going to go wrong more often than not. But if you ship early and fast, [14:29] and learn fast [14:31] You can change fast and you can improve fast. [14:34] And that's kind of how we that's the kind of culture that that we [14:37] as much as possible, try to embrace [14:39] and teach people. [14:41] But it's much easier said than done. [14:43] Especially when you're in the moment, like, goddammit, everything's going to fall apart. I really messed this one up. [14:48] And there's a trade-off with quality that people really struggle with. Like, you know, we have high standards of ourselves. [14:55] A lot of intercom comes from a kind of design founder background. We value the craft a lot. [15:00] We never want to be embarrassed by what we ship. [15:02] So there's a real tension there, a real trade-off where people [15:06] have these high standards, [15:08] which we encourage. Are we encouraged to ship fast and learn and make mistakes [15:13] It's a constant kind of [15:14] tension that we're navigating. [15:17] Speaking of taking big bets and going all in, [15:19] I know there's been a huge shift at Intercom to move towards AI and embrace AI. And so maybe just to start broadly, I'm curious just what are some of your broader insights

[15:30] or surprises... [15:32] so far in how you've thought about AI and how you think AI will integrate into product and product strategy. [15:38] What day did ChatGPT launch? November 29th, I think last year. Ever since that day, I literally wake up every day thinking about AI pretty much. [15:46] And [15:47] I read as much as possible and still feel like I'm way behind in it. [15:50] I think for me, like when I talk to people about AI, people typically fall into one or two camps. [15:55] you're either all in, really truly all in. [16:00] like meteor coming towards you. Like this is... [16:05] you know, bigger than mobile as a kind of technology shift. [16:08] As big as the internet, maybe it's bigger than the internet itself as a kind of social, you know, technology shifts, the way it will shape society. [16:15] So I'm all in. I've gone over the hill or whatever. I'm over the other side. And so there's people in that camp. And I think there's people in another camp, which is [16:25] I've heard this before, it's hype. [16:27] Like, [16:28] You know, last year was crypto, you know, Web3, like none of those things worked out. There was the metaverse, you know, so there's. [16:35] Definitely, I think a lot of skepticism or maybe cynicism around it. [16:39] And I can understand why. The other things didn't really-- [16:42] pan out, oh, the Beniverse is kind of an investment that might be coming back, but [16:45] And I kind of think about... [16:48] I'm trying to remember, there's a law where you have the hype and then the trough of disillusionment, and then you kind of come out the other side. Yeah, a little curve chart. Yeah, and I think that's where a lot of people might be, where there was so much hype. [17:00] It was so noisy and still is a little bit so noisy that you can't tune it out a little bit.

[17:05] And some people have kind of fallen into that camp. [17:08] I'm all in on the other camp. [17:11] This is going to radically change [17:14] transform society [17:15] And it kind of like blows my mind. [17:18] Even seeing new types of things that come out, like ChatGPT Vision just came out recently, and just seeing the things that people can do with it. [17:26] And we're just scratching the surface still. So we're all in, for sure. [17:31] Awesome. I want to unpack that. [17:33] But I think there's also this camp of people that like, yes, something big is happening. I just don't have the time to... [17:39] understand, to build, to play around. [17:42] What have you found and or what advice would you share to people that are just like, I want to [17:46] go deeper down this rabbit hole. [17:48] I just don't know where to start because I have so much work to do already and this isn't a side thing. The advice I have for people and the advice I have for myself, I'm in that too. I wake up every day to... [17:58] too many emails and Slack chats and people knocking on my door and [18:03] my desk and all kinds of things. So like, [18:05] This is a challenge for me too. [18:06] You just have to take the time. [18:08] Like there's just no other way for me. And that to me doesn't mean, you know, [18:12] It's about priorities. You know, it doesn't mean that you like need to work, you know, [18:15] Crazy hours. I don't believe in working crazy hours. I don't know what hours I work. I don't know, 50 hours a week maybe. I think beyond that you start to make bad decisions and things like that. You get tired. I need to live the rest of your life. [18:27] Like you got to put it into your day, you know, whether that's like setting aside dedicated time to read. Reading is the thing. You got to read. You got to stay up to date and you got to play with things and try things.

[18:38] if you don't have ChatGPT, if you don't have a Pro license or whatever, but if you haven't upgraded to get access to things like GPT for Vision, where you can take photos and you have the mobile app. And it goes out for dinner last Friday, [18:53] I try not to take work to dinner with my wife, but I wanted to try it, and I took some photos of her food. [19:01] and can do all sorts of crazy stuff, like tell you how healthy the meal is or whatever. Anyway, you gotta try it. You just gotta try it. So my advice to people is you've gotta try it, you've gotta set aside the time, [19:13] or it'll pass you by... [19:15] It does remind me of the mobile, the kind of mobile wave about a decade ago. [19:18] Again, I was at Google at the time. I was working on the mobile team, so I guess it was my job to stay on top of things. [19:22] But at that time, some companies [19:25] Like Facebook went all in on it. [19:27] maybe a bit late, but they eventually made the brave decision [19:31] And I think if people don't [19:33] explore AI properly, [19:35] it will leave them behind. [19:37] It reminds me, I think, at Facebook, Zuck, and also at Airbnb, Brian did this, is [19:41] He said, any mocks you show me, [19:44] for new product designs have to be in a mobile app. [19:47] or on a mobile web. They can no longer be desktop for now. [19:50] Right. Yeah, I read that same with Facebook. Yeah, I think that's right. I guess, do you think that that's a way to approach this as a leader? Just everything you bring me needs to have some AI component? That sounds probably not like a good idea, but... [20:00] Is there something there you're thinking about or have done of just convincing people this is where you want to spend your time? [20:05] Yeah, it's harder, for sure. It's harder because...

[20:09] Yeah, a lot of the tech is invisible. A lot of the things... We have a machine learning team. We've had it here for a long time. So we've been working in this space for quite some time. But it's funny, even if you go back like 18 months, [20:20] I think if I was on your podcast 18 months ago and you said to me, like, hey, what do you think about AI? [20:25] I would say something like, [20:26] It's not real. Machine learning is real. Let's talk about that. You know, so so things change and my perception of it's changed. But a lot of the a lot of the [20:34] Improvements are kind of like behind the scenes. [20:37] You know, there were like large language models or like different types of things people are building in the background of infrastructure. [20:43] So I don't know what it looks like to design mobile mock-ups that are like AI mock-ups, but I do think that people need to start really thinking strategically [20:53] Like, I don't know, maybe it's just not a mock-up stage. [20:56] but start to think really strategically about... [20:59] their product and [21:02] whether it's in the line of the meteor that's coming or not, you know, it's not everything is, [21:07] And if so, for some, I think they require a kind of a foundational strategic change. [21:12] Others, it might be less so, but I think that's actually the headspace that I think people need to be in. [21:17] Can you unpack that further? What does that look like to... [21:21] really think deeply about whether your product is in the way of the meteor. [21:25] You can get sidetracked by the technology for sure. And I do. I just imagine, like, hey, going out for dinner and taking a photo of my food, you know. You can get sidetracked by the tech. And some of it's really cool. I wouldn't start there. I'd start with... [21:38] the thing your product does.

[21:40] What's the core premise behind it? [21:42] Why do people use it? [21:44] "What problem is it solved for them?" That kind of thing. [21:47] and then ask the question, so go back to basics, okay, what is my product for? [21:52] Why do people love it? [21:53] And then ask, can AI do that? [21:56] And for a lot, the answer is going to be yes, it can. [21:59] For some, it might be it can partially do it, and then maybe for others, it can't do that, at least not yet. And the types of things, so you're gonna need to map what your product does against what AI can do. [22:10] And like AI can do a lot. [22:12] Like, [22:14] It can write. [22:16] I'll give you a list. It can write, it can summarise. [22:20] It can summarize text, it can write text, it can answer queries, it can find facts. [22:25] They can scan text, they can scan images. [22:28] I can [22:29] listen to your voice and repeat it. It can take actions. That's the next big thing coming. It can take actions, actually do things. It could like, hey, I mean, hey, AI, whatever the AI is called. Yeah, change my flight. [22:42] Change my flight to Tuesday. It can do things like that. [22:45] And so it can do a lot of things. It can build rules. So I think any product that has any kind of workflow in it [22:54] which is almost all B2B SaaS products. [22:57] Any product that has multimedia in it, [23:00] They're in the fire, they're in the meteor line or whatever. I don't know if this metaphor is working, but like, you know, the meteor is coming and they're like in its path. [23:07] And so for a lot of these products, you just need to look at what AI can do.

