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

1. 0:00 – 4:09

   Paul’s background

   1. PAPaul Adams0:00

      This is a, like, 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, it's, the answer is gonna be yes, it can. For some, it might be, it can partially do it, and then maybe for others, it, mm, 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, like it'll help people. But yeah, I think that you gotta map your product and what AI can do, and what it will d- be able to do, and then ask yourself, "Okay, what are we gonna do?"

   2. LRLenny Rachitsky0:49

      (instrumental music) 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 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, AllTrails, Loom, Mixpanel, and Algolia using Hex every day to make their work more impactful. Sign up today at hex.tech/lenny to get a 60-day free trial of the Hex team plan. That's hex.tech/lenny.

2. 4:09 – 7:28

   Freezing onstage in front of 8,000 people

   1. LRLenny Rachitsky4:10

      Paul, thank you so much for being here and welcome to the podcast.

   2. PAPaul Adams4:14

      Thanks, Lenny. Nice to be here.

   3. LRLenny Rachitsky4:15

      It's nice to have you here. I've heard so many good things about you from so many different people, so I'm really happy that we're finally doing this. Also, you have a Irish accent, which is always a boost for ratings in my experience, so thank you-

   4. PAPaul Adams4:25

      (laughs) .

   5. LRLenny Rachitsky4:25

      ... for bringing that with you here.

   6. PAPaul Adams4:27

      Yeah, that's nice to hear.

   7. LRLenny Rachitsky4:28

      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?

   8. PAPaul Adams4:38

      You know, something, some things that happen in work are very memorable at the time and they don't really scar you. Uh, this goes in the book that I'm scarred for life. Uh...

   9. LRLenny Rachitsky4:45

      (laughs) .

   10. PAPaul Adams4:45

       Yeah. I, I...

   11. LRLenny Rachitsky4:46

       A bit of a rim.

   12. PAPaul Adams4:46

       To cut a long story short, I was at Facebook just over a decade ago. Loved it at the time. Um, I think it was a great place to be at the time, and, uh, based in San Francisco. I did a lot of talks for Facebook internally and externally. Um, Facebook had a keynote slot, always have a keynote slot at Cannes, the world's biggest advertising festival. And, um, the year prior, Zuck had been interviewed, he was the speaker, he'd been interviewed, gotten a hard time on privacy. Uh, 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-

   13. LRLenny Rachitsky5:19

       (laughs) .

   14. PAPaul Adams5:20

       ... that (laughs) made, made the offer come my way. And yeah, I got o- I got out and stood on stage, you know, the world's, uh, 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 one I've given lots of times, and, um, I just froze. I, I couldn't remember what I was supposed to say. Uh, it was the first ever time in my life I'd rehearsed a talk word for word. You know, us- 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, do not say the wrong thing," kind of talk. And I just could- could not remember what to say. I had some version of a panic attack. Uh, walked offstage. I was still mic'd up. Uh, cursed.... and it was her laughing. I was, like, "Geez, are they laughing at me?" You know, oh my God, this is... Um, but I, you know, I, I can manage 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, uh... And I was famous that night. You know, out in Cannes afterwards, like, on the, on the, whatever, the, the seafront, it's just, like, rosé everywhere. And, um, yeah, I was famous and infamous for my performance.

   15. LRLenny Rachitsky6:26

       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 is, like, you could freeze in front of thousands of people, walk off stage (laughs) , and then it works out okay.

   16. PAPaul Adams6:43

       Yeah, and it all happened kind of, uh, organically I guess, or very naturally, you know? But yeah, e- 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.

   17. LRLenny Rachitsky7:15

       I also hate public speaking and I always fear this is exactly what's gonna happen to me. And so I think this is, uh, nice to hear that even when the worst possible thing basically happens, things can survive.

   18. PAPaul Adams7:27

       You can turn it around,

3. 7:28 – 12:31

   Insights from Google+ days

   1. PAPaul Adams7:28

      yeah.

   2. LRLenny Rachitsky7:29

      A second area I wanted to hear from is your time at Google and there's a couple 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?

