Lenny's Podcast: Product | Career | Growth - What AI means for your product strategy | Paul Adams (CPO of Intercom)

Starting point is 00:00:00 This is 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? What problem is it solved for them? That kind of thing.

Starting point is 00:00:20 So go back to basics and then ask, can AI do that? And for a lot, it's going to be yes, it can. For some, it might be it can partially do it. And then maybe for others, And, you know, I 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.

Starting point is 00:00:36 And in other places it'll be augmentation. It'll augment. It'll help people. But yeah, I think that you've got to match 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? Today, my guest is Paul Adams. Paul is chief product officer at Intercom, a role that he's held for over 10 years.

Starting point is 00:00:58 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. We then get deep into how to think about AI as a part of your product strategy,

Starting point is 00:01:25 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 Epo. Epo is a next generation AB testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch, Muro, ClickUp, and Draft Kings rely on Epo to power their experiments.

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Starting point is 00:02:56 and 10x your experiment velocity. That's get-epppo.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, Python, or no code, in any combination, and work together.

Starting point is 00:03:26 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 copilot 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, All-Trails, Loom, Mix Panel, and Algolia using Hex every day to make their work more impactful. Sign up today at hex.com slash Lenny to get a 60-day free trial of the Hex team plan. That's hex.com slash Lenny. Paul, thank you so much for being here and welcome

Starting point is 00:04:12 to the podcast. Thanks, honey. Nice to be here. 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. Also, you have an Irish accent, which is always a boost for ratings in my experience. So thank you for with you hear? Yeah, that's nice to hear. I wanted to start with a couple stories. So the first is your story of giving a keynote at Cannes. Can you share what happened there? Yeah, some things that happen in work every member at the time and they don't really scar you. This goes in the book that 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 basically, San Francisco,

Starting point is 00:04:54 So I did a lot of talks for Facebook internally and externally. Facebook had a keynote slot. Always had 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 well 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.

Starting point is 00:05:22 And, yeah, I got out in front of stage, you know, the world. 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'd given lots of times. And I just froze. I couldn't remember what I was supposed to say. It was the first ever time of my life, I'd rehearse to talk word for word. You know, usually like I have talking points and I'm ad lib and, you know, things get mixed

Starting point is 00:05:45 around and it's kind of informal. This was like, you know, media trained, like, don't, do not 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 miced up, cursed, ever started laughing. I was like, she's, are they laughing at me? You know, oh my God, this is. But I managed to turn around. I walked back out.

Starting point is 00:06:09 I'd kind of been disarmed internally in my head, and the mess of it went well. But it was, and I was famous that night, you know, out in Cannes afterwards, like on the, whatever, the seafront, it's just like rosé everywhere. And yeah, I was famous and infamous for my performance. I feel like you live 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.

Starting point is 00:06:43 Yeah, and it all happened kind of organically, I guess, very naturally, you know. But yeah, ever since then, every time I walk out onto a conference talk, stage still today, I 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

Starting point is 00:07:19 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. And there's a couple of products you worked on at Google. Both of them were not, 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 stories from that time? Yeah, similar to the face, to the kind of like, walking off stage thing. You live and learn. I was at Google for four years.

Starting point is 00:07:53 I was 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, the kind of social tech wave was like at its peak. Google were very afraid of the existential threat posed by Facebook. Facebook were very confident they could pull off some kind of like new social advertising unit that would be like an AdWords or something like that.

Starting point is 00:08:15 They 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 be a community companies. At Google, I worked on a lot of failed social projects, like you mentioned. Google Buzz, then later Google Plus. 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 that'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

Starting point is 00:08:54 I should say, you know, at the time in Google, there was other things happening that were amazing. Like Google were building Google Maps. Incredible product, one of my favorite products, and one of the best products ever made. They were building Android. You know, I was kind of in, 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 cut. And yeah, we, Google Bose is kind of a privacy disaster and Google Plus similar. And so kind of halfway through, I kind of published research about groups and how I've done a ton of research. An interesting kind of side note there is at the time I was been asked, I was working in the

Starting point is 00:09:33 research, in the UX team as a researcher, I was been asked to do a lot of tactical research, like usability study type stuff. And 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 is, 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.

Starting point is 00:10:01 We'd map on all with channels. We'd talk about what worked well, what didn't. And we did this with dozens and dozens of people over, like, the course, 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. I kind of look back now and go like, what's up? You know, or like, it may be like IMS if everyone's on Apple, but like really obvious in hindsight, but at the time, not obvious.

Starting point is 00:10:27 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, have been made public through a conference talk and suck and Facebook. notice, 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,

Starting point is 00:11:03 you basically shifted. Yeah, Midway. I'm stressed it even telling you about it. 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 worked in Google Plus was sent to a different building, that building had a different key card. If you didn't work in Google Plus,

Starting point is 00:11:24 you could not get in. All sorts of like kind of countercultural things at the time. And as a result, there was a lot of, you know, antagonism internally for Google Plus. And so when I left in the middle of the project, kind of leaving with all of the plans in my head to the enemy, you know, some people saw me as a trader,

Starting point is 00:11:43 understandably. Other people thought I was enlightened, you know, to Fatsy 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. 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