[23:11] And then for some of it, it'll be like kind of replacement. AI will replace. It'll just do it. [23:16] And then [23:17] But in other places, it'll be augmentation. It'll augment. It'll help people as a co-pilot ideas that are going around. [23:23] But yeah, I think that you got to match your product [23:26] and what AI can do and what it will be able to do. [23:29] And then ask yourself, okay, what are we going to do? [23:33] Is there an example of that at Intercom or a different company of [23:36] Here's a problem we're trying to solve. Oh, AI can actually do this fully for us. [23:40] Oh, yeah. I like... I'll give you Intercom first. [23:43] Again, this date is kind of-- I think it was never over 28. [23:48] etched in our head. We have like Fergal, who is our head of machine learning, and [23:53] Fergal just turns around that day and he's like, okay, I think he tweeted something actually. He had a tweet that day that was like, this is it. This is the time. This is the moment. This is the before after. [24:02] You know, I actually often talk about people. This is a framework I have, like before-after moments. [24:06] This is a before-after moment. [24:08] There was before and that is after and like [24:10] everything has changed. So we literally ripped up our strategy almost entirely [24:16] and start it again, like from first principles, and say, OK, [24:20] Why do people use Intercom? You know, Intercom is a customer support product. And then, [24:26] Very soon after that, Sam Altman, who's the founder and head of OpenAI, said, [24:31] "Hey, one of the first industries that's going to be disrupted is customer service." [24:34] We're like, "Yep." So we did. We totally changed how we think, how we work. [24:39] and [24:41] We just went kind of heads down and built a product called Fitton.

[24:45] We built other things first, actually Finn came later, now that I think about it. But we just went, we kind of went all in on it. It was a little bit of a bet the farm kind of mindset. [24:53] So we've done it. [24:54] I think other companies [24:57] like Google with Bard, [24:59] have to do it. [25:00] And maybe they were a little bit slow, but it's so early in this tech cycle that [25:05] I think they're fine. [25:07] So, you know, yeah, you just have to, we did. [25:11] It was hard, but we had to do it. [25:13] Can you share briefly what Fin is, just for folks that aren't familiar? Fin is, first and foremost, is an AI chatbot. So if you think about customer service, [25:22] You know, people have questions. [25:24] for a business and historically that was mostly email and phone [25:28] mostly ticketing based, so you file a ticket, a lot of do not reply email and kind of [25:33] so on, and then came along conversational, [25:37] customer support, which is just basic messaging, like WhatsApp or iMessage, like I mentioned earlier. [25:41] Now there's like bot first experiences, and Fin is an AI chat bot. AI first, chat bot first. [25:49] So the first line of defense for a customer support team is Finn. [25:52] not a person. [25:53] And so it fundamentally changes. And Fin can do work. The results we've seen in Fin are mind-blowing. [25:58] Our biggest challenge is actually [26:00] trying to help customer support teams think about organizational change. You know, it's not like the tech is like way ahead. [26:07] It's actually like people wrapping their heads around what this means for [26:11] the role of the teams, [26:13] Loads of cool stuff, you know, like new types of jobs for people, like conversation designers, a job we have where you design the conversations that Finn does, our managers. So anyway...

[26:22] That's what Fin is. Fin has expanded. So Fin is now also in our Intercom inbox, the place that people answer queries, customer support queries. [26:32] And now Finn's in there too, helping the support reps. [26:35] like suggesting answers for them to use, or [26:38] helping them like rephrase things or so it's it's now augmenting people as well as answering questions by itself. [26:45] I think you're one of the few companies that has... [26:48] pivoted fully into AI. [26:50] And [26:50] I think there's a lot of lessons here about how team structures might change, [26:55] product strategy, [26:57] priorities, things like that. So I'm curious just to unpack a couple more things here. [27:01] First of all, what kind of impact have you seen after going all in and going in this direction? [27:05] It's very early, honestly, to be able to answer that properly. And it depends what you measure as success. [27:11] So, [27:11] Again, there's a lot of hype and buzz with AI. [27:15] If you're measuring it by like... [27:17] interest. [27:18] It's a huge success. A lot of people, our target customers, customer support, our customer support manager leader, [27:25] And so they're very curious. They're like, "Does it actually work?" There's a little bit, again, back to the earlier thing of there's so much hype, there's a bit of skepticism around it. [27:34] Does it actually work? Is it as good as a person? [27:36] Hey, and customer support, [27:39] People who tend to work in that role are typically very high empathy [27:42] care a lot about people, [27:44] And so they're like, [27:45] But is it as good as a person? [27:47] Like, is it nice, friendly? Does it understand humanity? So there's a lot of curiosity.

[27:55] and a lot of interest and a lot of people trying it. [27:57] We have some customers who are hugely successful with it. They can answer up to [28:02] 50, 60, 70% of their inbound questions with Fin. So like with some customers who see huge success, [28:10] But it's early, you know, and so like, has it transformed our business? [28:13] Like financially, not yet. You know, it's not like this kind of, you know, oh, I think I'll... [28:19] fast-growing startups, [28:20] If you think of Intercom as [28:22] or like AI Intercom is I guess a new startup, even though we're 900 people. You know, the kind of growth curve, you're looking for this kind of exponential curve. [28:30] I suppose like big public company kind of linear growth curve. [28:33] With the exponential one, it takes a while. You know, the first kind of year, two years, [28:37] It's like bottom of that. [28:39] And so I think we're still in the like, [28:41] trying to figure out exactly what's going on, trying to talk to, educate people, [28:46] but [28:47] We have enough evidence. [28:50] to believe it's the future for sure. [28:53] Are there any examples of either this product [28:55] or other instances of AI just kind of blowing your mind. [28:59] We're just like, wow, I never imagined it would be this good. [29:02] I kind of go back to that before/after thing. So ChatGPT, the first version of ChatGPT was a before/after, [29:08] We had built-- We've been working, like I said, in this space. We've had a machine learning team for a long time. [29:12] The way our machine learning thing worked before ChatGPT was, you have a, there's a lot of manual setup, like a customer support manager, [29:20] we'd have to like orchestrate the boss and like teach it what to say. And like, you know, um,

[29:25] Just a lot of orchestration, a lot of teaching it. [29:28] And then ChatGPT showed up and it's like, oh, it can do it by itself. Like, it gets it wrong sometimes, but so do people. People get the question wrong too. You know, it's kind of as good as a person, really, for a lot of these basic things. So that blew my mind. [29:40] And then those are just, oh, I can answer questions. [29:42] But then you're like, it can reason. [29:44] There's actually a debate about whether, is this reasoning or deduction or, you know, but it can like... [29:51] Work things out. [29:52] And I'm not one for going down into these really philosophical things. [29:57] We just need to go back and build the product or whatever. But it can work things out. And that blew my mind. And we fed a whole bunch of... we fed Chachipity [30:05] And other companies too, like we played with other LLMs, like Entropic and so on. You can work things out, and that was kind of mind-blowing. [30:12] Then you can see it doing things like writing code. [30:16] And I was like, wow, it's really good at writing code. What does that mean? [30:19] And then you start thinking, [30:21] Here at Intercom, we have a one to five ratio. So a PM has about five engineers on a team. [30:27] You're looking at this thing right in code and you're like, [30:30] What happens next? You know, like, do we... [30:33] need as many engineers or will their role change? And they'll start doing different types of things like [30:39] reviewing code instead of writing code. So that kind of blew my mind. [30:42] And then the visual stuff, like I mentioned earlier, [30:45] I think the visual thing was bigger than the original one. [30:48] It can parse imagery and like, [30:50] It can help you see the world. You take a photo of your bike and say, "Hey, what's wrong?" And I'll tell you what's wrong, how to fix it.

