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

Transcription for the video titled "What AI means for your product strategy | Paul Adams (CPO of Intercom)".


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Introducing Paul

Paul’s background (00:00)

And this is like a meteor coming towards you. This is going to radically transform society. 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 does it solve for them? That kind of thing. So go back to basics and then ask, can AI do that? And for a lot, the answer's going to be yes, it can. For some, it might be it can partially do it. And then maybe for others, it can't, you know, 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. And then, you know, in other places it'll be 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 ask yourself, okay, what are we going to do? Today, my guest is Paul Adams. Paul is Chief Product Officer at Intercom, a role that he's held for over 10 years. 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. We then get deep into how to think about AI as a part of your product strategy, including a ton of great examples from Intercom's experience going all 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 Twitch, Miro, ClickUp, and DraftKings rely on EPPO to power their experiments. Experimentation is increasingly essential for driving growth and for understanding the performance of new features. And EPPO helps you increase experimentation velocity while unlocking rigorous, deep analysis in a way that no other commercial tool does. When I was at Airbnb, one of the things that I loved most was our experimentation platform, where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own. EPPO does all that and more with advanced 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 EPPO at geteppo.com/lenny and 10x your experiment velocity. That's geteppo.com/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, Python, or no code in any combination and work together with live multiplayer and version control. 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. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel, and Algolia using Hex every day to make their work more impactful. Sign up today at hex.tech.com to get a 60-day free trial of the Hex team plan. That's hex.tech/lenny. Paul, thank you so much for being here and welcome to the podcast.

Experiences And Insights In Paul'S Career

Freezing onstage in front of 8,000 people (04:09)

Thanks, Lenny. It's nice to be 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. Also, you have an Irish accent, which is always a boost for ratings in my experience. So thank you for bringing that with you here. I wanted to start with a couple of stories. So the first is your story of giving a keynote at Cannes. Can you share what happened there? Yeah, some things that happen at work, every member at the time, they don't really scar you. This goes in the book, it would have scarred for life. Yeah, it's a good long story short. I was at Facebook just over a decade ago loved it at the time. I think it was a great place to be at the time and based in San Francisco. I did a lot of talks for Facebook internally and externally. Facebook had a keynote slot, always have a keynote slot at Cannes, the world's biggest advertising festival. And the year prior, Zuck had been interviewed. He was the speaker. He'd been interviewed, gotten a hard time on privacy. It didn't go as well as they'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 yeah, I got out in front of the stage, you know, the world's biggest advertising stage and I'd say I was like three, four minutes into the talk, a talk I'd given a very similar talk to what I've given lots of times and I just froze. I couldn't remember what I was supposed to say. It was the first time in my life I'd rehearsed a talk word for word, you know usually like I have talking points and I'll ad lib, and you know things get mixed around and it's kind of informal. This was like, you know, media trained like, don't say the wrong thing kind of talk and I just could not remember what to say. I had some version of a panic attack, walked off stage. I was still mic'd up, cursed, everyone started laughing. I was like, she's really laughing at me, you know, oh my god, this is, um-but I managed to turn around, I walked back out, I'd kind of been disarmed internally in my head and the rest of it went well. But it was, and I was famous that night, you know, out in Cannes afterwards like on the, on the whatever the seafront. It's just like, rose everywhere and um, yeah, I was famous and infamous for my performance. I feel like you lived the worst nightmare that everybody has when they're thinking about giving a talk. And I think what's interesting is you survived. And I think that's a really interesting lesson. It's like you could freeze in front of thousands of people, walk off stage, and then it works out okay. 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 I ask like ask myself. I have this tiny doubt in the back of my head, like, it's never happened since but yeah you just I think you have to go with it with these things, you know like when life kind of throws you these whatever curveballs, you have got to kind of adapt. And 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. And so I think this is nice to hear that even when the worst possible thing basically happens, things can survive. You can turn it around. Yeah. A second area I wanted to hear from is your time at Google.

Insights from Google+ days (07:28)

And there's a couple of products you worked on at Google. Both of them were not what you'd call big successes. 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? Yeah, similar to the face to the kind of like, you know, walking off stage thing. You live and learn and I was at Google for four years, and now it's on Facebook for kind of two and a half years or so. And in both of those companies, this is at the height of the social, you know, the kind of social tech wave was like at its peak. Google was very afraid of the existential threat posed by Facebook. Facebook was very confident they could pull off some kind of like new social advertising unit that would be like AdWords or something like that that would, you know, destroy Google's revenue, eat them from the inside out. And so being there at the time was fascinating and moving between two companies. At Google, I worked on a lot of failed social projects like you mentioned: Google Buzz, Google+; I think a lot of the motivation for those projects came from a place of fear, 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, let's really, really try and create something wonderful. It came from a place of fear. And so during those times, I learned, I think, how not to lead in places. And by the way, I should say, you know, at the time in Google, there were other things happening that were amazing, like Google was building Google Maps, an incredible product, one of my favorite products, I think. One of the best products ever made. They were building Android. I was in the mobile team, in the mobile apps team at the time that Android came out. So an incredibly good product. So I just happened to be in the social side, which wasn't as good. And yeah, Google Buzz was kind of a privacy disaster and Google Plus, similar. And so, halfway through, I kind of published research about groups and how I'd done this. I've done a ton of research. An interesting kind of side note there is, at the time, I was being asked, I was working in the research in UX team as a researcher. I was being asked to do a lot of tactical research, like usability study type stuff, like can people use these products and 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, we'd 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. And the same pattern emerged every single time, which was people need way better ways to communicate with small groups of family and friends. And I kind of look back now and go, what's up? Or maybe iMessage if everyone's on Apple. But really obvious in hindsight, but at the time not obvious. And so we kind of tried to build a product around that called Google Plus, but again, it was kind of motivated from the wrong, came from the wrong place. And so halfway through the research that I'd kind of done, all this research had been made public through a conference talk and Facebook noticed and got in touch. One thing led to another and I left and joined Facebook, 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 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 everyone who worked in Google Plus was sent to a different building. That building had a different key card. If you didn't work in Google+, you could not get in. All sorts of countercultural things at the time. And as a result, there was a lot of antagonism internally for Google+. And so when I left in the middle of the project, leaving with all of the plans in my head to the enemy, some people saw me as a traitor, understandably. Other people thought I was enlightened, you know, to fancy you talked to. But it was, like, it was the right thing for me to do, but at the time, you know, it was a hard thing to do. I know there's also like a lot of scrutiny in what you took with you and the process. Yeah, when I left, Google kind of assumed that I was one of the spies. You know, I was quarantined when I told them I was leaving. They, you know, forensically analyzed my laptop, like all sorts of stuff like that. So it was pretty intense. You know, looking back, I can understand why that happened. 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. So one of the themes through the stories you just shared is, let's say failure.