Starting point is 00:12:32 themes through the stories you just shared is, let's say failure is, I don't want to make it that harsh, but just things not working out. And I'm curious as a product leader, how important important. You think that is for people to go through if you think that's something that is almost a good thing. And I guess just is there anything there that you find helpful as a coach, as a mentor, as someone, two people that are trying to become basically you. Very, very. It still is. It still is. You know, like, I've personally failed so many times, you know, like there are two stories and the Google one is like long, deep tentacles. Like, there are two, they're two stories. I've failed a ton of times. Intercom. I remember like, you know, when I was at Facebook, I was very happy. And I knew Owen and

Starting point is 00:13:17 I knew Owen and does to the co-founders of Intercom and they were trying to persuade me to join Intercom. We were like, it was like 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, you can design the product, but at Intercom you can design the company. And that was extremely appealing to me, like a great pitch. He's like, just design the company with us that you want to work in. And so the, and so part of was a company that embraces failure that says it's okay to try things. I'm a big believer in like big bets, you know, higher risk, higher reward. I don't get as excited about incremental things. Now, I haven't said that, of course, a place for that too, especially as companies get bigger,

Starting point is 00:14:00 but I get excited about like 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 or in building software, like we have a principle called Ship to Learn. And our, that we've actually changed it since. Still run 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.

Starting point is 00:14:21 It's like in that idea is the idea of failure. You know, 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 we, as much as possible, try to embrace and teach people. But it's much easier said than done. especially when you're in the moment, like, God damn it, everything's going to fall apart.

Starting point is 00:14:46 I really, I really mess this one out. Yeah, and there's a tradeoff 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 tradeoff where people have these high standards, which we encourage.

Starting point is 00:15:10 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 a big, 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 or surprises so far in how you've thought about AI and how you think AI will integrate into product and product strategy? A hot day to chat GPT launch, November 29th, I think last year.

Starting point is 00:15:42 ever since that day, I later 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 you about AI, people typically fall into one or two camps. You're either like all in, like really, truly all in. This is a like 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 technology shifts, the way it will shape society.

Starting point is 00:16:15 So I'm all in. I'm like, I've gone over the hill or whatever. I'm over the other side. And so there's people in that camp. And then I think there's people in another camp, which is, I've heard this before, it's hype. Like, you know, last year was crypto, you know, web three. Like, none of those things worked out.

Starting point is 00:16:33 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 Beneverse is kind of of the best one that might be coming back. And I kind of think about I'm trying to remember, there's a lot of the law where you have

Starting point is 00:16:50 like, you know, the hype and then the trough of disillusionment and then you kind of come out the other side. Yeah, a little curve. Yeah. And I think that's where a lot of people might be where like the height, 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.

Starting point is 00:17:08 I'm all in in the other in the other camp. like this is going to radically transform society and it kind of like 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 we're all in for sure.

Starting point is 00:17:31 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, and 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

Starting point is 00:17:54 the advice I have for myself, you know, I'm in that too. Like I wake up every day to too many emails and Slack chats and, you know, people knocking on my door and my desk and all kind of things. So like, this is the challenge for me too. You just have to take the time. Like there's just no other way for me. And that to me doesn't mean, you know, it's about priorities. You know, it doesn't mean that you like need to work, you know, crazy hours. I don't believe in working crazy hours. You know, 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. Like you got to put it into your day, you know, whether that's like setting aside

Starting point is 00:18:30 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. If you don't have chat GPT, if you don't have like a kind of, I can't remember if it's a pro license or whatever, like, 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 I got out for dinner last Friday,

Starting point is 00:18:53 like with my wife, I try not to take work to dinner, you know, my wife, but I wanted to try it and I took some photos of our food. And like, you know, can do all sorts of crazy stuff, like tell you how healthy the meal is or whatever. Anyway, you got to try it. You just got to try it. So my advice people is you've got to

Starting point is 00:19:11 try it. You've got to set aside the time or it will pass you by. It does remind me the mobile, the kind of mobile way about a decade ago. Again, I was at Google at the time. I was working in the mobile team. So I guess it was my job to stay on top of things. But at that time, you know, some companies like Facebook went all in on it, maybe a bit late, but they eventually made the brave decision. And I think if people don't explore AI properly, it will leave them It reminds me, I think, at Facebook, Zuck, and also at Airbnb Brian did this. He said any mocks you show me for new product designs have to be in a mobile app or on a mobile web. They can't, it can no longer be desktop for now.

Starting point is 00:19:50 Right. Yeah, I'm at that same at Facebook. Yeah. I think that's the way to approach this is as a leader. Just everything you bring me needs to have some AI component. That sounds probably not like a good idea. But is there something there you thinking about or have done of just like convincing people? This is where you want to spend your time.

Starting point is 00:20:05 Yeah, it's harder. For sure. It's harder because... Is it on a 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 it in here for a long time. So we've been working in the space for quite some time.

Starting point is 00:20:17 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 like, hey, what do you think about AI? I would have said something like, it's not real. Machine learning's real. Let's talk about that. You know, so things change and my perception of it's changed. But a lot of the improvements are kind of like behind the...

Starting point is 00:20:36 scenes, you know, there were like large language models or like different types of things people are building in the background of infrastructure. So I don't know what it looks like to, you know, design mobile mockups that are like AI mockups. But I do think that like people need to need to start really thinking strategically. Like I don't know, maybe it's just not a mockup stage, but start to think really strategically about their product and whether it's in the line of the meteor 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. Other areas, it might be less so. But I think that's actually the headspace that I think people

Starting point is 00:21:16 need to be in. Can you impact that further? What do you, what does that look like to really think deeply about whether your product is in the way of the meteor? 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 is it solved for them?