[30:57] You can be traveling, take photos of stuff. It's in a different language. It's like etched in stone on a 12th century cathedral. You're like, what does that say? [31:04] And it'll tell you what it says. It's just like... [31:08] How to do that? This is one I'm actually repeating most to people these days. [31:14] Here in Ireland, if you want to be a radiologist, study x-rays and [31:20] and tell people what's wrong and so on and so forth. [31:22] It's seven years training to learn that skill. [31:26] So seven years to be a radiologist. And then you're just kind of just into the job. [31:30] AI, it seems, is already better at it. [31:33] So it's already bad about it. [31:36] and it can ingest every x-ray ever made. [31:39] like no human can ever [31:41] read and think about and synthesize every extra ever made. So of course it's better. And then you're like, [31:47] "Okay, what happens now?" I guess the whole job changes. You know, radiologists will not [31:51] Take X-ray, well, I guess I might take them, but they won't analyze them for sure. They'll look at what AI says. [31:57] check that it's right, and then it's like kind of [31:59] Bedside manner time like you know tell the patient maybe tell them what kind of course so like the job just fundamentally changes And by the way, that could be amazing we have here in Ireland that [32:08] We have long queues for hospitals, epic waiting lists for people getting x-rays. [32:13] This is a really good thing possibly for people. Here's the craziest one I have. [32:19] AI can... [32:20] listen to your voice, and copy it. So you can say things, and it sounds exactly like you, and it's really, really good. Like almost indistinguishable, but you're like, that sounds like Paul. And so I mentioned the metaverse earlier. I don't know if you saw

[32:32] Zuck talked to Lex Friedman, see that? So that was my first like, oh, so it's the Meta, if people haven't seen it, they met in the Metaverse, I think. [32:40] or some virtual world. Yeah, it was like a black room. [32:44] In a black room, yeah. And the tech has come on so they can analyze your face and build a 3D model. It's really good, like really, really close. So you can imagine that's going to get better. [32:54] Based on the trajectory of that technology, it's going to get better. And so the voice thing and the face thing means [32:58] Both of those things are [33:00] almost indistinguishable from a real person. [33:03] and [33:04] AI will be able to ingest [33:07] All the things people say and do. [33:09] And when people die, [33:11] it'll be able to replicate that person. [33:15] There's an afterlife. [33:17] Hey, you know like you're [33:18] Your parent dies and you can still talk to them. [33:22] And that could be the weirdest thing. Maybe it's not good for people. I don't know. [33:26] .tech is like [33:27] just around the corner, you know, and the AI can like [33:30] That's kind of like... [33:32] Any questions? Mind-blowing. [33:35] There's actually a Black Mirror episode with that same premise. That's right. [33:39] Yeah, and I don't think it ended well. No, I like it. Be careful. [33:44] For sure, for sure. Yeah, it is. I think the minority report and the voice translation thing is another one. I can't remember. Maybe it's in Mission Impossible. [33:54] where it can take a voice, translate it, and translate it in real time. [33:58] And this stack is, again, just here. [34:00] where if I was a native Spanish speaker and couldn't speak English, you and I could still have this podcast.

[34:05] You know, it's been your voice to be translated in Spanish in real time for me. It's like, again, mind blowing. [34:10] We're actually working on dubbing [34:12] slash translating. [34:14] podcast episodes, which is all done through AI, where it figures out what you're saying. It makes it Spanish. [34:19] And then also changes your lips to match. And we're trying to launch a couple of those. And that's actually very AI based. Yeah. [34:25] That's cool. You mentioned that [34:28] your eng team might change. You're thinking like, because AI can make them much more efficient and work differently. I'm curious what you've seen actually change on your team [34:36] either [34:37] using AI-ish tools or just building AI products. [34:41] What do you think is most different? [34:44] From the perspective of a team that's trying to think about integrating AI and starting to lean into AI, [34:48] What have you seen most change and should change? [34:51] Ultimately, you need really great machine learning engineers. [34:54] Like, that's where it starts. And if you don't have that, [34:57] then, um, [34:59] You've got to find it hard to build truly, really, truly great things. [35:03] So what OpenAI provide, what Antropic provide, and Claude, they provide an amazing technology. [35:12] But you've got to build on top of it. If you really want something brilliant, you've got to build on top of it. So we adapted what they build for customer support. Maybe someday we need to go build our own LLM that's just for customer support. Maybe. I don't know where that will all go. [35:27] and maybe everyone will have their own LLM for every single business. I don't really know, to be honest. [35:32] Maybe these companies will provide specialized LMs. [35:35] That's like kind of the first thing.

[35:38] And of course, these people are in high demand. So you need to invest in building out that function, I think. Really invest in building out the function. So that's what we've been doing. Our ML team is way bigger than it. [35:51] It was, and way bigger than it ever has been at Intercom. And then it forks. So some projects are very heavy on that ML team, and it needs them. But other projects are more front end. Like the inbox stuff I mentioned earlier, [36:06] We have Finn, and Finn is kind of working. We've built the underlying technology. [36:10] Now it's a question of like, [36:12] If you have a human support person answering questions in the inbox, [36:16] That's like a natural chat. [36:17] kind of conversational interface, pretty straightforward. [36:20] What happens when there's now like an AI... [36:22] assistant in there. [36:24] How do they talk and what do they do and when do they interject? [36:27] How do you represent that in the user experience that feels natural? So that's a really hard design problem. [36:32] So then you're kind of back into like, [36:34] "Okay, we have a product team that's like a product manager, a product designer." [36:37] maybe three, four, maybe five engineers, and they're getting help from the machine learning team. So we now have both setups. [36:47] And increasingly, we can do more with the latter, you know, more teams who can build on the foundational technology that, [36:53] we've been building over the last 12 months or so. [36:56] So that's kind of one thing. [36:57] I think a second thing that comes to mind is [37:01] Not to think about it as bolted on. [37:03] I think some people are still in that camp. [37:06] Um... [37:07] Again, I'll go back to the mobile thing. There's just so many direct parallels with it.

[37:13] Like I said earlier, at Google I worked in the mobile apps team. [37:16] I worked on mobile Gmail. [37:18] mobile docs. And it was like the mobile team. And we were in London. We're like, "Hey, we're the mobile team in London." And meanwhile, over in Mountain View in California, no one cared. You know, it was like, you're 20 people, we're 200. [37:33] "No one uses this stuff on a phone." And again, a lot of skepticism. No one's gonna write docs on a phone. Seriously, they're gonna write a document? They're gonna write a full document on a phone? Are you crazy? [37:42] You know, so... [37:44] So don't do that. We're trying not to do that. Don't bolt it on. Don't be like, "Oh, we'll have a bunch of AI people." And we do have some specialists, [37:52] But generally speaking, we're trying to have everyone learn about it. [37:56] Thank you. [37:57] Interesting. So I'm curious just specifically what that looks like, don't bolt it on. [38:01] The idea there is don't just have like a side team. That's like they're the AI team. They're going to add AI to all this stuff. [38:06] your finding and lesson is integrated into every product team. [38:11] Thank you. [38:11] Yeah, and we're still early there. You know, we're still early. So, like, what we're trying not to do is have, like, the kind of, like, AI inbox team. And they're the only people who work on AI features in the inbox. [38:22] I think it's much better to have everyone learn about it [38:25] By the way, I'm a big believer in generalists. [38:28] like a big, big believer in like, I mean, I guess my background is like, you know, [38:33] Jack of all trades, master of none. That's probably how I describe myself. I've worked as a researcher, designer, PM. [38:40] I believe in generalists, and so I believe in setting teams up that way.

[38:44] And yes, specialism matters at times. Like machine learning for sure is a deep specialism. [38:49] And at INSCOM, we generally, much in engineering too, [38:52] I much prefer people who learn new things. [38:56] whether it's like a new coding language or framework, [39:00] how to design AI interfaces or whatever, get more people being able to do it. [39:05] I feel like, again, your company is a little bit of living in the future where a lot of companies are going to get to once they realize, oh shit, we really need to [39:12] get big hair or they're already working on it. [39:14] I'm curious if there's other [39:16] maybe pitfalls you ran into that [39:19] you think people should try to avoid and something you could share there or just like any other lessons about [39:24] making this transition that you think might be useful to other people? [39:27] Yeah, what I've mentioned so far, don't bolt it on. Stay up to date. I mentioned it, like, read. I feel like I'm behind all the time. It's moving so fast. [39:36] What are you reading? What do you find is most interesting and informative for [39:40] reading about what's happening in AI? I'd love to tell you that it's incredibly structured. I have a great reading list that I get done every Sunday morning. It's [39:49] It's pretty random. I'm on Twitter, which is now called Xcourse, a lot. I follow some people on Twitter. [39:56] Um... [39:57] I actually use the recommended feed in Twitter a lot. I think because I interact and look at a lot of AI, I get to see a lot more. [40:03] So I do that and I kind of do it deliberately to try and generate more stuff. [40:06] I'll search. [40:07] Twitter as well, I guess those are cool stuff there. [40:09] There's some newsletters as well and some people I follow. Any newsletters you could call out that you think are most interesting? Yeah, Matt Rickard is one guy who talks a lot about AI.