Learning from failure (12:31)

I don't want to make it too harsh, but things are just not working out. I'm curious as a product leader, how important do you think it is for people to go through? Do you think it's almost a good thing? And I guess is there anything that you find helpful as a coach, mentor, someone for people who are trying to become basically you? It still is. You know, like I've personally failed so many times. There are two stories. And the Google one is like long, deep, deep tentacles. There are two stories. I failed a ton of times. At Intercom, I remember when I was at Facebook, I was very happy and Owen and I, knew Owen and Des, the co-founders of Intercom, and they were trying to persuade me to join Intercom. It was like a 10-person company at the time, but Owen said something to me at that time, which has stuck with me ever since. He said, "you know, at Facebook, you can design the product, but at Intercom, you can design the company." And that was extremely appealing to me, a great pitch. He said, "just design the company with us that you want to work in." Part of that was a company that embraces failure, that says it's okay to try things. I'm a big believer in big bets, higher risk, higher reward. I don't get as excited about incremental things. Now, having said that, there's of course a place for that too, especially as companies get bigger.

Intercom’s “ship fast, ship early, ship often” principle (13:56)

But I get excited about big bets. And if you make big bets, you're going to get a lot of it wrong. So a lot of the principles that we built here at Intercom are in building software. Like we have a principle called "ship to learn". And we've actually changed it since. It's over on the wall here. "Ship fast, ship early, ship often" is what it says now. You say "ship to learn". "Ship fast, ship early, ship often." It's like in that idea is the idea of failure. It's not going to go right. And it's going to go wrong more often than not. But if you ship early and fast and learn fast, you can change fast and you can improve fast. And that's kind of how we, that's the kind of culture that, that we, as much as possible, try to embrace and teach people, but it's much easier said than done. Yeah. Especially when you're in the moment, like, "God damn it, everything's going to fall apart. I really messed this one up." Yeah. And there's a trade-off with quality that people really struggle with. Like, you know, we've high standards of ourselves. A lot of Intercom comes from a kind of design founder background. We value the craft a lot. We never want to be embarrassed by what we ship. So there's a real tension there, a real trade-off where people have these high standards, which we encourage. And we encourage them to ship fast and learn and make mistakes. It's a constant kind of tension that we're navigating. Speaking of taking big bets and going all in, I know there's been a huge shift at Intercom to move towards AI and embrace AI.

Integrating AI into product strategy (15:17)

And so maybe just to start broadly, I'm curious, just what are some of your broader insights or surprises so far in how you've thought about AI and how you think AI will integrate into product and product strategy? Had a chat GPT launch November 29th, I think, last year. Ever since that day, I literally wake up every day thinking about AI pretty much. And I read as much as possible and still feel like I'm way behind in it. I think for me, like when I talk to people about AI, people typically fall into one of two camps. You're either all in, like really, truly all in. This is like a meteor coming towards you. Like this is, you know, bigger than mobile as a kind of technology shift 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'll shape society so I'm all in, I've gone over the hill or whatever, I'm over the other side and so there are people in that camp. And then I think there are people in another camp, which is, I've heard this before, it's hype. Like, you know, last year was crypto, you know, it was web three, like none of those things worked out. There was the metaverse, you know, so there's definitely, I think a lot of skepticism or maybe cynicism around it. And I can understand why, you know, the other things didn't really pan out. No, the metaverse is kind of really has one that might be coming back. But, and I kind of think about, um, other things didn't really pan out. No, the metaverse is kind of coming back. And I kind of think about, I'm trying to remember, there's a lot of the law where you have like, you know, the hype and then the trough of disillusionment and then 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 like the hype, there was so much hype. It was so noisy and still is a little bit so noisy that you kind of tune it out a little bit and some people have kind of fallen into that camp. I'm all in on the other camp like this is going to radically transform society and it kind of blows my mind even seeing new types of things that come out like chat GPT vision just came out recently and like just seeing the things that people can do with it and we're like just scratching the surface still so I'm we're all in for sure awesome I want to unpack that 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 understand to build to play around. What have you found and or what advice would you share to people that are just like I want to go deeper down this rabbit hole, I just don't know where to start because I have so much work to do already and this isn't like a side thing? The advice I have for people and the advice I have for myself, you know I'm in that too, like I wake up every.

Making time for AI learning (17:31)

Day by day, I receive too many emails and Slack chats, with people knocking on my door and at my desk. This is a challenge for me, and I have to make the time for it. There's just no other way. To me, this doesn't mean it's about priorities. It doesn't mean that you need to work crazy hours. I don't believe in working crazy hours. I don't track the hours I work. I don't work 50 hours a week, maybe beyond that you start to make bad decisions and get tired. And you need to live the rest of your life. You've got to put it into your day, whether that's setting aside dedicated time to read. Reading is the thing. You have to read, stay up to date, and play with things and try things. If you don't have access to GPT, if you don't have a pro license, 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. I got that for dinner last Friday night with my wife. 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 and can do all sorts of crazy stuff, like tell you how healthy the meal is or whatever. Anyway if you don't, you got to try it. You just got to try it. So my advice to people is you've got to try. You've got to set aside the time or it will pass you by. It does remind me of the mobile wave about a decade ago. Again, I was at Google at the time, working in the mobile team. It was my job to stay on top of things. At that time, some companies like Facebook went all in on it, maybe a bit late, but they eventually made the brave decision. I think if people don't explore AI properly, it will leave them behind. It reminds me of Facebook where Zuck and also at Airbnb, Brian did this, as he said any mocks you show me for new product designs have to be on a mobile app or on a mobile web. They can no longer be desktop for now. I think that's right. I guess do you think that that's the way to go?

AI in new-product development (19:37)

Approach this as a leader. Everything you bring me needs to have some AI component. That sounds probably not like a good idea, but is there something there you're thinking about or have done? Of just like convincing people this is where you want to spend your time? Yeah, it's harder for sure. It's harder because you want to force it. Yeah, a lot of the tech is invisible. You know, like a lot of the things, like we've a machine learning team, we've had in here for a long time, so we've been working in the space for quite some time. But it's funny, even if you go back like 18 months, I think if I was on your podcast 18 months ago and you said to me, "Hey, what do you think about AI?" I would have said something like, "It's not real. Machine learning is real. Let's talk about that." So things change and my perception of it has changed. But a lot of the improvements are kind of like behind the scenes. There were large language models or different types of things people are building in the background, like infrastructure. 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 like people need to start really thinking strategically. Like, I don't know, maybe it's just not a mock-up stage, but start to think really strategically about their product and whether it's in the line of the media or it's coming or not, you know, it's not everything is. And if so, for some, I think they require a kind of a foundational strategic change. Others, it might be less so, but I think that's actually the headspace that I think people need to be in. Can you unpack that further?

Questions to ask about your product (21:16)

What do you, what does that look like to really think deeply about whether your product is in the way of the media? You can get sidetracked by the technology for sure. And I do. I just mentioned, 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 the thing your product does. Like, what's the core premise behind it? Why do people use it? You know, what problem does it solve for them? That kind of thing. And then ask the question. So go back to basics. Okay, what is my product for? And why do people love it? And then ask, can AI do that? And for a lot, the answer 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 the types of things it, you know it can't do that at least not yet, and the types of things you know, so you're going to need to map like what your product does against what AI can do and like AI can do a lot. Like, uh, it can write, it can summarize, it can summarize text, it can write text, it can answer queries, it can find facts, it can scan text, it can scan images, it can 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, change my flight. Change my flight to Tuesday." It can do things like that. It can do a lot of things. It can build rules. I think any product that has any kind of workflow in it, which is almost all B2B SaaS products, any product that has multimedia in it, they're in the media line or whatever. I don't know if this metaphor is working. The media is coming and they're in the far they're in the meteor line or whatever. I don't know if this metaphor is working, but like yeah, the meteor is coming and they're like in its path and so for a lot of these products that you just need to look at what AI can do and then for some of it, it'll be like kind of replacement, AI will replace, it'll just do it and you know in other places it'll be augmentation, it'll augment, it'll help people um as a co-pilot, ideas that are going around. But yeah, I think that you've 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?".