Starting point is 00:21:46 That kind of thing. And then ask the question. So go back to basics. Okay, what is my product for? Why do people love it? And then ask, can AI do that? And for a lot, it's the answer to me, yes, it can. For some, it might be, it can partially do it.

Starting point is 00:22:01 And then maybe for others, it can't do that, at least not yet. And the types of things, you know, so you can't need to map like what your product does against what AI can do. And like AI can do a lot. Like, it can write. I'll try, I'll give you a like a list. It can write. It can summarize text. It can write text.

Starting point is 00:22:22 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 like the next big thing coming. It can take actions.

Starting point is 00:22:35 do things. It could like, hey, I mean, hey, AI, whatever the AI is called. Yeah, change my flight. Change my flight to Tuesday, right? It can do things like that. And so it can do a lot of things. It can, it can think, it can build rules. It can, you know, so any, 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 fire, they're in the media line or whatever. I don't know if this metaphor is working, but like, yeah, the media is coming and they're, in its path. And so for a lot of these products,

Starting point is 00:23:09 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 in other places it'll be augmentation. It'll augment. It'll help people as a co-pilot ideas that are going around. But yeah, I think that you've got to match your product and what AI can do and what it will be able to do

Starting point is 00:23:29 and then ask yourself, okay, what are we going to do? Is there an example of that at Intercom or a different? company of here's a problem we're trying to solve. Oh, A, I can actually do this fully for us. Oh, yeah. I'll give you Intercom first. Like, again, you know, this date's kind of, I think it's never over 20 nights. Like etched in our head. You know, we have like Fergal, who was our head of machine learning. And Fergal just turns around that day and he's like, okay, you 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. You know, like, I actually often talk about people. There's a little framework

Starting point is 00:24:04 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 of a head of OpenAI, said, hey, one of the first industries that's going to be disrupted is customer service.

Starting point is 00:24:34 We're like, 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, 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 were 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. Do you share briefly what Finn is, just for folks that aren't familiar? Finn is, first and foremost, is an AI chatbot. So if you think about customer service,

Starting point is 00:25:22 you know, people have questions for a business. And historically, that was mostly email and phone, mostly ticketing based, so you know, a lot of do not reply email and kind of so on. And then came along conversational customer support, which is just basic messaging, like WhatsApp or I messaged, I mentioned earlier. Now there's like, you know, bot first experiences.

Starting point is 00:25:44 And Finn is an AI chat bot. AI first, chat bot 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 seem to Finn are like mind-blown. Our biggest challenge is actually trying to help customer support teams think about organizational change.

Starting point is 00:26:04 You know, it's not, like, the tech is, like, 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 is expanded. So Finn is now also in our Intergram inbox, the place that people answer queries,

Starting point is 00:26:31 customer support queries. And now Finn's in there, too, helping. the support reps, like suggesting answers for them to use or helping them like rephrase things or so it's it's now augmenting people as well as answering questions by itself. 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. I'm curious, just unpack a couple more things here. First of all, what kind of impact have you seen after going all in and going in the story?

Starting point is 00:27:04 direction. It's very early, honestly, to be able to answer that properly. And it depends what you measure as success. So again, there's a lot of hype and buzz with AI. So if you're measuring it by like interest, it's a huge success. A lot of people, like our target customers, customer support, our customer sport 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,

Starting point is 00:27:48 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, I think all fast-growing startups, you know, if you think of intercomas or, like, AI intercomas, I guess a new startup, even though we're

Starting point is 00:28:25 900 people, you know, the kind of growth curve, you're looking for this kind of exponential curve. as opposed to like big public company kind of linear growth curve. With the exponential, it takes a while. You know, the first kind of year,

Starting point is 00:28:36 two years is the like 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, you know, we have enough evidence to believe it's the future for sure.

Starting point is 00:28:53 Are there any examples of either this product or other instances of AI just kind of blowing your mind where it just like, wow, I never imagine it would be this good. I kind of go back to that like before-after thing. So chat GPT, the first version of chat GPT was a before-after where we had built, like we've been working, like I said in a space.

Starting point is 00:29:10 We've had a machine learning team for a long time. The way our machine learning thing worked before chat GPT was, yeah, but there's not a manual setup. Like a customer support manager would have to like orchestrate the bot and like teach it what to say and like, you know, just a lot of orchestration, a lot of teaching it. And then chat GPT showed up and it's like, it can do it by itself. Like, it gets it wrong sometimes, but so do people. People get the question

Starting point is 00:29:34 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 those are just, oh, it can answer questions. But then you're like, it can reason. There's actually like a debate about whether, is this reasoning or deduction or, you know, but it can like work things out. And I'm not one for going down into these like really philosophical things. Like I'm like, we just need to get build, let's go back building the product or whatever. but it can work things out and that blew my mind and like we fed it a whole bunch

Starting point is 00:30:04 just we fed chatchiti and other companies too like we played with other LLMs like on Tropic and so on it can work things out and that was like kind of mind blowing then you can see it doing things like writing code

Starting point is 00:30:15 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

Starting point is 00:30:28 writing code and you're like what happens next? 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,

Starting point is 00:30:45 I think the visual thing was bigger than the original one. Like it can parse imagery and like, you know, 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. You can be traveling, take photos of stuff. It's in a different language.