   3. PAPaul Adams7:45

      Yeah. Uh, similar to the Fa- to the kind of like, you know, walking off stage thing, um, you live and learn. And, uh, G- I, I was at Google for four years now and I was at 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, y- you know 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, or something like that, that would like, you know, destroy Google's revenue, eat them from the inside out. And so being there at the time was fascinating and moving between the two companies. At Google, I worked on a lot of failed social projects, like you mentioned, Google Buzz, um, Google, then later Google Plus. I think a, 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, uh, and so during those times I kind of lear- I learned I think how, how not to lead, uh, in places. And by the way, I should say, you know, that at the time in Google there was other things happening that were amazing. Like, Google were building Google Maps, uh, incredible product, one of my favorite products, I think one of the best products ever made. They built, 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 like I made, you know, incredibly good product. So I just happened to be in the social side, which wasn't as good (laughs) . And, uh, yeah, we, we, um... Google Buzz was kind of a privacy disaster and Google Plus similar. And so kind of halfway through, I, I'd kind of published research about groups and how... I'd done this, I'd done a ton of research. A ki- an interesting kind of side note there is at the time I was being asked, I was working in the research, in the UX team as a researcher, I was being asked to do a lot of tactical research, like usability study type stuff, um, like can people use these products? And I ended up doing, um, a lot of formative research as well in the same session. So I sp- I'd kind of say to the team like, "Hey, I'll do the research, I'll answer your questions, but also I'm gonna do this other thing and I'm gonna take 20 minutes doing that." And so what we used to do is, um, or 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 ta- and we did this with dozens and dozens of people over, like, 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 like, I kinda look back now and go like, WhatsApp, you know? Or like, maybe like iMessage if everyone's on Apple. But like really obvious in hindsight, but at the time not obvious and, uh, uh, and so we kind of tried to build a product around that called Google Plus. But, um, again, it was kind of motivated from the wrong, came from the wrong place and so halfway through the research that I, that I'd kind of done, all this research had been made public through a conference talk and, uh, Zuck and Facebook noticed, got in touch, one thing led to another and I left and joined Facebook, which was an amazing thing for me personally. I, uh, Facebook was amazing, an amazing place at the time and exciting and, and they were trying to do things for the other reasons, the, the kind of good reasons like, "Hey, let's build an amazing product for people."

   4. LRLenny Rachitsky11:01

      And this was during Google Plus being built, you basically shifted?

   5. PAPaul Adams11:04

      Yeah, midway. It hadn't r- I'm stressed to even tell you about it. The project hadn't been launched, it was still under wraps, you know? Um, it was highly confidential. Google had done a lot of things at the time that, that, that were the first for them. I don't know if they've done them since, but things like everyone who worked on Google Plus was sent to a different building, that building had different, had a different key card. If you didn't work on Google Plus you could not get in. All sorts of like kind of, uh, counter-cultural 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 traitor, understandably. Uh, other people thought I was enlightened, you know? (laughs) It depends who you talked to. But it was, um... I was a, like, it was the right thing for me to do but at the time, you know, it was, um, was a hard thing to do.

   6. LRLenny Rachitsky11:56

      I know there's also, like, a lot of scrutiny in what you took with you and the process.

   7. PAPaul Adams12:01

      Uh, yeah. When I left, uh, Google kind of assumed that I was one of the spies, you know. I was quarantined when I told them I was leaving, uh-... uh, they, uh, you know, forensically analyzed my laptop, like all sorts of stuff like that. So, it was pretty intense. Um, you know, looking back I, I can understand why that happened. But the root cause for me is that the project was being run from a place of fear, competitive fear, which I don't think leads to good things.

4. 12:31 – 13:56

   Learning from failure

   1. PAPaul Adams12:32

   2. LRLenny Rachitsky12:32

      So one of the themes through the story that you just shared is, let's say failure, is, is... (laughs) I don't wanna make it that harsh, but just things not working out. And I'm curious as a product leader how important do 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, as a coach, as a mentor, as someone... two people that are trying to become basically you.

   3. PAPaul Adams12:58

      Very. Very. Uh, still is, it still is, you know? Like, um, I've personally failed so many times, uh, you know, like there are two stories, and the Google one is like long, deep, deep tentacles. But there are two, there are two stories. I, I, I failed a, a ton of times. Like at Intercom... I remember like, you know, when I was at Facebook I was very happy, and, um, Owen and De-... I knew Owen and Des, two of the co-founders of Intercom, and, uh, they were trying to persuade me to join Intercom. We were like... it was like a 10-person company at the time. But Owen said something to... something to me at that time which, uh, has stuck with me ever since. He said, "You know, at, at Facebook you can j-... you can design the product. But at Intercom you can design the company." And that was extremely appealing to me, like a great pitch. Uh, he's like, "Just design the company with us that you wanna work in." And so the... and so part of that 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, high-risk, high-reward. Uh, I, I, I don't get as, as excited about incremental things.