How Intercom pivoted after the release of ChatGPT (23:33)

This date was kind of, I think it was November 29th, etched in our heads. We have Fergal, who was our head of machine learning, and Fergal just turns around that day and he's like, 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." I actually often talk about people, there's a little framework I have, like before-after moments. This is a before, this is the before-after. You know, like I actually often talk about people, this is a framework I have like before-after moments. This is a before-after moment. It was before and that is after and like everything has changed. So we literally ripped up our strategy almost entirely and started again, like from first principles and said, "Okay, why do people use Intercom?" You know, Intercom is a customer support product. And then very soon after that, Sam Altman, who's the founder and head of OpenAI said, "Hey, one of the first industries that's going to be disrupted is customer service." Right? Yep. So we did. We totally changed how we think, how we work. And we just went kind of heads down and built a product called Finn. We built other things first. Actually, Finn came later. I think about it. But we just went kind of heads down and built a product called Finn. We built other things first. Actually, Finn came later. I don't know why 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. So we've done it. I think other companies like Google with Bard have to do it, you know. And maybe they're a little bit slow, but it's so early in this tech cycle that I think they're fine. So you know, yeah, we just have to, we did. It was hard, but we had to do it.

Intercom’s AI chatbot, Fin (25:13)

You share briefly what Finn is just for folks that aren't familiar. Finn is, first and foremost, an AI chatbot. So if you think about customer service, you know, people have questions for a business and historically, that was mostly email and phone and mostly ticketing based. So you'd file a ticket, you know, a lot of "do not reply" emails and so on. And then came along conversational customer support, which is just basic messaging like WhatsApp or iMessage, as I mentioned earlier. Now there's bot-first experiences and Finn is an AI chatbot. AI-first, chatbot-first. So the first line of defense for a customer support team is Finn, not a person. And so it fundamentally changes and Finn can do. The results we've seen with Finn are like mind-blowing. Our biggest challenge is actually trying to help customer support teams think about organizational change. You know, it's not like the tech is way ahead. It's actually like people wrapping their heads around what this means for the role, the teams. 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, that's what Finn is. Finn has expanded. So FIN is now also in our Intergram inbox, the place that people answer queries, customer support queries. And now FIN's in there too, helping the support reps, suggesting answers for them to use or helping them rephrase things. So it's now augmenting people as well as answering questions by itself.

The early impact of AI adoption at Intercom (26:45)

I think you're one of the few companies that has pivoted fully into AI. And I think there's a lot of lessons here about how team structures might change, product strategy, priorities, things like that. So I'm curious just to unpack a couple more things here. First of all, what kind of impact have you seen after going all in and going in this direction? It's very early, honestly, to be able to answer that properly. And it depends on what you measure as success. So again, there's a lot of hype and buzz with AI. So if you're measuring it by interest, it's a huge success. A lot of people, our target customer is customer support, our customer support manager leader, and so they're like very curious. They're like, does it actually work? There's a little bit again back to the earlier thing of like there's so much hype, there's a bit of skepticism around it. Does it actually work? Is it as good as a person? Hey, and you know like in customer support, people who tend to work in that role are typically very high empathy, care a lot about people. And so they're like, but is it as good as a person? Like, is it nice, friendly? Like, does it understand humanity? You know? And so there's a lot of curiosity and a lot of interest and a lot of people trying it. We have some customers who are hugely successful with it. They can answer up to 50, 60, 70% of their inbound questions with Finn. So like we've some customers who see huge success but it's early, you know, and so like has it transformed our business like financially not yet, you know, it's not like this kind of, you know, oh, I think all fast-growing startups, you know if you think of intercom as or like AI intercom as I guess a new startup, even though we're 900 people, you know, the kind of growth curve you're looking for is kind of an exponential curve, as opposed to like big public company kind of linear growth curve with the exponential where it takes a while, you know, the first kind of year, two years is the bottom of that. And so I think we're still we're still in the like trying to figure out exactly what's going on, trying to talk to, educate people. But we have enough evidence to believe it's the future for sure. Are there any examples of either this product or other instances of AI just kind of blowing your mind where you're just like, wow, I never imagined it would be this good?

Capabilities of AI (28:53)

I kind of go back to that before-after thing. So, ChatGPT, the first version of ChatGPT was a before-after where we had built, never imagined it would be this good. I kind of go back to that like before-after thing. So, ChatGPT, the first version of ChatGPT was a before-after where we had built, like we've been working, like I said, in this space, we've had a machine learning team for a long time. The way our machine learning thing worked before ChatGPT was, you know, there's not a manual setup, like a customer support manager would have to orchestrate the bot and teach it what to say. Just a lot of orchestration, a lot of teaching it, 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 nearly for all these basic things. So that blew my mind. And then that was just, "Oh, I can answer questions." But then you're like, "I can reason." There's actually like a debate about whether it's reasoning or deduction, or, you know, but it can like work things out. And I'm not one for going down into these really philosophical things. Like I'm like, "We just need to build it. Let's go back, build the product or whatever." But it can work things out. And that blew my mind. And like we found it a whole bunch of stuff. We found ChatGPT and other companies too, like we played with other LLMs like Entropic and so on. It can work things out. And that was kind of mind-blowing. Then you can see it doing things like writing code. And I was like, "Wow, it's really good at writing code." What does that mean? You kind of, and then you start thinking, like here at Intercom, we have a kind of a one to five ratio. So like a PM has about five engineers on a team and you're looking at this thing writing code and you're like, "What happens next? You know, like do we need as many engineers or will their role change and they'll start doing different types of things like reviewing code instead of writing code?" So that kind of blew my mind. And then the visual stuff, like I mentioned earlier, I think the visual thing was bigger than the original one. It can parse imagery and it can help you see the world. You take a photo of your bike and say, "Hey, what's wrong?" And it'll tell you what's wrong, how to fix it. You can be traveling, take photos of stuff. It's in a different language. It's etched in stone on a 12th-century cathedral, you're like, "What does that say?" And it will tell you what it says. It's just like, "I don't know how to do that, you know, this is one I'm actually repeating most people these days, um, here in Ireland, if you want to be a radiologist, you know, so like study x-rays and tell people what's wrong, it's seven years training to learn that skill. Seven years to be a radiologist, and then you're kind of just into the job. AI, it seems, is already better at it. So it's already better at it, and it can ingest every x-ray ever made like no human can ever read and think about and synthesize every x-ray ever made. So, of course, it's better. And then you're like, "Okay, what happens now?" I guess the whole job changes, you know, radiologists will not take x-ray. Well, I guess it might take them, but they won't analyze them for sure. They'll look at what AI says, check that it's right. And then it's like kind of 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 here in Ireland, we have long queues for hospitals, epic waiting lists for people getting X-rays. So like this is a really good thing possibly for people. Here's the craziest one I have. AI can listen to your voice and copy it so it can say things and it sounds exactly like you and it's really, really good like almost indistinguishable we're like that sounds like Paul. And so imagine that the metaverse earlier, I don't know if you saw Zuck talks to Lex Friedman, see that, yeah, so that was my first like, "Oh, like so it's the metaverse if we haven't seen it they met in the metaverse I think or some virtual world." Yeah, it's like a they love black room. And they love black room. 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. Based on the trajectory of that technology, it's going to get better. And so the voice thing and the face thing means both of those things are almost indistinguishable from a real person. And AI will be able to ingest all the things people say and do. And when people die, it'll be able to replicate that person. And so there's an afterlife. Hey, your parent dies, and you can still talk to them. And that could be the weirdest thing. Maybe it's not good for people. I don't know. But that is like just around the corner, you know, and the AI can, like that's kind of like my inner questions mind it's mind-blowing there's actually a black mirror episode with that same premise where that's right, yeah, and I don't think it ended well. So no, I like, careful for sure, for sure, yeah, it is like the, I think for ended well. So I like, careful. For sure. For sure. Yeah, it is like the, I think for an IRD report and like the voice translation thing is another one. I can't remember. Maybe it's in Mission Impossible, where it can take a voice, translate it, and translate it in real time. So, you know, and this tech is like, again, just here where like if I was a native Spanish speaker and couldn't speak English, you and I could still have this podcast. You know, it's been your voice to be translated in Spanish in real time for me. It's like, again, mind-blowing. We're actually working on dubbing/slash translating podcast episodes, which is all done through AI where it figures out what you're saying, makes it Spanish, 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, that's cool.