Starting point is 00:31:00 It's like etched in stone on a like 12th century cathedral. You're like, what does that say? And it'll tell you what it says. Like, it's just like, I don't know how to do that, you know. This is when I'm actually repeating most people these days. Here in Ireland, if you want to be a radiologist, you know, so like study x-rays and tell people what's wrong and so on and so forth. It's seven years training to like learn that, learn that skill.

Starting point is 00:31:26 So 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 it and synthesize every extra ever made. So, of course, it's better. And then you're like, okay, what happens now?

Starting point is 00:31:48 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 have here in Ireland, we have like long queues for hospitals, epic waiting lists for people getting x-rays.

Starting point is 00:32:12 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 you can say things and it sounds exactly like you and it's really, really good. like almost indistinguishable. We were like, that sounds like Paul. And so I mentioned that the Metaverse earlier.

Starting point is 00:32:31 I don't know if you saw Zuck talks to Lex Friedman, see that. So that was my first like, oh, like, so it's the Met, you know, if people haven't seen it, they met in the Metaverse, I think, or some virtual world. Yeah, it was like a, like a black room. In a black room, yeah. And the tech has come on so they can analyze your face and, you know, build a 3D model.

Starting point is 00:32:50 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.

Starting point is 00:33:13 You know, and so, like, there's an afterlife. Hey, you know, like your parent dies and you can still talk to them. And like, I could be the weirdest thing. Maybe it's not good for people. I don't know. but that tech is like just around the corner you know and the AI can like that's kind of like your question is mind blowing 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

Starting point is 00:33:45 yeah it is like the I think we're an irony report and like the voice translation thing is another one I can't remember it maybe it's in Mission Impossible where it can't 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, your voice will be translated in Spanish in real time for me. It's like, again, mind-blower. 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 in Spanish, and then also changes your lips to match. And we're trying to launch a

Starting point is 00:34:22 couple of those. And that's actually very AI-based, yeah. That's cool. That's pretty cool. You mentioned that 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, 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?

Starting point is 00:34:51 Ultimately, you need, like, really great machine learning engineers. like that's where it starts and if you don't have that then you know you're going to find a hard to build truly really truly great things you know so like what Open AI provide and what I'm tropic provide

Starting point is 00:35:07 and you know Claude and they provide like amazing an amazing technology but you got to build on top of it if you really want something brilliant you got to build on top of it so like we adapted what they build for customer support maybe someday we need to go build our own LLM that's just for

Starting point is 00:35:24 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 LMs. But anyway, that's like kind of the first thing. And of course, these people are in high demand. So you need to like invest in building out that function, I think. Really invest in building out the function. So that's what we've been doing. You know, our kind of like ML teams way bigger than it was and way bigger than it ever has been an intercom.

Starting point is 00:35:53 And then kind of 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 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.

Starting point is 00:36:20 What happens when it is now like an AI assistant in the inbox? 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 then you're kind of back into like, okay, we've a product team that's like a product manager, a product designer, you know, maybe three, four, maybe five engineers. And they're, they're getting help from the machine learning team. So like we've, we now have both set ups. 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.

Starting point is 00:36:55 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 app team. I worked on mobile Gmail, mobile docs. And it was like the mobile team.

Starting point is 00:37:22 And we were in London. We're like, hey, we're the mobile team in London. And meanwhile, over in Matinview in California, no one cared. You know, it was like, it was like, you're 20 people, we're 200. No one uses this stuff on a phone. And again, a lot of skepticism. No one's going to write docs on the phone. Seriously, you're going to write a document.

Starting point is 00:37:39 They're going to write a full documents on a phone. Are you crazy? You know, 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

Starting point is 00:37:54 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 team. That's like, they're the AI team. They're going to add AI to all this stuff. You're finding and lesson is integrated into every product team.

Starting point is 00:38:11 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, 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 like, I mean, I guess my background is like, you know, Jack of All Trades, Master, and Nunn, it's probably I described myself.

Starting point is 00:38:37 Like, I've worked as a researcher, designer, PM. And so I believe in generalists, and so I believe in setting teams up that way. And yes, specialists and matters at times, like machine learning, for sure. is a deep specialism. And 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,

Starting point is 00:39:02 get more people being able to do it. 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's other maybe pitfalls. 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?

Starting point is 00:39:27 Yeah, what I've mentioned so far. Don't you. Don't bolt it on. Don't keep, I stay up to date. You know, like, I mentioned it like read, like, I feel like I'm behind all the time. It's moving so fast. What do 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've great reading lists that I got read. I got dinner on Sunday morning. it's pretty random. I'm on Twitter, which is not called X-Course, a lot.

Starting point is 00:39:54 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, 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,

Starting point is 00:40:08 because those are cool stuff there. There's some newsletters as well and some people I follow. Any newsletters you could call out? They think we're our most interesting. Matt Rickard is one guy. who talks a lot about AI. The blogs of companies, too,

Starting point is 00:40:21 like, you know, open AI have a pretty good blog, and they write papers and summarize them. Cool. If there's 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.

Starting point is 00:40:33 Yeah, perfect. Yeah, yeah. There definitely is. I'll dig him out. Your question earlier, how do you do? Just try, book out half an hour

Starting point is 00:40:40 and just go deep for half an hour and then bookmark a few things, come back to the minute. Like everyone, like, you know, it could be so busy, so many distractions. you just got to have to set aside time.