[40:20] The blogs of companies too, like OpenAI have a pretty good blog. [40:24] And they write papers and summarize them. Cool. If there's any other ones you think of, either people on Twitter, [40:29] to follow or newsletters, [40:31] Email me after and then we'll add them to the show notes. - Yeah, perfect. Yeah, yeah, there definitely is. I'll dig them out. Your question earlier, how do you do it? Just try, book out half an hour and just go deep for half an hour and then bookmark a few things, come back to the minute. [40:44] Like everyone, you could be so busy. [40:47] So many distractions, you're just going to have to set aside time. [40:50] Are there any other tools or apps that you find really helpful? [40:53] Sounds like Chachapiti is kind of at the center of... [40:55] how you play around with it. Is there anything else that you find really interesting? [40:59] I'll try other things like BARD, you know, for example, at Google. BARD is Google's kind of AI search engine. [41:05] Rewind is another fascinating company. I think it's rewind.ai. [41:09] Rewind is basically augmented AI for your memory. So, install it on your local machine, [41:16] and it captures everything and remembers everything. It's all local, so there's no privacy issues. [41:22] and, uh, [41:24] You got to try these things to understand whether it's any good or useful or [41:27] Where's the boundaries and how does it work and so on. So I'm a believer in that type of thing.

[41:57] generated answers, and lists of the most relevant documentation from all of your help sources, including your knowledge base, docs, blog, and video libraries. You can also use Help Bar to navigate your app and launch actions, such as scheduling a meeting or viewing an interactive demo. The best products today use Command K for in-app search and navigation. Help Bar makes that readily available within your app without engineering or new code. Give users a faster and more delightful self-serve experience that reduces friction and increases in-app engagement. [42:27] Upgrade your user experience with this modern component and supercharge your product-led motion. Sign up for Help Bar today. It's free and easy to set up in minutes. Check it out at helpbar.ai slash Lenny. That's helpbar.ai slash Lenny. [42:45] When you started rolling out AI and kind of leaning into this direction, [42:49] Did you run into any big challenges or hurdles organizationally or personal? [42:54] interests or opinions? I don't know. Was there anything you ran into that was a big stumbling block and something you had to get over? [43:00] Like any company, Intercom is full of [43:03] diverse opinions about things, you know, and, [43:06] I think with AI... [43:09] I'm all in, I'm leaning forward, the media is coming, I'm sold. I'm way past that point. [43:17] Also, [43:19] No one knows. [43:20] Like, no one knows. And so... [43:22] A lot of the time when we talk internally, like the strong buy-in, [43:25] from Owen, co-founder and CEO, Des, co-founder,

[43:29] like me, a lot of the senior leadership team, we're in the all-in camp. And so that helps a lot. Of course, if you're a senior leadership team in the company, you're all-in, of course, then it kind of trickles down. [43:38] But equally, people sometimes ask, [43:41] Some of the kind of hurdles of being like, [43:43] Why are you all in? And I'm like... [43:47] An educated guess? A hunch? You know, a lot of it's like... [43:53] the kind of the [43:54] The part of like business strategy and product strategy that it's just hard. It's just kind of. [43:59] It's like taste, you know, people talk about taste, product taste, who has product taste? [44:02] And a lot of it is like, [44:04] It's judgment based on experience. That's all I can say. Like, [44:08] I don't know. For me personally, I don't know. I lived through the mobile thing pretty closely. [44:13] having worked at Google on mobile. I lived through that phase. [44:17] So I can see the same type of thing happening now, but bigger. So I'm kind of like using that experience to like go all in. [44:23] But it's a challenge for some people because they don't have that context or they disagree with it. We have a lot of debate here about the future. [44:31] Ferg, what I mentioned earlier, [44:33] gave myself and a few other people, a few of the product leaders, [44:37] And he gave us like a... [44:39] I don't know, is it a pitch or what? A play? I don't know. [44:43] about how maybe all of our roadmap with AI [44:47] is wrong, maybe we're like kind of, [44:50] I don't know if you think, are familiar with the Horizons framework, like Horizon 1, 2, and 3. Yeah, Amazon. Yeah. [44:57] So like horizon one is kind of the medium, short to medium term, like next 12 months, 12 to 18 months. Horizon two being like, "Hey, what's happening?

[45:04] whatever, 18 to 36 months out. I think people use different time frames, different horizons. [45:08] We're like in Horizon One land. We're like, yeah, and then next year we're going to do this. [45:11] And he's like, "Yeah, but two years from now, [45:14] If this path, [45:15] you know, plays out. [45:17] Everything we're doing now is going to be irrelevant. [45:21] and like useless. [45:23] And you're like, okay. [45:25] And so those discussions happen. [45:30] and the level of ambiguity is off the charts. [45:35] So-- [45:36] A lot of the challenges have been navigating that. [45:39] ambiguity and [45:41] helping people [45:43] get the conviction I have. [45:45] without kind of drying out voices of like, [45:48] alternative voices and opinions, which are often valid too. [45:53] What does help people get that conviction? Is it just showing them examples of here? Something, wow, look at this thing. This is unreal. [45:59] And [45:59] I think partly what helps, I imagine, is the market you're in seems like such a clear opportunity for AI. It feels like an easier pitch than maybe a lot of other... [46:08] markets. [46:09] Yeah, that's true, for sure. That's true. Yeah, showing people is definitely like the easiest way. [46:15] I think, yes, the customer support is definitely there. [46:18] Like I said, Sam Altman's like, number one, customer support. So you're like, okay, I guess we should adapt. Adapt or die is kind of our mantra, adapt or die. I think that there are other industries where [46:33] They're on the same journey, it's just not as obvious. So for example,

[46:37] Reporting software. [46:38] you know, Tableau or any kind of reporting product. [46:42] How do they work? Well, they're like the typical kind of like, [46:46] you know, read/write app, build dashboards, filtering, querying, you know, kind of hardcore querying, kind of query a database, get some numbers, show it in a UI. [46:54] A lot of thought and care goes into how you present that data to people, the charts that are appropriate. [47:00] help people make good decisions ultimately. [47:04] I think, again, this is like hand wavy, who knows? [47:08] Maybe that's all. [47:10] Done? [47:11] dead now, and the reporting product of the future is just a box. [47:15] and the box just goes to the database, and the box is just, [47:19] What was our best sales one last year? [47:21] January. Okay. [47:23] Who is our top performing rep in January? [47:25] you know [47:26] many. Like, the reporting products of the future might look like that. And so [47:30] Project management tools is another one. It was a bunch of, [47:33] products that I think are [47:35] just outside the most obvious customer support one, and yet, [47:38] equally ripe for [47:40] a newcomer to come with a completely different paradigm [47:43] and potentially take over. [47:45] I like that this connects back to your very first point about [47:48] trying to think about where AI integrates is think about what problem are you solving as a company, for example, Tableau. [47:53] helping people [47:54] visualized data. [47:56] And then the question is, can AI just do this for you? [47:58] And in that case, oh, maybe you can. [48:00] And that gives you basically a whole strategy of like, okay, how do we actually... [48:04] Do that. They are. [48:06] yeah and it's very hard to you know if you're i don't know if the reporting thing will play out that way but

[48:11] If you're like a Tableau type company, [48:14] You've tons of designers who design dashboards and filters and querying type [48:18] Like workflow. [48:20] Like, what do they do? The UI is the box. [48:22] You know, so it's really hard to [48:25] It's really hard to get into your head like, we must, if you believe, if you have conviction that we must change. Really hard. [48:33] Maybe one last question here for team members learning and starting to work within this realm. Is there anything you find [48:39] helpful to get them ramped up other than the advice you've already shared, which is just [48:43] read a lot of stuff. [48:44] Watch. [48:45] Twitter slash X. [48:46] subscribe to these newsletters, and then just try it. [48:49] I also try and read things [48:51] that say it's all a load of crap. [48:53] you know, so like it's very easy to, [48:57] I've been guilty of this many times. Back to the mistakes you've made. I've been guilty of this many times where I've jumped on a bandwagon. [49:04] and [49:05] uh it was all wrong and like the the older i get [49:09] Like the Web3 thing, I'm like, I don't even know what Web3 is. [49:12] Crypto, I never bought crypto. Maybe I'm wrong about that. [49:15] But-- [49:16] I'm not a bandwagon jumper. [49:17] But I kind of, maybe might have been when I was earlier. [49:21] And I try these days to read the alternative opinion. [49:25] Um, [49:26] people who are skeptical or... [49:29] Or think it's bad. You know, a lot of people think this is terrible for humanity. This technology is going to... [49:35] eat us alive, you know, so [49:37] I try and balance my [49:40] optimism,