Implementing Ai Brings Conviction And Consensus

How to structure teams around AI products (34:27)

You mentioned that your Eng team might change. You're thinking because AI can make them much more efficient and work differently. I'm curious what you've seen actually change on your team, either using AI-ish tools or just building AI products. What do you think is most different? And I'm curious from the perspective of a team that's trying to think about integrating AI and starting to lean into AI, what have you seen most change and should change? Ultimately, you need really great machine learning engineers. That's where it starts. And if you don't have that, then you've got to find a heart to build truly, really, truly great things. So what OpenAI provides, and what Anthropic provides and Cloud, they provide amazing technology, 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 built for customer support. Maybe someday we'll need to go build our own LLM that's just for customer support. Maybe, I don't know where that will all go. And maybe everyone will have their own LLM for every single business. I don't really know, to be honest. Maybe these companies will provide specialized LLMs. But anyway, that's kind of the first thing. 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's way bigger than 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, where, you know, we have Finn and Finn is kind of working. We've built the underlying technology. Now it's a question of like, you know, if you have a human support person answering questions in the inbox, that's like a natural chat kind of conversational interface, pretty straightforward. What happens when there's now like an AI assistant in there? How do they talk and what do they do? And when do they interject? And how do you represent that in the user experience that feels natural? So that's a really hard design problem. So that's saying you're kind of back into like, okay, we have a product team that's like a product manager, a product designer, you know, maybe three, four, maybe five engineers. And they're getting help from the machine learning team. So like we now have both setups and increasingly we can do more with the latter, you know, more teams who can build on the foundational technology that we've been building over the last kind of 12 months or so. So that's kind of one thing. I think a second thing that comes to mind is not to think about it as bolted on, you know, I think some people are still in that camp. Like, again, I go back to the mobile thing. There's just so many direct parallels with it. Like I said earlier, at Google, I worked in the mobile apps team. I worked on mobile Gmail, 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's like it was like you're 20 people we're 200 no one uses this stuff on a phone no and again a lot of skepticism no one's gonna write docs on the phone seriously you're gonna write a document they're gonna write a full document on the phone are you crazy you know so um so don't do that you know we're trying not to do that like don't bolt it on don't be like i would have a bunch of ai people and we do have some specialists but generally speaking we're trying to like have everyone learn about it interesting so i'm curious just specifically what that looks like don't bolt it on the idea there is don't just have like a side that's like, they're the AI team. They're going to add AI to all this stuff.

Why all teams should be involved in AI (37:57)

You're finding that the lesson is integrated into every product team. Yeah, and we're still early there. We're still early. So what we're trying not to do is have the kind of AI inbox team, and they're the only people who work on AI features in the inbox. I think it's much better to have everyone learn about it. By the way, I'm a big believer in generalists. Like a big, big believer in, I mean, I guess my background is like, you know, jack of all trades, master of none. That's probably how I describe myself. I've worked as a researcher, designer, PM. So I believe in generalists. And I believe in setting teams up that way. And yes, specialism matters at times. Machine learning, for sure, is a deep specialism. And at Intercom, we generally, in engineering too, much prefer people who learn new things, whether it's like a new coding language or framework or how to design AI interfaces or whatever. Get more people being able to do it.

Staying up to date on emerging technology (39:04)

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 get big here," or they're already working on it. I'm curious if there are other, maybe pitfalls you ran into that you think people should try to avoid and something you could share there, or just like any other lessons about making this transition that you think might be useful to other people. Yeah, what I've mentioned so far, don't bolt it on. Um, don't keep, I stay up to date. You know, like I mentioned, like read. I feel like I'm behind all the time. This is moving so fast. What are you reading? What do you find is most interesting and informative for reading about what's happening in AI? I'd love to tell you that it's incredibly structured and, you know, I have a great reading list that I get every Sunday morning. It's pretty random. I'm on Twitter, which is now called Xcourse, a lot. I follow some people on Twitter. 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. So I do that, and I kind of do it deliberately to try and generate more stuff. I'll search Twitter as well because there's cool stuff there. There are 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. The blogs of companies too, like OpenAI, have a pretty good blog and they write papers and summarize them. Cool. If there are any other ones you think of, either people on Twitter to follow or newsletters, email me after and then we'll add them to the show notes. Yeah, perfect. 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 them. Like everyone, it can be so busy, busy, so many distractions you just gotta have to set aside time. Are there any other tools or apps that you find really helpful? Sounds like ChatGPT is kind of the center of how you play around with it. Is there anything else that you find really interesting? I'll try other things like Bard, you know, for example, Google Bard is Google's kind of AI search engine. Rewind is another fascinating company. I think it's rewind.ai. Rewind is basically augmented AI for your memory. So you install it on your local machine, and it captures everything and remembers everything. It's all local, so there's no privacy issues. And you've got to try these things to understand whether it's any good or useful or where's the boundaries and how does it work and so on. So I'm a believer in that type of thing. This episode is brought to you by Helpbar by Chameleon, the free in-app universal search solution built for SaaS. Your help content lives outside your app and is scattered in many places, forcing users to waste time hunting for answers. Helpbar solves this. It delivers answers directly inside your app and eliminates context switching. Users can search or ask questions to get AI-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 HelpBar 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. 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/lenny. That's helpbar.ai/lenny. When you started rolling out AI and kind of leaning into this direction, did you run into any big challenges or hurdles organizationally or personal interests or opinions?