Starting point is 00:40:50 Are there any other tools or apps that you find really helpful? Sounds like chat GPT is kind of at 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, like Google, barred is Google's kind of AI search engine.

Starting point is 00:41:05 Rewind is another, like, fascinating company. I think it's Rewind.A.I. Rewind is basically augmented AI for your memory. So install it on your, like, 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 is the boundaries and how it work and so on.

Starting point is 00:41:32 So I'm a believer in that type of thing. This episode is brought to you by HelpBark by Camellion, 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. HelpBarr solves this. It delivers answers directly inside your app and eliminates context switching.

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Starting point is 00:42:34 Sign up for HelpBAR today. It's free and easy to set up in minutes. Check it out at helpbar.a.i slash Lenny. That's helpar.a.com slash Lenny. 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? I don't know. Was there anything you ran into? That was a big stumbling block and something you had to get over. Yeah, like any company, Intercom is full of diverse opinions about things, you know, and I think

Starting point is 00:43:06 with AI, you know, I'm like, I'm all in. I'm not talking about I'm all in. Like I'm mean for the media is common. Like I'm sold. I'm way past that point. Also, no one knows. Like, no one knows. And so a lot of the time when we talk internally, like the strong buy-in from, you know, Owen, you know, co-founder and CEO, Des, you know, co-founder, like me,

Starting point is 00:43:30 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 in the company are 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, you know, why are you all in?

Starting point is 00:43:45 and I'm like an educated guess, a hunch. You know, a lot of it's like 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, 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.

Starting point is 00:44:09 For me personally, I don't know. I lived through the mobile thing pretty closely, haven't worked at Google on mobile. I lived through that phase, so I can see the same type of thing happening now with 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 that context or they disagree with it. You know, we've a lot of debate here about the future. You know, Fergut, I mentioned earlier gave myself and a few other people, a few of the product leaders. And he gave us like a, I don't know, was it a pitch or what, play, I don't know, about how,

Starting point is 00:44:44 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 1, 2, and 3. Yeah, Amazon. Yeah. So like Horizon 1 is kind of the medium,

Starting point is 00:44:59 short to medium term, like next 12 months, 12 to 18 months, Horizon 2 being like, hey, what's happening, whatever, 18 to 36 months out? I think people use different time frames, different horizons. Anyway, we're like in Horizon 1 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 plays out, everything we're doing now is going to be irrelevant and, like, useless.

Starting point is 00:45:23 And you're like, okay. You know, and so, like, 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 dry on and out voices of like alternative voices and opinions, which are often valid to. What does how people get that conviction? Is it just showing them examples of here? 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

Starting point is 00:46:06 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, yes, the customer support is definitely that, you know, like I said, Sam Altman's like, number one, customer support. So you're like, okay, I guess we should adapt. Adap or die is 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.

Starting point is 00:46:36 So for example, reporting software, you know, Tablo or, you know, any kind of reporting product, you know, how do they work? Well, they're like the typical kind of like, you know, read, write app, build dashboards, filtering, you know, kind of hardcore querying, kind of query query 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, 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 one last year? January.

Starting point is 00:47:22 Okay. Who was our top performing rep in January? You know, Lenny. Like, the reporting products in 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, Tablo, helping people visualize data. And then the question is, can AI just do this for you? And then that case, oh, and maybe you can. And that gives you basically a whole strategy like, okay, how do we actually

Starting point is 00:48:04 do that with AI? Yeah, 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 designed 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, 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 slash 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 the like mistakes you've made like I've been guilty of this many times where like I've jumped on a bandwagon and it was all wrong.

Starting point is 00:49:07 And like the older I get like the Web 3 thing, I'm like, I don't even know what Web 3 is. Crypto, I never bought crypto. Maybe I'm wrong about that. But I'm not a bandwagon jumper, you know. But I kind of maybe might have been when I was earlier. So like, and I try these days to read the alternative opinion. People who are skeptical or I think it's bad. A lot of people think this is terrible for humanity.

Starting point is 00:49:33 This technology is going to eat us alive. So I try and I try and like balance my optimism. I'm kind of a delusional 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?

Starting point is 00:49:58 Yeah, the other thing is don't be afraid. maybe, 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're only two engineers per team going forward. That's probably not really a good idea to do that. And I think in reality, that's not going to be how it plays out. Like, there's all sorts of like, you know, those are great studies over the years about how people don't end up losing jobs.

Starting point is 00:50:27 The jobs get moved around. And also, you know, for customer support, for example, It's a high attrition job. So people saying 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 would play out as much.

Starting point is 00:50:46 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. You have a lot of interesting frameworks. 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 act, you actually mentioned this before and after,

Starting point is 00:51:09 which I hadn't heard about is what's what's the general idea to that concept? Before after it is literally that simple, I think. Like we've a rebrand at the moment happening and that would be a before after moment, you know, we're redesigning our pricing and then the day that pricing goes live, that would be a before after because it was like, nothing's the same. And so, So we need to go back out and talk to people again. Like I'm a big believer in talking, you've got to talk to customers. It's the only way. You've got to talk, talk, talk, learn, learn, learn.

Starting point is 00:51:39 Don't take what to say 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, you know? Can you talk more about this pricing, learning slash mistake you shared? What do you think you did wrong? What happened there? You know, we had a principle called align price to value.