[49:42] I'm kind of a delusionally optimistic thinker, so I try and balance that with [49:47] negativity, I guess. That's really good advice. [49:51] Yeah. [49:52] Is there anything else in this realm that you think might be useful to share before we shift to a different topic? [49:58] The other thing is don't be afraid. [50:00] Maybe... [50:03] I think people are a bit afraid of it. For example, [50:07] If I started walking around our office here saying, [50:10] "Hey, I think we only need two engineers per team. [50:13] Going forward. [50:14] That's probably not really a good idea to do that. I think in reality that's not going [50:19] be how it plays out. Like there's all sorts of like, [50:22] Those are great studies over the years about how people don't end up losing jobs [50:26] the jobs get moved around. And also, you know, for customer support, for example, it's a high attrition job. So, [50:32] People say like, hey, everyone's going to lose their job. [50:34] A bot's going to take over. [50:36] Maybe some of that will happen, but probably to attrition. [50:39] As in, like, someone quit. [50:41] and just didn't get backfilled. So the doomsday scenarios, I don't think will play out as much. [50:46] For sure, like, [50:48] You know, it's easy to kind of be afraid of it. [50:51] Um, [50:52] And I think you kind of have to lean into it. [50:54] I love that. [50:56] Okay, I want to chat about frameworks. You have a lot of interesting frameworks that you've put out there. [51:01] Maybe we do kind of a rapid fire through a number of frameworks [51:05] that you've [51:06] worked with and find useful. And the first act, you actually mentioned this before and after, which I hadn't heard about, [51:11] What's the general idea to that concept?

[51:14] Before/after is literally that simple, I think. Like we have a rebrand at the moment happening, and that will be a before/after moment. [51:21] We're redesigning our pricing. [51:24] And then the day that pricing was live, that would be a before or after because it was like, [51:28] Nothing's the same, and so we need to go back out and talk to people again. [51:33] Like, I'm a big believer in talking-- you've got to talk to customers. It's the only way. [51:36] You gotta talk, talk, talk. Learn, learn, learn. [51:39] Don't take what they say at face value. Go deeper. [51:42] And so, you know, a lot of these before-after moments [51:45] Once you've passed the app into the after, [51:48] You got to start learning. [51:50] Were we right? Were we wrong? What happened? What do people think? You know, [51:54] Can you talk more about this pricing? [51:56] learning slash mistake you shared what do you think you did wrong what happened there [51:59] We had a principle called Align Price to Value. [52:04] Thank you. [52:05] By the way, I like... [52:06] I think pricing is incredibly difficult. [52:10] Uh... [52:12] A lot of the design team who are comprising here, I say to them, it's one of the hardest design problems I know. [52:19] I think onboarding is another one. Onboarding people into a product is also... People are like, "Oh, hey, you just designed a few steps and it's pretty easy." People follow the steps. Again, deceptively difficult. [52:29] to design great onboarding. So I think pricing is deceptively difficult. [52:33] We had a principle around aligning price to value, [52:36] You know, people should pay based on the amount of value they get in the product. [52:40] easy to say and incredibly hard to do. [52:43] values subjective.

[52:45] The price is for some person, they got like... [52:49] 10 units of value, I think that's about $5. [52:53] Someone else is like, I'd pay you $5,000 for those 10 units of value. The biggest mistake was... [53:00] A lot of mistakes compounded. [53:02] And this is an area where I think we were risk averse. [53:06] We've ended up with too many pricing models. [53:09] we've, [53:10] built on top of old comparative mistakes. [53:13] and it took a brave decision to say, [53:16] We're going to start again. [53:18] Well, this feels like it could be its own episode, just talking through your pricing [53:22] lessons and journey. [53:23] Maybe is just, is there a nugget of wisdom you could share for someone that's trying to think about pricing right now based on your... [53:29] your experience. [53:30] The number one thing I would say is keep it simple. [53:33] Keep it simple. It's so tempting to... [53:38] Like with us, for example, a lot of SaaS products, [53:41] have add-ons where like [53:44] "Hey, we built X," and that's like 10 bucks, or... [53:48] 100,000, what kind of product you're selling. We built X, and that's the price of X. [53:52] "Hey, we just built Y. Y is awesome, and it's a new thing you can do, and it unlocks all these new capabilities." [53:57] People shouldn't get that for free because it's a new thing they didn't have. [54:01] So let's charge like more for Y. [54:03] But that doesn't really work with the other, okay, let's look at an add-on. [54:07] Oh, yeah, cool. People just add on. [54:09] But then, like later, now you've got like, [54:11] people who have the add-on and [54:13] people who don't. And then you're like, add another thing. And

[54:16] So like tiering, we've added tiers. [54:19] We've like... [54:20] you know, [54:21] Cut different with products, tiers. [54:25] Add-ons. [54:26] tiering in the add-on. [54:28] people can't understand their bill. [54:32] My advice is keep it simple, reject, fight so hard to not [54:36] to resist the temptation to [54:39] Add extra... [54:41] ways in which you price. [54:43] Amazing. [54:44] I didn't [54:45] I think about going into this topic, but I'm glad that we touched on it. I was talking about scars for life earlier. That's another scar for life. [54:53] All right, let's keep talking about some frameworks. Another that I found that I loved is something that you call differentiation. [55:01] versus table stakes, what's that about? [55:03] It's kind of like the Kano model, if you're familiar with that. But it's very simple. It's kind of like... [55:07] I guess we took the Kano model and tried to make a really crazy simple version of it. [55:11] Again, like, I... [55:12] I'm a little bit allergic to things like this. I can't even hate myself for bringing up the cleaner model. [55:16] I'm allergic to like [55:18] people over intellectualizing frameworks and like, you know, "Oh, well, if you've seen the new different law of whatever, [55:24] I'm like, [55:25] keep things simple, practical and pragmatic, and then let's all, again, go back to work and start building the product so that customers can benefit, because that's actually all that matters. And so difference versus table stakes, very simple. [55:37] I think people who [55:39] - Yeah. [55:40] adopt a product or buy a product or switch to a product, [55:44] There's kind of two driving forces. One is

[55:47] the attraction of the new solution. [55:49] And that's basically differentiation. So what's different and better? [55:54] But critically, what's different and better [55:56] in ways that [55:58] customers care about. [55:59] Again, back to all the failed projects, my lesson from a lot of these was [56:03] We were different and better. [56:04] in these Google projects in ways people didn't care about. [56:07] You know, like all sorts of Google projects, like Google Wave was an amazingly innovative product. [56:13] that no one really cared about. [56:15] Be different and better in ways people care about. So that's the attraction. That's like, oh, I want to check out that. That looks cool. I want to check that out. That looks better than what I have today. [56:24] But on the other side, [56:26] There's like a kind of entry requirement or like table stakes. You know, to play the game, you got to have... [56:33] a certain amount of things. [56:35] and so they're table stick features. [56:37] They're often very boring. They're like real basic stuff, boring stuff. [56:41] and easy to ignore and easy to not build. And again, a mistake with Intercom maybe over the years is that we were much [56:47] more attracted to the differentiation, and built it out of that. [56:50] So we went through different... [56:51] iterations of our roadmap sometimes like changing over the course of the of a of a year or two where [56:57] we were like, oh, the differentiation to realize that [57:00] Everyone loved it and really wanted to buy it. [57:03] but they couldn't because we didn't have the basic report that they needed, or we didn't have the basic permission feature that they needed. [57:09] And then the Roboop was built based on those [57:11] But like, [57:12] trading off [57:13] whether we need more differentiation or trading off, whether we need to invest more table stakes. [57:16] So these days,

[57:18] The place to binge come today is like we're kind of 50/50 probably in terms of resources. [57:21] but it has swung 70-30 in both directions at times. [57:26] The last piece about it is, I think it's really powerful to look at a roadmap or look at a proposed roadmap and ask yourself, [57:32] Which of these two things matters more to us? Not to us, actually, to our customers right now. [57:37] The other thing that we've talked a lot about here internally is [57:40] If you're a startup, [57:43] and you're entering any kind of established category, [57:45] Customer support for us, big established category, massive. [57:49] A lot of table stakes built up over years, decades. ServiceNow, Service Cloud, Salesforce, Zendesk, decades of table stake feature building. [58:00] So to play the game, [58:02] You need a lot of the table stakes. [58:04] unless [58:05] you have incredible differentiation. So from the early years of Intercom, people just bias alongside [58:11] Service Cloud or Zendesk, they just buy us alongside. They're like, this intercom thing, we were like messenger first, modern messaging. [58:18] and modern UX, they were like, "We want that for our customers. [58:22] alongside the big giant bag of table stakes, because Intercom doesn't have any of those. [58:26] Then over the years, we've built the table stakes to a point where [58:29] okay, now we can fully play the game and we can like... [58:32] people can switch, so they can swap Zendesk for intercom. But it took us years to get there, you know? And then, hence, if you're a startup, you need to invest a lot more in differentiation. [58:43] And then over the years, I think you start to balance the books a bit. [58:47] I think what's interesting about this is one, it just