Hurdles implementing AI at Intercom (42:44)

I don't know. Was there anything you ran into that was a big stumbling block and something you had to get over? Like any company, Intercom is full of diverse opinions about things. And I think with AI, I'm all in. I'm all in. I'm leaning forward. The media is coming. I'm sold. I'm way past that point. Also, no one knows. No one knows. And so a lot of the time when we talk internally, the strong buy-in from, you know, Owen, you know, co-founder and CEO Des, you know, co-founder like me, like a lot of the senior leadership team, are like we're in the all-in camp and so that helps a lot of course if your senior leadership team of the company or like all in, of course then it kind of trickles down but equally, like, you know, people sometimes ask some of the kind of hurdles of being like, "hey, you know, why you all in now?" I'm like, "uh, an educated guess, a hunch, you know, a lot of it's like the kind of the part of like business strategy and product strategy that you just it's just hard it's just kind of it's like taste, you know, people talk about taste like product taste who has product taste and a lot of it is like, it's judgment based on experience. That's all I can say. Like, I don't know, for me personally, I don't know. I lived through the mobile thing pretty closely, having worked at Google on mobile. I lived through that phase, 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. But it's a challenge for people, some people, because they don't have the context or they disagree with it. You know, we have a lot of debate here about the future, you know, Fergal I mentioned earlier, gave myself and a few other people, a few other product leaders and he gave us like, I don't know, was it a pitch or what? I don't know about how maybe, all pitch, or what, a play? I don't know, about how maybe all of our roadmap with AI is wrong. Maybe we're like kind of, I don't know if you think, are familiar with the horizons framework, like horizon one, two, and three. I don't know where that comes from. Yeah, so like horizon one is like kind of the medium, short to medium term, like next 12 months, 12 to 18 months. Horizon two being like, "hey, what's happening, whatever, 18 to 36 months out, or I think people use different timeframes, different horizons." Anyway, we're like in horizon one land. We're like, "yeah, and then next year we're going to do this." And he's like, "yeah, but two years from now, if this path, you know, plays out, everything we're doing now is like going to be irrelevant and like useless." And you're like, "oh, OK." And so those discussions happen. And the level of ambiguity is off the charts. So a lot of the challenges have been navigating that ambiguity and helping people get the conviction I have, you know, without kind of drowning out voices of like alternative voices and opinions, which are often valid too. What does help people get that conviction?

Building conviction around AI (45:52)

Is it just showing them examples of "Here's something, wow, look at this thing, this is unreal." And 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 markets. Yeah, that's true. For sure. That's true. Yeah. Showing people is definitely like the easiest way. I think it's the customer support is definitely that. You know, like I said earlier, Sam Altman's like, number one, customer support. So you're like, "Okay. Adapt. Yeah, adapt or die is adapt, adapt, adapt or die." It's kind of our mantra, "Adapt or die." I think that there are other industries where they're on the same journey. It's just not as obvious. So for example, reporting software, you know, Tableau or, you know, any kind of reporting product, you know, how do they work? Well, they're like the typical kind of like read-write app, build dashboards, filtering, querying, kind of hardcore querying, kind of query a database, get some numbers, show it in a UI. A lot of thought and care goes into like how you present that data to people, the different types of charts that are appropriate, help people make good decisions ultimately. I think, again, this is like hand-wavy. Who knows? Appropriate, help people make good decisions ultimately. I think, again, this is hand-wavy. Who knows? Maybe that's all done, dead now. And the reporting product of the future is just a box. And the box just goes to the database. And the box is just, "What was our best sales month last year? January. Okay. Who was our top performing rep in January? You know, Lenny." Like the reporting products of the future might look like that. And so project management tools is another one. It was a bunch of products that I think are just outside the most obvious customer support one and yet equally ripe for a newcomer to come with a completely different paradigm and potentially take over. I like that this connects back to your very first point about trying to think about where AI integrates us. Think about what problem are you solving as a company? For example, Tableau, helping people visualize data. And then the question is, can AI just do this for you? And in that case, "Oh, maybe it can." And that gives you basically a whole strategy of like, "Okay, how do we actually do that with AI?" 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 you know if you're like a Tableau type company you've tons of designers who design dashboards and filters and querying type like workflow like what do they do the UI is the box you know so 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. Maybe one last question here for team members learning and starting to work within this realm is there anything you find helpful to get them ramped up other than the advice you've already shared which is just read a lot of stuff watch Twitter/ "X" subscribe to these newsletters and then just try it? I also try and read things that say like it's all a load of crap, you know? So like, it's very easy, I've been guilty this many times, back to like mistakes you've made, like I've been guilty of this many times where like I've jumped on a bandwagon and uh it was all wrong and like the, the older I get like the web3 thing I'm like "I don't even know what three is crypto I never I never bought crypto maybe I'm wrong about that but I'm not a bandwagon jumper you know" and I but I kind of maybe might have been when I was earlier so like and I try these days to read the alternative opinion um people who are skeptical or or think it's bad, you know, a lot of people think this is terrible for humanity this technology is gonna eat us alive, you know I try and I try and like balance my optimism i'm kind of a delusionally optimistic thinker so i try and balance that with negativity i guess that's really good advice yeah is there anything else in this realm that you think might be useful to share before we shift to a different topic oh yeah the other thing is don't be afraid uh maybe um I think people are a bit afraid of it. And like, for example, if I started walking around our office here saying, "Hey, I think we need two engineers per team going forward."

Why you shouldn’t fear AI (49:52)

That's probably not really a good idea to do that. You know, and I think, in reality, that's not going to be how it plays out. Like there's all sorts of, you know, loads of great studies over the years about how people don't end up losing jobs. The jobs get moved around. And also, you know, for customer support, for example, it's a high attrition job. So people say, like, hey, everyone's going to lose their job. A bot's going to take over. It's like, maybe some of that will happen, but probably to attrition. As in, like, people, someone quit and just didn't get backfilled. So, you know, the doomsday scenarios, I don't think will play out as much, but for sure, like, you know, it's easy to kind of be afraid of it. And I think you kind of have to lean into it. I love that. Okay, I want to chat about frameworks.

Paul'S Business Insights And Frameworks

Paul’s “before-after” framework (50:56)

You have a lot of interesting frameworks that you've put out there. So maybe we do kind of a rapid-fire through a number of frameworks that you've worked with and find useful. And the first thing, you actually mentioned this before and after, which I hadn't heard about. What's the general idea of that concept? Before after is literally that simple, I think. Like we have a rebrand at the moment happening, and that'll be a before-after moment. We're redesigning our pricing. And then the day that pricing goes live, that'll be a before-after, 'cause it was like, nothing's the same. And so we need to go back out and talk to people again. Like I'm a big believer in talking. You have to talk to customers. It's the only way you got to talk, talk, talk, learn, learn, learn. Don't take what they say at face value, go deeper. And so, you know, a lot of these before-after moments, once you've passed into the after, you've got to start learning, were we right? Were we wrong? What happened? What do people think?