Starting point is 00:52:05 By the way, like, I think pricing is incredibly difficult. A lot of the design team who work in pricing here, you know, I say to them, like, it's one of the hardest design problems I know. Like, onboarding is another one. Onboarding people into a product is also, like, people are like, hey, you just design a few steps and it's pretty easy. People follow the steps. Again, like, deceptively difficult to design. design great onboarding. So I think pricing is like deceptively difficult. We had a principle around like aligning price to value. You know, people should pay based on the amount of value they get in the

Starting point is 00:52:39 product. Easy to say and incredibly hard to do. Value is subjective. The price is people's, you know, for some person, you know, they get like 10 units of value. Like I think that's about $5. Someone else is like, I'd pay it $5,000 for those 10 units of value. You know, so the biggest mistake was a lot of mistakes compounded. And 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.

Starting point is 00:53:18 Well, this feels like it could be this old episode, just talking through your pricing lessons and journey. Maybe just is there a nugget of wisdom you could share for someone that's trying to think about pricing right now based in your experience. The number one thing I would say is keep it simple. Keep it simple. It's so tempting to,

Starting point is 00:53:38 like with us, for example, a lot of SaaS products, you know, have add-ons where you're like, hey, you know, we built X and that's like 10 bucks or 100,000, spend what kind of product you're selling. We built X and that's the price of X. Hey, we've just built Y.

Starting point is 00:53:54 Why 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 that didn't have. So that's charge more for why. That doesn't really work with the other. Okay, let's look at an add-on. Oh yeah, cool.

Starting point is 00:54:08 People just add-on. But then, like, later, now you've got, like, 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, like, added tiers. We've, like, you know, cut with products, tiers, add-ons, tiering in the add-on. Oh, my God. You know, people can't understand their bill. So my advice is keep it simple.

Starting point is 00:54:33 Reject, like, fight so hard to not, to, like, 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 think I was talking about scars for life earlier. That's another scar for life. All right. Let's keep talking about some frameworks. Another that I found that I loved

Starting point is 00:54:59 is something that you call differentiation versus table stakes. What's that about? It's kind of like the Cano model if we're familiar with that, but it's very simple. It's kind of like, I guess we took the Cano model

Starting point is 00:55:08 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 Cano model. I'm allergic to like people over intellectualizing frameworks

Starting point is 00:55:20 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. And so difference in which table say is very simple. I think people who adopt a product or buy

Starting point is 00:55:42 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

Starting point is 00:56:05 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.

Starting point is 00:56:20 That looks good. I want to check that out. That looks better than what I have to be. But on the other side, there's like a kind of entry requirement or like table stakes. You know, to play the game, you got to have a certain amount of things. And so they're table stick features. They're often very boring. You know, they're like real basic stuff, boring stuff.

Starting point is 00:56:41 And easy to ignore and easy to not build. And again, a mistake with Intercom maybe over the years is that we were much 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 all 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.

Starting point is 00:57:09 And then the robot was built based on those, like, trading off whether we need more differentiation or trading off whether we need to invest more tablesakes. So these days, the place of Intercombe 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 look at a roadmap or look at a proposed roadmap 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, any kind of established category, customer support for us,

Starting point is 00:57:47 big established category, massive, a lot of table stakes, built above, years, decades. You know, Service Now, 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 from 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 like Messenger first, modern messaging and modern

Starting point is 00:58:19 new X, they're like, we want that for our customers, alongside the big giant bag of table stakes, because Intercom 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 then hence, if your 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, gives you a way to think about looking at your roadmap. How much are we actually doing?

Starting point is 00:58:53 And are we doing too much table stakes? Are we doing too much differentiation? So it gives you kind of a 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 as it go the way Intercom went, like differentiate first.

Starting point is 00:59:10 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. Yeah. Awesome.

Starting point is 00:59:21 Okay. The next framework is something that you call swinging the pendulum. What is that about? I actually kind of mentioned an example of it earlier. The differentiation in table stakes was 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, you know, maybe it's, well, we've all the differentiated.

Starting point is 00:59:49 in the world, but people can't adopt a 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 there's undesirable state. And then so you go and fix it. But the temptation is that you overcorrect.

Starting point is 01:00:13 And we've done this so many times in so many domains, everything from, okay, we don't have enough differentiation. A year later, oh, wait a minute. Like, we're missing all the table stakes. Okay, everyone over there, you know, so product building is one. People is another one, building our teams and people. Like another big one was, uh, maybe, I don't know, maybe five years into intercom. We were, you know, we're on this kind of like high, high growth trajectory, really kind of good classic startup, uh, before our pricing problems. And, um, we kind of like, we looked around and said, none of us have done this before. I don't think that's good. 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?

Starting point is 01:00:59 We need to hire some experts. We need to hire some experts. Like, you know, if we're going to go upmarket, we need upmarket people who've done it before. So, you know, that was like undesirable state. Fix it by hiring people who've done it before. Then we hired loads of people who've done it before. What they did was brought the culture and ways of working of their prior company to Intercom. And so we totally overcorrected. It didn't work out for in a lot of cases. In most cases, didn't work out because we weren't trying to be a bigger company that already exists. We were trying to be us, you know. So like hiring, hiring and building teams is a matter of where we really overcorrected to find out like, okay, it's a balance here. Related to

Starting point is 01:01:43 that one, really the hiring one is like generalists and specialists. Kind of similar theme. People have done it before or people who are specialized. And we hired a bunch of specialists, specialists, only to realize that they're not adaptable. And in intercom, you know, we believe in kind of, we've 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 startupy culture with a lot of ambiguity that 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

Starting point is 01:02:30 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. It's like, here's not everything was perfect. Here's a lot of mistakes that are made along the way. And it 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 is like, no, we're going all in or in mobile.