[58:49] Gives you a way to think about looking at your roadmap. [58:52] How much are we actually doing? And are we doing too much table stakes? Are we doing too much differentiation? [58:56] So it gives you kind of an awareness of what's happening. [58:59] And I think there's also [59:00] It's an interesting strategy as a startup. [59:03] spend years [59:05] doing table stakes and then launch, or does it go [59:08] the way Intercom went, like, differentiate first, we'll build everything else later. [59:12] I wonder when it makes sense to go one or the other. [59:14] Yeah, and it probably depends on [59:17] Different categories and all sorts of things, yeah. [59:20] Awesome, okay. The next framework [59:23] is something you call swinging the pendulum. What is that about? I actually kind of mentioned an example of it earlier. Like the differentiation in table stakes with swinging the pendulum. So, swinging the pendulum means [59:36] you take a step back from [59:38] everyday work life and [59:41] you kind of make the observation that [59:43] Something's in an undesirable state. [59:45] So like-- [59:46] You know, maybe it's, well, we've all the differentiation in the world, but people can't adopt the product because we've never built any of these table stakes. [59:54] That's, like, undesirable. Or... [59:56] "Oh, we've now built all these table stakes and we've not been investing in differentiation." [1:00:01] we're not that attractive to people because switching product is like a pain and we're not just not attractive to people. We need to like, okay, so, [1:00:07] this undesirable state, and then, so you go and fix it, [1:00:10] But the temptation is that you overcorrect. And we've done this. [1:00:14] so many times in so many domains, everything from [1:00:18] Okay, we don't have enough differentiation.

[1:00:20] A year later. [1:00:22] Oh, wait a minute. We're missing all the table stakes. Okay, everyone over there. So product building is one. People is another one. Building out teams and people. Another big one was... [1:00:32] maybe, I don't know, maybe five years into Intercom, [1:00:36] We were on this kind of high growth trajectory, really kind of good classic startup, [1:00:41] before our pricing problems. And we kind of like... [1:00:47] We looked around and said, [1:00:49] None of us have done this before. [1:00:51] I don't think that's good, undesirable state, [1:00:54] Do we even know what we're doing? [1:00:55] like [1:00:56] Where's a bunch of random people? Do you know what we're doing? [1:00:59] We need to hire some experts. [1:01:01] We need to hire some experts. [1:01:03] If we're going to go up market, we need up market people who've done it before. [1:01:07] So, [1:01:08] That was an undesirable state. Fix it by hiring people who've done it before. [1:01:12] Then we hired loads of people who have done it before. And what they did was brought [1:01:16] the culture and ways of working of their prior company to Intercom. [1:01:21] And so we totally overcorrected. [1:01:24] Didn't work out in a lot of cases. In most cases, it didn't work out, [1:01:28] We weren't trying to be... [1:01:30] bigger company that already exists we're trying to be us you know so I like hiring hiring and building teams as a matter where you [1:01:38] We really overcorrected to... [1:01:40] find out like, okay, there's a balance here. Related to that one, related to hiring one is like generalists and specialists, kind of similar theme. [1:01:47] people who have done it before, or people who are specialized.

[1:01:50] and we hired a bunch of specialists [1:01:53] only to realize that [1:01:55] They're not adaptable. [1:01:57] and [1:01:58] In Intercom, we believe in kind of, we've a lot of ambiguity and we lean into the ambiguity. [1:02:04] People who are highly specialized [1:02:07] can thrive in big companies. [1:02:09] really thrive they're invaluable employees [1:02:12] But in a fluid [1:02:14] startup-y culture, [1:02:16] with a lot of ambiguity, they can really drown, really struggle. [1:02:20] Maybe the middle of this pendulum kind of landing in the middle is, [1:02:23] Let's hire someone who [1:02:24] has done a bit of it. [1:02:26] And I have a bit of specialism, not much, but enough to try and figure it out. So we hire a lot of those kind of people today. [1:02:34] First of all, I love all these stories of things that didn't work out because a lot of people [1:02:37] don't like sharing these, and this is what people want to hear. It's like, here's [1:02:41] Not everything was perfect. Here's a lot of mistakes that were made along the way. [1:02:44] and it feels like this framework is a result of just doing this too many times. [1:02:48] is the main lesson here. [1:02:50] generally avoid swinging the pendulum too far. [1:02:53] Because sometimes it's worth it, like in this case of AI, it's like, no, we're going all in. Or in mobile, it was worth going all in. [1:02:59] Is there kind of a-- I guess, yeah, what do you think of when I say that? [1:03:03] In talking to people about this before, [1:03:07] Sometimes the conclusion of the conversation is something like, [1:03:11] It's the only way to do it. [1:03:14] Like you actually can't do it a different way. [1:03:17] And so maybe the question is really like, how high up, how high does the pendulum go?

[1:03:22] versus like you gotta swing it. [1:03:25] And then it's like, how far do you swing it? And for sure, you're right. With AI, we are like... [1:03:29] We're actually, we're still going to pretty high. Maybe I overestimated earlier, like, [1:03:34] If AI is in the differentiation camp to mix the frameworks, [1:03:38] We're still building a lot of table stakes features too, like building depth into the product. [1:03:42] And that's 50-50. [1:03:43] I think I mentioned 50-50 earlier. So that's 50-50. So we're not totally swinging it. We're not like... [1:03:49] It's swung, but... [1:03:52] We're also kind of doing the other thing, balancing things out. [1:03:56] I think you probably have to swing it. [1:03:59] It reminds me, to know where the boundary is, is what I was going to say. It reminds me of a story, you know, like back to the olden days stories. I remember when I went, I remember at Google, [1:04:09] Privacy was like really top of mind. [1:04:11] to the point that it would block decisions, block product progress. [1:04:15] just privacy, circular conversations, so many circular conversations. [1:04:18] and nothing ever got built or shipped. I worked on a project for a year at Google. [1:04:23] and we shipped nothing in the year just circular conversations uh which killed me at the time so [1:04:29] When I went to Facebook, I realized they have a different approach to privacy. Again, I'm not advocating this is necessarily good. [1:04:35] It certainly didn't help their brands. [1:04:37] There was... [1:04:39] kind of an idea that [1:04:41] To know where the boundary is, you've got to cross it. [1:04:44] And crossing is painful. [1:04:46] but if you don't cross it, you'll never know. So if you think you're going up to the boundary, [1:04:51] Then you stop before it, turns out it's actually miles over there.

[1:04:54] So I think with a lot of this stuff, [1:04:56] You don't really have a choice. You've got to cross the boundary. [1:05:00] Feel the pain. [1:05:03] be humble enough to realize you didn't get it right and [1:05:07] kind of go again or [1:05:10] whatever the Right Course Act Corrective course is. [1:05:12] Yeah, get that pendulum off the pivot thing that it's on, and then let's fix that pendulum. Let's put it back. [1:05:18] - Yeah. [1:05:19] Okay. [1:05:21] Another framework that I... [1:05:23] read about briefly, and I love the general idea of it already, which is something that I think you call product market [1:05:29] Story fit. Yeah. What is that? So, yeah, it was product market fit. [1:05:35] Basic, well understood, very important. [1:05:37] The way I describe product market fit is [1:05:39] You've got to build the right product for the right market. [1:05:42] I think, by the way, as an aside, not enough people think about the market side of that equation. A lot of product people don't think about the market side. [1:05:49] But for me, it's very simple. The market is the people. [1:05:52] the problems they have, [1:05:54] and how important the problems are to them. [1:05:57] To have a good market, you need a lot of people with the same problem. [1:06:00] and they need to care a lot about it. Going back to the Google social stuff, we found a lot of people [1:06:06] with the same problem, but they didn't really care. [1:06:09] They didn't really care. What they had was fine. So a lot of people with the same problem, and a lot of energy around the problem, and the product is the solution to that. If the market is who, the product is the what. [1:06:21] and [1:06:23] I just, I don't know, in my career again, so a bunch of products that were built, they were good products in good markets.