Pricing lessons from Intercom (51:54)

Can you talk more about this pricing learning/mistake you shared? What do you think you did wrong? What happened there? You know, we had a principle called align price to value. By the way, I think pricing is incredibly difficult. A lot of the design team who work in pricing here, I say to them, it's one of the hardest design problems I know. And onboarding is another one. Onboarding people into a product is also. People are like, "Hey, you just designed a few steps, and it's pretty easy. People follow the steps." Again, deceptively difficult to design great onboarding. So I think pricing is deceptively difficult. We had a principle around aligning price to value. People should pay based on the amount of value they get in the product. Easy to say and incredibly hard to do. Value is subjective. The price is, for some person, they get like 10 units of value. I think that's about $5. Someone else is like, "I'd pay you $5,000 for those 10 units of value." The biggest mistake was a lot of mistakes compounded. This is an area where I think we were risk averse. We've ended up with too many pricing models. We've built on top of old competitive mistakes. And it took a brave decision to say, we're going to start again. Well, this feels like it could be this whole episode, just talking through your pricing lessons and journey. 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 experience the number one thing I would say is keep it simple, keep it simple it's so tempting to, like with us, for example, a lot of SaaS products, you know, have add-ons where like hey, we built X and that's like 10 bucks or a hundred thousand, depending on what kind of product you're selling. We built X and that's the price of X. Hey, we've just built Y. Y is awesome. And it's a new thing you can do. And it unlocks all these new capabilities. People shouldn't get that for free because it's a new thing they didn't have. So let's charge more for Y. What that doesn't really work with the other. Okay, let's look at an add-on. Oh yeah, cool, people just add on but then later now you've got people, who have the add-on and people who don't and then you're like and you add another thing and so like tiering we've added tiers we've cut, with products, tiers, add-ons, tiering in the add-on, you know, people can't understand their bill. So my advice is keep it simple, reject, fight so hard to not, to resist the temptation to add extra ways in which you price. Amazing. I didn't think about going into this topic, but I'm glad that we touched on it. I was thinking, let's talk about scars for life earlier. That's another scar for life. All right.

Paul’s “differentiation vs. table stakes” framework (54:54)

Let's keep talking about some frameworks. Another that I found that I loved is something that you call differentiation versus table stakes. What's that about? It's kind of like the Kano model. People are familiar with that. But it's very simple. It's kind of like the Kano model people familiar with that, but it's very simple. It's kind of like I guess we took the Kano model and tried to make a really crazy simple version of it again. Like, I'm a little bit allergic to things like this. I can't even hate myself for bringing up the Kano model. I'm allergic to like people over-intellectualizing frameworks and like, you know, "Oh well if you've seen the new different law of whatever." I'm like 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. Um, and so difference between table stakes very simple. I think people who adopt a product or buy a product or switch to a product there's kind of two driving forces. One is the attraction of the new solution. And that's basically differentiation. So what's different and better? But critically, what's different and better in ways that customers care about? Again, back to all the failed projects, my lesson from a lot of these was we were different and better in these Google projects in ways people didn't care about. You know, like all sorts of Google projects, like Google Wave was an amazingly innovative product that no one really cared about. So 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 good. I want to check that out. That looks better than what I have today." But on the other side, there's like a kind of entry requirement or like table stakes you know to play the game you gotta have a certain amount of things and so they're table stake features they're often very boring, you know, they're like real basic stuff boring stuff and easy to ignore and easy to not build and again a mistake with Intercom maybe over the years is that we were much more attracted to the differentiation and built a lot of that. So we went through different iterations of our roadmap, sometimes like changing over the course of a year or two, where we were like, "Oh, the differentiation to realize that everyone loved it and really wanted to buy, 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." And then the roadmap was built based on those, like trading off whether we need more differentiation or trading off whether we need to invest more table stakes. And so these days, the place of Intercom today is like we're kind of 50-50 probably in terms of resources, but it has swung 70-30 in both directions at times the last piece about it is I think it's really powerful to like look at a road map or look at a proposed road map and ask yourself which of these two things matters more to us not to us actually to our customers right now the other thing that we've talked a lot about here internally is if you're a startup and you're entering some kind of established category, customer support for us, big established category, massive, a lot of table stakes, built up over years, decades, ServiceNow, Service Cloud, Salesforce, Zendesk, like decades of table stake feature building. So to play the game, you need a lot of the table stakes unless you have incredible differentiation. So for the early years of Intercom, people would just buy us alongside Service Cloud or Zendesk. They just buy us alongside. They're like, this Intercom thing, we were messenger first, modern messaging, a modern UX. They were like, we want that for our customers, alongside the big giant bag of table stakes, Cause then she doesn't have any of those. Then over the years, we've built the table stakes to a point where, okay, now we can fully play the game and we can like, people can switch so they can swap Zendesk for intercom. But it took us years to get there, you know, and, and then hence kind of, if you're a startup, you need to invest a lot more in differentiation. And then over the years, I think you start to balance the books a bit. I think what's interesting about this is one, it just gives you a way to think about looking at your roadmap. How much are we actually doing? And are we doing too much table stakes? Are we doing too much differentiation? So it gives you kind of an awareness of what's happening. And I think there's also interesting, it's an interesting strategy as a startup. Like do we spend years doing table stakes and then launch? Or does it go the way Intercom went? Differentiate first, we'll build everything else later. I wonder when it makes sense to go one or the other. Yeah, and it probably depends on the market. Different categories and all sorts of things. Yeah. Awesome. Okay.

What “swinging the pendulum” means and examples from Intercom (59:22)