Starting point is 01:02:57 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

Starting point is 01:03:22 versus, like, you gotta swing it. And then it's like, 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, like, you know, if AI is like in the differentiation camp to kind of mix the frameworks, we're still building a lot of table stakes features

Starting point is 01:03:40 too, like building depth into the product. And that's 50-50. You know, I think I mentioned 50-50 earlier. So that's 50-50. So we're not totally swinging it. We're not like, you know, it's swung, but we're also kind of doing the other thing and balancing things out. So I think you probably have to swing it.

Starting point is 01:03:59 It reminds you 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 what I went, I remember at Google, privacy was like, really top of mind to the point that it would like 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 uh 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 it's necessarily good it certainly didn't help

Starting point is 01:04:36 their brand but there was um kind of an idea that to know where the boundary is, you've got to cross it. And crossing, it's painful. But if you don't cross it, you'll never know. So if you think you're going up to the boundary, then you stop before it. Turns out it's actually miles over there. You know, so I think with a lot of this stuff, you know, you don't really have a choice. You've got to kind of cross the boundary.

Starting point is 01:05:00 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 act corrective courses yeah get that pendulum 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 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 yeah what is that so yeah with product market fit pretty basic well understood very important you know the way i just buy product market fit is

Starting point is 01:05:39 you've got to build the right product for the right market. I think, by the way, as an aside, a lot of 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. Like, 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,

Starting point is 01:06:01 and they need to care a lot about it. Again, back to the Google social stuff, we found a lot of people with the same problem, they didn't really care. they didn't really care. Like, you know, what they had is fine. So, like, a lot of people with the same problem and a lot of energy around the problem.

Starting point is 01:06:16 And the product is the solution to that. You know, so what? If that's the markets, the who, the products are what. And 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.

Starting point is 01:06:34 And eventually I came back to this idea that, like, and maybe someone might say, say, Paul, that's marketing. You're talking about marketing. But like story, the story is wrong or the story's missing. And so sometimes it would be a great product and 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.

Starting point is 01:06:55 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. important. And actually, sometimes you'll see, like, not great products, certainly worse on paper. I'm trying to remember, like, the Spotify competitor back of the day, people who were like, audio. Audio? Audio was one of these where like, yeah, audio, audio was one of these where like, yeah, people, like, great, like people, all I've ever heard about audio was amazing product.

Starting point is 01:07:29 It's failed, you know, and why did it fail? Spotify and audio at the same market. They were solving the same set of problems. audio was arguably the better product at the time. I don't know if that's true, but arguably the better. I always think Spotify's an incredible product. But the story, they've got the story wrong. And so again, I think all product people,

Starting point is 01:07:48 whether you're a designer, product manager, people in research, data science, need to think about the story all the time, worker marketing, work of 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.

Starting point is 01:08:08 And we had April Dunford on the podcast very recently talking a lot about that. Yeah, yeah, yeah, she's excellent. Yeah, it is really like, why are you better, you know, and can you explain why you're better? It was such an important point. A final area I wanted to touch on is jobs to be done. So we had the co-creator of jobs to be done on the podcast. We had Sri Ram Krishna on the podcast.

Starting point is 01:08:31 They'd 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 just 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? It doesn't work. Whatever comes up.

Starting point is 01:08:47 Yeah. I'll be totally honest at the risk of offending people to listen. Like we worked with Bob West, you know, who's age of years ago, I think Bob's right guy. And we kind of followed that model of jobs we done more than the ODI, I think is the other school of all. Anyway, I'll try and say this in a simple way. We found jobs fans you really good.

Starting point is 01:09:08 You're very, very useful. But in a very simple way, you're going back to the 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

Starting point is 01:09:22 and peculiarities of one version. Who cares? Like, no one cares. Oh, well, I don't care. They care, obviously. Your customers don't care. Like, people you're trying to build a product for it, don't care. no one cares. That's like a cool intellectual debate, but like kind of for me, maybe this is too extreme.

Starting point is 01:09:43 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, which else we've done, it was a really good way of us centering on the customer problem, like focusing on like not getting distracted, based on research, like good, solid research informed insight that told us like, thing people are trying to do. 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. I think about it. I think it did actually. I think the idea of like this

Starting point is 01:10:16 idea that 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. It's very easy to see if someone's apathetic versus like into it. So we've had it pretty good and we invented this job stories thing kind of by accident.

Starting point is 01:10:34 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 job story. Someone else called it that. We just, at the time, we're like, there was this, 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, um, the four forces, which is just like framework out of jobs we done.

Starting point is 01:11:10 The four forces being like different four people, 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, 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.

Starting point is 01:11:31 I was saying earlier that like Owen and Dads are trying to convince me to leave Facebook, which I loved at the time, join in the come. 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, like, whatever. Like, you know, basically worked me on the Four Forces. And I was like, that is genius.

Starting point is 01:11:55 That is ingenious. 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.