[1:06:30] And they failed. [1:06:32] And I couldn't work it out. [1:06:33] And [1:06:34] Eventually I came back to this idea that like, [1:06:37] And maybe someone might say, "Paul, that's marketing." You're talking about marketing. But story, the story's wrong or the story's missing. [1:06:45] And so sometimes it would be a great product and a great market, [1:06:49] explained in a convoluted way. [1:06:52] I used to see that a lot at Google again. It explains in a very complicated way, over-intellectualized. [1:06:59] And as a result, people are like, [1:07:01] What are you talking about? You know, you don't get their attention. And so the story, [1:07:07] is really important as important and actually sometimes you'll see like [1:07:11] Not great products. [1:07:13] Certainly worse on paper. [1:07:15] I'm trying to remember the Spotify competitor back in the day, people were like, what was the name of it? Rdio? [1:07:20] Yeah, audio. Audio was one of these where like... [1:07:23] - Yeah, people are great. [1:07:25] All I've ever heard about Ordeo was amazing product. [1:07:29] It's failed. [1:07:31] And why did it fail? Spotify and audio are the same market. [1:07:34] They were solving the same set of problems. Ordeo was arguably the better product at the time. I don't know if that's true, but arguably the better. I always think Spotify is an incredible product. [1:07:42] But the story, they got the story wrong. [1:07:46] And so again, I think [1:07:47] All product people, whether you're a designer, product manager, people in research, [1:07:51] Data science. [1:07:52] You need to think about the story all the time. Worker marketing, product marketing, [1:07:57] And like, [1:07:58] Learn about how to explain the product.

[1:08:01] as much as how to build a product. [1:08:04] Makes me think about it. [1:08:05] positioning [1:08:06] and how important that is. [1:08:08] And we had April Dunford on the podcast very recently talking a lot about that. [1:08:12] - Yeah, yeah, yeah, she's excellent. Yeah, it is really like, why are you better? [1:08:17] And can you explain why you're better? [1:08:20] Bye. [1:08:20] Such an important point. [1:08:23] A final area I wanted to touch on is jobs to be done. [1:08:26] So we had the co-creator of Jobs to be Done on the podcast. We had Sri Ramakrishnan on the podcast. They'd very much disagree about that. [1:08:34] how effective jobs to be done is. I know you guys are big on jobs to be done. [1:08:37] So what are your general thoughts on the jobs to be done framework? [1:08:41] Effective was it for you all? How do you use it? [1:08:44] What do you find work, doesn't work, whatever comes up. [1:08:46] Yeah, I'll be totally honest at the risk of offending people if they listen. Like we worked with Bob Muesta, you know, who's age of eight years ago, and Bob's the right guy. And we kind of followed that model of JavaScript more than the ODI, I think is the other thing. [1:09:02] Anyway [1:09:03] I'll try and say this in a simple way. [1:09:05] We found Josson 2 really good. [1:09:07] Very, very useful. [1:09:09] But in a very simple way, and getting back to this idea of simple frameworks, in a simple way, [1:09:14] Thank you. [1:09:15] kind of separately, [1:09:17] There's like so many people who spend so much of their energy [1:09:21] debating the nuances and peculiarities of one version, who cares? Like no one cares. Oh, I don't care, they care obviously. But I'm like, your customers don't care. Like people you're trying to build a product for don't care. No one cares.

[1:09:36] That's a cool intellectual debate. [1:09:39] But kind of, for me, maybe this is too extreme, [1:09:43] it doesn't really have any place in work, you know, like in the work we do. [1:09:48] We're just trying to build a great product. And so for us with JavaScript, [1:09:51] It was a really good way of us centering on the customer problem [1:09:55] like focusing on like, [1:09:57] not getting distracted, [1:09:58] basing it in research, like good, solid research-informed insight [1:10:03] that told us the thing people were trying to do, [1:10:06] Like, what is the thing people are trying to do? Again, energy. Do they have a lot of energy around it? [1:10:11] Maybe the energy thing might have come from talking to Bob, actually, now that I think about it. I think it did, actually. I think... [1:10:16] Like the idea of this idea that-- [1:10:18] You need people who have a lot of energy around the problem. And you kind of have to interview them for that most of the time to feel the energy they have. [1:10:26] It's very easy to see if someone's apathetic versus like into it. So, so we've had a pretty good and we invented this job stories thing kind of by accident. [1:10:35] I can't remember exactly what happened, but like [1:10:38] I wrote out this way of writing a job story, basically. Well, we didn't call it job story. Someone else called it that. We just... [1:10:44] At the time, we're like... [1:10:45] There was this, I can't even remember, you know, there's like a trigger and an, anyway, [1:10:49] Um, [1:10:51] We didn't even give the thing a name. Someone else named it, I think. [1:10:55] I'm just like, "We're just trying to build a great product." So we found it really good in that way, really simple. And then the other one that we use a lot still here is... [1:11:04] Thank you. [1:11:05] The Four Forces...

[1:11:07] which is this framework out of jobs we've done, the four forces being like... [1:11:13] There's different forces when people try and switch product. And some of it's the differentiation table stakes stuff, like the attraction of the new solution, [1:11:21] the reasons that you might not adopt it, habits, people have anxieties, [1:11:25] Here's another kind of funny story. [1:11:28] to tell you how much the four forces is really good here's a here's a funny story [1:11:31] I was saying earlier that Owen and Daz were trying to convince me to leave Facebook, which I loved at the time, joining to come. [1:11:37] They wrote out the four forces for me. [1:11:39] to join. [1:11:40] and then secretly, [1:11:42] over a few beers, talked to me, and fed me my anxieties. [1:11:46] and like, you know, like, whatever, like, you know, [1:11:51] basically worked me on the four forces. And I was like, that is, [1:11:54] That is genius. That is ingenious. Maybe it's a bit... [1:11:58] But it's ingenious. And so it's just, the Four Forces is incredibly good at [1:12:03] helping understand why people make decisions. [1:12:06] I love that a lot of your advice just continues to come back to keep it simple. [1:12:10] Cut away anything that isn't necessary. [1:12:12] And I find the same exact thing with jobs to be done. I find it really useful as a framework for the podcast, the newsletter, but [1:12:19] I think there's this, like, endless... [1:12:21] set of processes and [1:12:23] ways of optimizing that gets people [1:12:25] distracted and [1:12:26] often just kind of slows everything down. [1:12:28] yeah yeah i mean and it's interesting and fun to talk about sometimes like really fascinating you know [1:12:33] But-- [1:12:35] Unless you're like an academic,

[1:12:37] But if you're working in a company [1:12:39] that you're trying to build a software product for people to improve their lives in some small, meaningful way, [1:12:45] Like, it doesn't matter. You know, just use the thing that helps you do that. That's the goal. [1:12:50] And use the thing that helps you do that. [1:12:52] and [1:12:53] That's it. [1:12:54] With that, we've reached our very exciting lightning round. Are you ready? [1:12:58] I'm ready, yeah. [1:13:00] What are two or three books that you've recommended most to other people? [1:13:04] Yeah, the two books I recommend to everyone always, I've copies in my office here, it's not how good you are, it's how good you want to be. [1:13:10] It's a book by Paul Arden, who worked in advertising a long time ago. It's an excellent book. It kind of shows people that... [1:13:16] You've got unlimited potential, if you think about it the right way. Everyone does. The second book I recommend to everyone and buy for people and give to them is Principles. [1:13:24] by Ray Dalio. [1:13:26] I'm a big fan of Ray Dalio. I think he's incredible. I'm a big believer in principles. A lot of us at Intercom are. I always get those two books. And they're totally different. The Paul Arden book, you can read it in 20 minutes. Principles is like... [1:13:37] What is a favorite recent movie or TV show that you've really enjoyed? [1:13:42] Most recent is the bear, which I came to late. [1:13:45] The reason I actually love the show is because I think it, [1:13:49] somewhat celebrates the grind [1:13:52] And I think that's important. I worked in coffee shops a lot when I was younger. [1:13:56] when I put myself through college and stuff, and like the grind is... [1:14:00] part of life and part it's in the sag the grind is a necessity to get things done and get [1:14:05] make great things happen sometimes.