The next framework is something you call swinging the pendulum. What is that about? I actually kind of mentioned that in an example a bit earlier. Like the differentiation in table stakes with swinging the pendulum. So swinging the pendulum means you take a step back from everyday work life, and you kind of make the observation that something's in an undesirable state. So like, maybe it's "whoa, 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" that's like undesirable. Or "oh, we've now built all these table stakes and we've not been investing in differentiation and actually we're not that attractive to people because switching product is like a pain and we're just not attractive to people—we need to like, okay, so this pain." And we're just not attractive to people. We need to like, okay, so this undesirable state. And then so you go and fix it. But the temptation is that you overcorrect. And we've done this so many times in so many domains. Everything from, "OK, we don't have enough differentiation." A year later, "oh, wait a minute. Like, we're missing all the table stakes." Okay, we're over there. So product building is one. People is another one. Building our teams and people. Another big one was maybe five years into Intercom, we were on this high-growth trajectory, really good classic startup before our pricing problems. And we looked around and said, none of us have done this before. I don't think that's good, an undesirable state. "Do we even know what we're doing? Like we're just a bunch of random people. Do we know what we're doing? We need to hire some experts. We need to hire some experts. Like, you know, if we're going to go upmarket, we need to upmarket people. We need to hire some experts." Like, if we're going to go upmarket, we need to upmarket people who've done it before. So that was like undesirable state. Fix it by hiring people who've done it before. And then we hired loads of people who've done it before. And what they did was brought the culture and ways of working of their prior company to Intercom. And so we totally overcorrected. Didn't work out in a lot of cases. In most cases, it didn't work out—for in a lot of cases, in most cases didn't work out because we weren't we weren't trying to be a bigger company that already exists we're trying to be us, you know? So, hiring and building teams is another where we really overcorrected to find out like, okay it's a balance here related to that one. Really hiring one is like generalists and specialists, kind of a similar theme. People have done it before or people who are specialized. And we hired a bunch of specialists only to realize that they're not adaptable. And in Intercom, we believe in kind of, we have a lot of ambiguity and we lean into the ambiguity and people who are highly specialized can thrive in big companies, really thrive—they're invaluable employees. But in a fluid startup culture with a lot of ambiguity, they can really drown, really struggle. Maybe the middle of this pendulum kind of landing in the middle is "let's hire someone who has done a bit of it and have a bit of specialism, not much but enough to try and figure it out." You know? So we hire a lot of those kind of people today. First of all, I love all these stories of things that didn't work out, because a lot of people don't like sharing these. And this is what people want to hear, is like here's not everything was perfect, here's a lot of mistakes that are made along the way. And feels like this framework as a result of just doing this too many times is the main lesson here: generally avoid swinging the pendulum too far 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. Is there kind of a, I guess, yeah, what do you think of when I say that? In talking to people about this before, sometimes the conclusion of the conversation is something like, "it's the only way to do it." Like you actually can't do it a different way. And so maybe the question is really like, how high up, how high does the pendulum go? Versus like, you gotta swing it, and then it's how far do you swing it? And for sure, you're right. With AI, we are like, we're swinging it pretty high. Maybe I overestimated earlier. If AI is in the differentiation camp, to kind of mix the frameworks, we're still building a lot of table stakes features too, like building depth into the product. And that's 50-50. I think I mentioned 50-50 earlier. So that's, so we're not, we're not totally swinging it, we're not like, you know, it's swung but, uh, we're also kind of doing the other thing and balancing things out so I, I think you probably have to swing it. It reminds me to know where the boundary is, is what I was going to say. It reminds me of a story like back to the olden days stories. I remember when I went, I remember at Google, privacy was really top of mind, to the point that it would block decisions, like block product progress. Just privacy, circular conversations, so many circular conversations. And nothing ever got built or shipped. I worked on a project for a year at Google, and we shipped nothing in the year, just circular conversations, which killed me at the time. So when I went to Facebook, I realized they have a different approach to privacy. And again, I'm not advocating this is necessarily good. It certainly didn't help their brand, but there was kind of an idea that to know where the boundary is, you got to cross it. And crossing it's painful, but if you don't cross it, you'll never know. So if you go, if you think you're going up to the boundary and you stop before it turns out, it's actually miles over there, you know? So I think with a lot of this stuff, you, you know, you don't really have a choice. You got to kind of cross the boundary, feel the pain, be humble enough to realize you didn't get it right. And, you know, kind of go again or whatever the right course, corrective courses. Yeah. Get that pendulum off the, off the even like pivot thing that it's on. And then, oh, and then let's fix that pendulum. Let's put it back. Yeah. Okay. Another framework that I read about briefly, and I love the general idea of it already, which is something that I think you call product-market story fit.

Paul’s “product market story fit” framework (01:05:21)

What is that? So yeah, with product-market fit, pretty basic, well-understood, very important. The way I describe product-market fit is you've got to build the right product for the right market. 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. But for me, it's very simple. The market is the people, the problems they have, and how important the problems are to them. To have a good market, you need a lot of people with the same problem, and they need to care a lot about it. Going back to the Google social stuff, we found a lot of people with the same problem, but they didn't really care. 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. That's the market. So who the product is to what. I just, I don't know, in my career again, so a bunch of products that were built, there were good products in good markets and they failed and I couldn't work it out. And eventually I came back to this idea that like, and maybe someone might say, "Paul, that's marketing. You're talking about marketing," but like story, the story's wrong or the story is missing. And so sometimes it would be a great product in a great market explained in a convoluted way. Like that, I see that a lot. I used to see that a lot at Google, again, just explained in a very complicated way, over-intellectualized. And as a result, people are like, "What are you talking about?" You know, you don't get their attention. And so the story is really important, as important, and actually sometimes you'll see like, not great products, certainly worse on paper. Try to remember, like the Spotify competitor back in the day, people were like, "What was the name of it? Ardio?" Yeah, Rdio. Rdio was one of these where like, yeah, people like great, like people, all I've ever heard of Rdio was amazing product. It's failed, you know, and why did it fail? Spotify and rdio at the same market, they were solving the same set of problems. Rdio 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. But you know the story they've got the story wrong. And so again, I think all product people, whether you're a designer, a product manager, people in research, data science, need to think about the story all the time. Work with marketing, work with product marketing, and learn about how to explain the product as much as how to build the product. Makes me think about positioning and how important that is. And we had April Dunford on the podcast very recently. Oh, yeah, yeah, yeah, she's excellent. Yeah, it is really that, like, why are you better? You know, and can you explain why you're better? Such an important point. A final area I wanted to touch on is jobs to be done.

His take on JTBD (01:08:23)

So we had the co-creator of Jobs-to-be-Done on the podcast. We had Sridhar Krishnan on the podcast. They very much disagree about how effective Jobs-to-be-Done is. I know you guys are big on Jobs-to-be-Done. So what are your general thoughts on the Jobs-to-be-Done framework? How effective was it for you all? How do you use it? What do you find work doesn't work? Whatever comes up. Yeah. I'll be totally honest at the risk of offending people if they listen. Like we worked with Bob West, you know, who's eight years ago. I think Bob's the right guy. And we kind of followed that model of Jobs-to-be-Done more than the ODI, I think is the other school of thought. Anyway, I'll try and say this in a simple way. We found Jobs-to-be-Done to be really good. Very, very useful. But in a very simple way, and going back to this idea of simple frameworks, in a simple way, kind of separately, there's like so many people who spend so much of their energy debating the nuances and peculiarities of one version. Who cares? No one cares. Oh, well, I don't care. They care, obviously. But I'm like, your customers don't care. People you're trying to build a product for don't care. No one cares. That's a cool intellectual debate, but for me, maybe this is too extreme, it doesn't really have any place in work, you know, like in the work we do. We're just trying to build a great product. And so for us with Jobs-to-be-Done, it was a really good way of us centering on the customer problem, like focusing on like not getting distracted, basing it in research, like good, solid, research-informed insight that told us, like the thing people were trying to do, like what is the thing people are trying to do again energy do they have a lot of energy around it maybe the energy thing might have come from talking to Bob. Actually, now I think about it, I think it did, actually. I think, like the idea of, like this idea that, you know, 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. You know, like it's very easy to see if someone's apathetic versus, like, into it. So we've had a pretty good, and we invented this job stories thing kind of by accident. I can't remember exactly what happened, but like, I wrote out this way of writing a job story, basically. Well, we didn't call it a job story. Someone else called it that. We just, at the time we're like, there was, I can't even remember, you know, there's like a trigger in an act. Anyway, we didn't even give it the thing a name. Someone else named it, I think, and I'm just like, we're just trying to build a great product, you know? So like we found it really good in that way, really simple and then the other one that we use a lot still here is the four forces, which is just a framework out of Jobs-we've-done. The four forces being, like different for people, there's different forces when people try and switch product.