Starting point is 01:12:18 But I think there's this endless set of processes and ways of optimizing that gets people distracted and often just kind of slows everything down. Yeah, yeah. And it's interesting and fun to talk about sometimes, like really fascinating, you know, unless you're like 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,

Starting point is 01:12:45 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, yeah.

Starting point is 01:13:00 What are two or three books that you've recommended most to other people? people. 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. It's a book by Paul Arden, who has worked in advertising a long time ago. It's an excellent book. It kind of shows people that you feel 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 as Principles by Ray Dalio. I'm a big fan 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

Starting point is 01:13:32 totally different. The Paul Arden book is, you can read it in 20 minutes. Principles is like, that thick. What is a favorite recent movie or TV show that you 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 like that about it. What is a favorite interview question you like to ask candidates?

Starting point is 01:14:13 Yeah, I'll give you a slightly different answer. I don't already have said in a few questions for candidates. And I don't like, I don't like, I'll answer questions universally. 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, he used to work here. I had to do referral calls. So you're interviewing someone,

Starting point is 01:14:32 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 it and their favorite managers. So this question is, what feedback will I be giving this person

Starting point is 01:14:42 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.

Starting point is 01:14:59 question. I love it. It's a great, amazing question. All right. What a gym? Thank you for sharing that. What is the favorite product you 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, I think chat GPT 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 coming up founders of Intercom love Rewind. Use it and love things amazing. So I'm a bit late to that, but it's just like augmented memory. It's kind of like kind of mind-blowing. So rewinds me fun.

Starting point is 01:15:32 And they just came out with a little audio thing that can record your actual day. Yeah, I'm so sure about that. Yeah, I got some plaque. Yeah, I'm not so sure about that. Yeah. I don't know if it's real. It kind of looked like not a real product

Starting point is 01:15:46 when they launched it, but I think it's real. And it's if he goes into the what's okay and not okay with AI and, you know, yeah, yeah. It's a cool theory, though, for sure. What is the favorite life motto that you often come back to, share with people, find helpful for yourself. Yeah, I have a posted on my monitor that says, only work on what matters most.

Starting point is 01:16:10 It's on my monitor to 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 with things you can't control. and so I have two of those

Starting point is 01:16:29 and so only work my matters most stop worrying with things you can't control it just like reduces the temperature again like life lessons learned I send a lot of dumb emails in my past you know like red energy

Starting point is 01:16:42 oh my God what are they thinking you know like you wake up in Dublin to a San Francisco email and you're like oh god you know keyboard and if your monitor says these two things

Starting point is 01:16:54 you just don't do that you just take a breath get a coffee I'm back. Is it reading Otter? You know. Beautiful. A second one, I think I learned first from seven habits of highly effective people.

Starting point is 01:17:07 You've read that? Yeah. 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 it as a Post-its. I feel like I need to make post-its of all these lessons.

Starting point is 01:17:23 People share as their little mottos. Yeah, the Post-in on the monitor is. a real life hack I found a few years ago. It's like it's kind of dumb in a way. The post's on the monitor. It's in the way. It's in the way of your screen. Yeah. It's in the bottom, the bottom left. Like just cover in the bottom, you know, it's like because otherwise, if it wasn't there, I wouldn't look at it. I make myself look at it. Yeah. Wow. 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 and your at taught you.

Starting point is 01:17:58 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've

Starting point is 01:18:14 no idea what's going on in people's lives. You've no idea. People could have all sorts of 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.

Starting point is 01:18:33 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 it. Because it's kind of too much of a, I don't know, fluffy truism or whatever. I 1,000% 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.

Starting point is 01:19:03 So I fully bite into that. My parents taught me a similar lesson. Yeah. And sometimes it's hard. I'd never fired anyone before I joined Intercom, for example. I really did not like doing it. 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.

Starting point is 01:19:23 And the nicest thing to do is to do. do the harder thing. You know, it's actually the nicer thing to do. People are like relieved in this example. It's a better, it's a nicer thing to do. So it's a, it can be a complicated one. I'd love it. Final question. You're Irish. You're based in Ireland. What is a Irish food? You think people should definitely try out if they ever visit Ireland. Can I cheat and say Guinness? Is that food? Absolutely. Guinness and Ireland, people talk about this, and like, it's true. The Guinness and 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, the way they think about it's 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 and Ireland is amazing because it's made here. The other thing I think that Ireland does really well as 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.

Starting point is 01:20:29 But the fish here is incredible. You can get incredible fish. In Ireland, it's 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.

Starting point is 01:20:48 So Guinness had 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 on the East Coast between New York and the 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.

Starting point is 01:21:14 One of my friends does a lot of work in Guinness. I think the boat carry 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.

Starting point is 01:21:37 Amazing. Paul, you are awesome. Thank you so much for being here. Two final questions. Where can folks finding online if they want to reach out? And how can listeners be useful to you? I have a hand light is everywhere. Basically, P-A-D-D-A-Y.

Starting point is 01:21:49 It's like Paddy with an extra A. So P-A-D-A-Y. That's everywhere. So Paddy at Gmail. at Patty. It's my kind of handle everywhere. So that's where you can find me. I'd love, yeah, I'd love people 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.

Starting point is 01:22:18 That's how I kind of like to learn and get better. So if people have those opinions, I'd love to hear them. Enough 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 lenniespodcast.com.

Starting point is 01:22:57 See you in the next episode.

Lenny's Podcast: Product | Career | Growth - What AI means for your product strategy | Paul Adams (CPO of Intercom)

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