[1:14:07] And I like that about it. I really like that about it. [1:14:09] What is a favorite interview question you like to ask candidates? [1:14:13] Yeah, I'll give you a slightly different answer. [1:14:15] I don't really have said any few questions for candidates, and I don't like questions that rely on memory. [1:14:21] you know a lot of like tell me about the last time you did x you know [1:14:25] Here's an amazing question I got given recently by Alyssa, who used to work here. I had to do referral calls. So like you're interviewing someone, you want to give them the job, and they've got referees. And of course the referees they have are like the best people that they ever worked with and their favorite managers. [1:14:39] So this question is, [1:14:40] What feedback will I be giving this person in their first performance review? [1:14:45] That's an amazing question because the person can't dodge it. [1:14:48] You know, there's an answer. [1:14:50] and [1:14:52] It's incredibly enlightening. [1:14:54] And that's a question you ask on reference calls? [1:14:57] Yeah, on reference calls. That is such a good question. I love it. [1:15:00] It's an amazing question. All right. What a gem. Thank you for sharing that. [1:15:04] What is a favorite product you recently discovered that you really love? [1:15:08] I know this is kind of like maybe cheating, but I go back to a lot of the AI products. I think ChatGPT Vision is mind-blowing. [1:15:15] I've been playing with Rewind lately. I was a bit late to it. Des and Kieran, a bunch of people here, founders of Intercom, love Rewind. Use it and love things. It's amazing. So I'm a bit late to that, but... [1:15:24] Um, [1:15:25] It's just like augmented memory. It's kind of like [1:15:29] Again, kind of mind-blowing. So rewind's been fun. [1:15:32] And they just came out with a little audio thing that can record your actual day. [1:15:36] Yeah, I'm not so sure about that. Yeah, got some flack.

[1:15:40] Yeah, I'm not so sure about that. [1:15:42] Yeah. [1:15:43] I don't know if it's real. It kind of looked like not a real product when they launched it, but I think it's real. [1:15:48] Well, and it's if he toes into the... [1:15:51] what's okay and not okay with AI. Yeah, yeah. [1:15:55] It's a cool theory though for sure. [1:15:57] What is a favorite life motto that you often come back to, share with people, find helpful for yourself? [1:16:03] Yeah, I have a post-it on my monitor that says, only work on what matters most. [1:16:09] It's on my monitor, it's posted. And if someone falls off, I have to write it again. Only work on what matters most. [1:16:17] It's amazing. I go into work, someone emails me, and I'm like, "Oh, God." I'm like, "Only work on what matters most." [1:16:23] The second one is, they're related is, [1:16:26] Stop worrying about things you can't control. [1:16:28] And so I have two of those. [1:16:31] Only work what matters most. Stop worrying about things you can't control. It just like reduces the temperature again like life lessons learned and [1:16:38] I sent a lot of dumb emails in my past, you know, [1:16:41] like, "Red energy," "Oh my God, what are they thinking?" You know? Like, you wake up in Dublin to a San Francisco email, you're like, "Oh God, rah, you know, keyboard." [1:16:51] If your monitor says... [1:16:53] These two things, you just don't do that. You just take a breath, get a coffee. [1:16:58] Come back. Does it really matter? You know? [1:17:01] Beautiful. [1:17:02] That second one I think I learned first from Seven Habits of Highly Effective People. [1:17:07] Have you read that? Yeah. [1:17:09] Just think about the focus, the circle that you have things you can control, and then there's like the circle of things you can influence, and then there's the things you have no control over.

[1:17:17] and [1:17:18] I find that really helpful myself. [1:17:20] I love that you have it as a post-its. I feel like I need to make post-its of all these lessons people share as their little mottos. Yeah, the post-it on the monitor is a real life hack I found a few years ago. It's kind of dumb in a way. The post's on the monitor. It's in the way. [1:17:34] Well, you actually put it on the monitor in the way of your screen. Yeah. Oh, wow. It's in the bottom left, just covering the bottom. [1:17:43] Because otherwise, if it wasn't there, I wouldn't look at it. I make myself look at it. Yeah. Wow. [1:17:50] - Sure. [1:17:51] precious real estate on their monitor. [1:17:52] Yeah, that works. [1:17:55] What's the most valuable lesson your mom and your dad taught you? [1:17:58] The biggest one, again, so reductive and simple, is to be nice to people. [1:18:03] I think... [1:18:05] Being nice goes way further than people really realize. [1:18:10] One thing that I've learned, again, the hard way through life is [1:18:13] You have no idea what's going on in people's lives. [1:18:17] You've no idea. People could have all sorts of like really stressful... [1:18:21] All sorts of personal stuff going on. [1:18:24] And the reason they did the thing in work that you didn't like [1:18:27] It's because of that. And so like, [1:18:30] I try and think like, [1:18:31] Be nice. [1:18:33] You don't know what's going on. [1:18:35] like you might learn later, don't, you know, [1:18:37] Like, [1:18:38] Don't act in a way you would regret. [1:18:41] I think being nice. [1:18:43] In life. [1:18:44] goes far further than [1:18:46] Mostly boo.

[1:18:47] give it credit for it because it's kind of too much of a, I don't know, [1:18:51] fluffy truism or whatever. [1:18:54] I 1000% resonate with that. I've been told I'm too nice and I had to become a little less nice, but I still can't lose that. So I fully... [1:19:05] Biting to that, my parents taught me a similar lesson. [1:19:08] Yeah. And sometimes it's hard. Like, um, [1:19:11] I'd never fired anyone before I joined Intercom, for example. [1:19:14] I really did not like doing it. [1:19:17] And since then, I've done it quite a few times in a bunch of different circumstances and realized [1:19:21] It always works out for both sides. And the nicest thing to do [1:19:26] It's to do the harder thing. [1:19:27] It's actually the nicer thing to do. People are like relieved. [1:19:31] in this example, [1:19:32] it's, [1:19:33] It's a nicer thing to do. So it can be a complicated one. [1:19:38] I love it. [1:19:40] Final question. You're Irish. You're based in Ireland. What is an Irish food you think people should definitely try out if they ever want? [1:19:48] visit Ireland. [1:19:49] Can I cheat and say Guinness, South Food? Absolutely. The Guinness in Ireland, people talk about this and it's true, the Guinness in Ireland is much, much better for a whole bunch of reasons. [1:20:03] It's basically a fresh product and it's brewed here. [1:20:05] The way I think about it is it's like milk. Milk goes off, Guinness goes off. [1:20:09] Guinness is older than a few days old, tends to start deteriorating. [1:20:13] So Guinness Naurant is amazing because it's made here. [1:20:17] The other thing I think that Ireland does really well is fish.

[1:20:20] Ireland has not had, by the way, the greatest reputation for culinary excellence. [1:20:25] over the years. I think Irish food in the States in particular is not good, but [1:20:30] The fish here is incredible. You can get incredible fish in Ireland's obviously an island so I [1:20:35] There's a lot of fish. [1:20:37] On the Guinness front, is there any way to get the good stuff not in Ireland? Or is that just, you gotta go? [1:20:43] No, there is, actually. You just need to be near a brewery. [1:20:47] You need to be like, and so Guinness is a brood in Nigeria. [1:20:50] There's a huge Guinness market in Nigeria. I think they actually use a different recipe, but it's brewed there. [1:20:56] I think the brewery in the US is... [1:21:00] uh somewhere on the east coast between new york and eastern canada so it's somewhere there so often the guinness in new york can be actually pretty good [1:21:08] The Guinness in San Francisco tends to be really bad. [1:21:12] I remember talking to someone about this that works in Guinness. One of my friends does a lot of work in Guinness. [1:21:16] I think the boat, Carrie of the Guinness, goes down through the Panama Canal, [1:21:20] back up to San Francisco so you're like 12 weeks old or something. Wow. Did not think we would be learning about [1:21:28] the travel path of Guinness from... At least this is what I've heard. The Guinness has so many myths, you just don't really know what's true, but... [1:21:36] These are the stories I've been told. [1:21:37] Amazing. Paul, you are awesome. Thank you so much for being here. Two final questions. Where can folks find you online if they want to reach out? And how can listeners be useful to you? [1:21:45] I have a handle I use everywhere, basically P-A-D-D-A-Y. [1:21:49] It's like Paddy with an extra A. So P-A-D-D-A-Y. That's everywhere. So P-A-D-D-A-Y.

[1:21:54] patty at gmail.com. [1:21:55] @paddy, it's my handle everywhere. So that's where you can find me. [1:22:00] Um... [1:22:02] I'd love, yeah, I'd love people to reach out to me, like genuinely learn. I'd love to hear from people who think my AI talk is nonsense. [1:22:10] You know, it's more like a crypto Web 3 or, you know, I'd love to hear people who have alternative opinions. [1:22:16] and challenge mine. That's how... [1:22:19] I kind of like to learn and get better. [1:22:22] If people have those opinions, I'd love to hear them. I'd love to talk to them. [1:22:25] Be careful what you wish for. The YouTube comments are always a spicy place. We'll see what we see. Awesome, Paul. Thank you again so much for being here. [1:22:33] Yeah, thanks, Danny. I really appreciate it. [1:22:34] Bye, everyone.