Intercom'S Use Of The Four Forces Framework

How Intercom uses the “four forces” framework (01:11:01)

And some of it's the differentiation table stakes stuff, like the attraction of the new solution, the reasons that you might not adopt it. Habits people have anxieties. Like here's another kind of funny story to tell you how much the four forces is really good. Here's a funny story. I was saying earlier that Owen and Dad were trying to convince me to leave Facebook, which I loved at the time, and to join. They wrote out the four forces for me to join and then secretly, over a few beers, talked to me and fed me my anxieties. And like, you know, basically worked me on the four forces. I was like, that is genius. Maybe it's a bit, you know, but it's ingenious. And so it's just the four forces is incredibly good at helping understand why people make decisions. I love that a lot of your advice just continues to come back to keep it simple, cut away anything that isn't necessary. 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 I think there's this like endless set of processes and ways of optimizing that gets people distracted and often just kind of slows everything down. Yeah, yeah. I mean, and it's interesting and fun to talk about sometimes, like really fascinating, you know, but unless you're an academic, but if you're working in a company that you're trying to build a software product for people to improve their lives in some small, meaningful way, like it doesn't matter. You know, just use the thing that helps you do that. That's the goal and use the thing that helps you do that. And that's it. With that, we've reached our very exciting lightning round. Are you ready? I'm ready. What are two or three books that you've recommended most to other people? 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."

Final Q&A Sessions

Lightning round (01:12:54)

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 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 by Ray Dalio. 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 that thick. What is a favorite recent movie or TV show that you've really enjoyed? Most recent is The Bear, which I came to late. The reason I actually love the show is because I think it somewhat celebrates the grind. And I think that's important. I worked in coffee shops a lot when I was younger, when I put myself through college and stuff. And like the grind is part of life. The grind is a necessity to get things done and make great things happen sometimes. And I liked that about it. I really liked that about it. Part of life and part of the grind is a necessity to get things done and make great things happen sometimes. And I liked that about it. I really liked that about it. What is a favorite interview question you'd like to ask candidates? Yeah, I'll give you a slightly different answer. I don't really have set in a few questions for candidates. I don't like, I don't like, I'll ask a question adversely. I don't like questions that rely on memory. You know, a lot of like, tell me what the last time you did X, you know, 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. So this question is, what feedback will I be giving this person in their first performance review? That's an amazing question because the person can't dodge it. You know, there's an answer. And it's incredibly enlightening. And that's a question you ask on reference calls? Yeah, on reference calls. That is such a good question. I love it. It's a great, amazing question. All right. What a gem. Thank you for sharing that. What is a favorite product you've recently discovered that you really love? 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. I've been playing with Rewind lately. I was a bit late to it. Des and Kira and 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 um it's just like augmented memory it's kind of like my kind of mind-blowing so Rewind's been fun and they just came out with a little audio thing that can record your actual day and yeah yeah got some got some flack yeah I'm not so sure about that yeah I don't know I don't know if it's real it kind of looked like not a real product know. 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. Well, and it tippy-toes into the what's okay and not okay with AI, and, you know, yeah, yeah. It's cool theory though, for sure. What is a favorite life motto that you often come back to, share with people, find helpful for yourself? Yeah. often come back to, share with people, find helpful for yourself? Yeah, I have a post-it on my monitor that says only work on what matters most. It's on my monitor, it's a post-it. And if someone falls off, I have to write it again. Only work on what matters most. And like, it's amazing. I go into work, someone emails me and I'm like, oh God, you know, I'm like, only work on what matters most. The second one is, and they're related, is stop worrying about things you can't control. And so I have two of those. And so, uh, only work on what matters most, stop worrying about things you can't control. It just like reduces the temperature again, like life lessons learned. I sent a lot of dumb emails in my past, you know, 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 good you know keyboard and if your monitor says these two things you just don't do that you just take take a breath got a coffee come back does it really matter you know beautiful that's second one I think I learned first from um seven habits of highly effective people; have you read that, yeah, I've 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 there's the things you have no control over and i find that really helpful myself i love that you have 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 is on the monitor. It's in the way. You know? Wait, 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. Because otherwise, if it wasn't there, I wouldn't look at it. I make myself look at it. Yeah. Well, I haven't heard I haven't heard of people putting it over precious real estate on their monitor. Yeah, that works. What's the most valuable lesson your mom or your dad taught you? The biggest one, again, so reductive and simple is to be nice to people. I think being nice goes way further than people really realize. One thing that I've learned, again, the hard way through life is you have no idea what's going on in people's lives. You've no idea. People could have all sorts of like really stressful, all sorts of personal stuff going on. And the reason they did the thing and work that you didn't like is because of that. And so like, I try and think like, be nice. You don't know what's going on. Like you might learn later. Don't, you know, don't like, don't act in a way you would regret. I think being nice in life goes far further than most people give a credit for because it's kind of too much of a, I don't know, fluffy truism or whatever. 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 buy into that my parents taught me a similar lesson yeah it, and sometimes it's hard like um I'd never fired anyone before i joined intercom for example i didn't really did not like doing it and then and since then i've done it quite a few times in a bunch of different circumstances and realized it always works out for both sides and the nicest thing to do is to do the harder thing. You know, it's actually the nicer thing to do. People are like relieved in this example. It's, it's a better, it's a nicer thing to do. So it's, it's a, it can be a complicated one. I love it. 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 visit Ireland? Can I cheat and say Guinness? Is that 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. It's basically a fresh product and it's brewed here. It's kind of like milk, milk goes off, Guinness goes off. You know, Guinness is less than a few days, older than a few days old, tends to start deteriorating. So Guinness in Ireland is amazing because it's made here. The other thing I think that Ireland does really well is fish. Ireland has not had, by the way, the greatest reputation for culinary excellence over the years. I think Irish food in the States in particular is not good, but the fish here is incredible. You can get incredible fish. Ireland is obviously an island, so there's a lot of fish. On the Guinness front, is there any way to get the good stuff not in Ireland, or is that just you got to go? No, there is actually. You just need to be near a brewery. You need to be like and so Guinness is brewed in Nigeria. There's a huge Guinness market in Nigeria. I think they actually use a different recipe but it's brewed there. I think the brewery in the US is somewhere 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. The Guinness in San Francisco tends to be really bad. I remember talking to someone about this that works in Guinness. One of my friends does a lot of work in Guinness. I think the boat carrying the Guinness goes down through the Panama Canal back up to San Francisco. So you're like it's 12 weeks old or something. Wow. Did not think we would be learning about 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 these are the stories I've been told. 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? I have a handle I use everywhere. Basically P-A-D-D-A-Y. It's like Paddy with an extra A. So P-A-D-D-A-Y. That's everywhere. So Paddy at Gmail, at Paddy. That's my kind of handle everywhere. So that's where you can find me. I'd love, yeah, I'd love people to reach out to me. Like genuinely learn. I'd love to hear from 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 and, you know, it's more like a crypto web three or, you know, I'd love to hear people who have alternative opinions and challenge mine. That's how I kind of like to learn and get better. So if people have those opinions, I'd love to hear them. I'd love to talk to them. 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. Yeah, thanks, Danny. I really appreciate it. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

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