5. 13:56 – 15:17

   Intercom’s “ship fast, ship early, ship often” principle

   1. PAPaul Adams13:56

      Now having said that, there's of course a place for that too, especially as companies get bigger. But I, I, I get excited about, like, big, big bets. And if you make big bets you're gonna get there-... a lot of it wrong. So a lot of the principles that we built here at Intercom around building software, like we have a principle called Ship to Learn. And, uh, our sh-... and, uh, we've actually changed it since, it's still on the wall here. Uh, "Ship fast, ship early, ship often" is what it says now. Used to say Ship to Learn. "Ship fast, ship early, ship often." so like in that idea is the idea of failure. You know, you're gonna... e- it's not gonna go right. And, uh, it's gonna 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 kinda how we... that's the kinda culture that, that we as much as possible try to embrace, uh, and teach people. But it's much easier said than done.

   2. LRLenny Rachitsky14:42

      (laughs) Yeah. Especially when you're in the moment, like, "Goddammit, everything's gonna fall apart. I really, I really messed this one up."

   3. PAPaul Adams14:48

      Yeah. And there's a trade-off w- with quality that people really struggle with. Like, you know, we, we have high standards of ourselves. A lot of Intercom comes from a kind of design founder background. We, we value the craft a lot. We never wanna be embarrassed by what we ship. So there's a real tension there, a real trade-off, uh, where people have these high standards which we encourage, and we encourage them to ship fast, and learn, and make mistakes. Uh, it's a constant kind of tension that we're navigating.

6. 15:17 – 17:31

   Integrating AI into product strategy

   1. LRLenny Rachitsky15:17

      Speaking of taking big bets and going all in, I know there's been a, 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?

   2. PAPaul Adams15:39

      W- uh, what day did ChatGPT launch? November 29th, I think, last year? E- ever since that day I, 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 i- 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 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 kinda technology shift. As big as the internet, maybe it's bigger than the internet itself as a kinda soc-... you know, technology shift, the way it'll shape s- shape society. So like, "I'm all in. Uh, I'm like, I've gone over the hill or whatever (laughs) .""I'm over the other side." A- and so there's people in that camp. And then I think there's, like, people in, in another camp which is, "I've heard this before, it's hype." Like, you know, last year it was crypto, you know, it was Web3, 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. Now with the metaverse, this kind of investment might be coming back but... and I kinda think about, um... oh, I'm trying to remember, there's a la- the law where you have, like, you know, the hype and then the trough of disillusionment-

   3. LRLenny Rachitsky16:52

      Mm-hmm.

   4. PAPaul Adams16:52

      ... and then you kinda come out the other side.

   5. LRLenny Rachitsky16:53

      Uh, that ch-... yeah, little curve chart

   6. NANarrator16:55

      ... yeah.

   7. PAPaul Adams16:56

      And, 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 kinda tune it out a little bit. Um, and I think some people have kind of fallen into that camp. I'm all in, in the, in the other, in the other camp. Like, this is going to radically transform society, and it, it kinda like blows my mind e- even seeing new types of things that come out, like ChatGPT 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.

7. 17:31 – 19:37

   Making time for AI learning

   1. PAPaul Adams17:31

   2. LRLenny Rachitsky17:31

      Awesome. I wanna unpack that. But I think there's also this camp of people that are 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 wanna go deeper down this rabbit hole, I just don't know where to start 'cause I have so much work to do already, and this isn't like a side thing"?

   3. PAPaul Adams17:53

      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 day to too many emails, and Slack chats, and, you know, people knocking on my door, and my desk, all kind of things. So like, uh, this is a challenge for me too. You just have to take the time. Like there's just no other way for me. And that, 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, 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, get tired. And you need to live the rest of your life. Uh, like you got to put it into your day, you know? Whether that's like setting aside dedicated time to read. Uh, uh, reading I- is the thing. You gotta read. You gotta stay up-to-date and you gotta play with things and try things. If you don't have ChatGPT, if you don't have like a kind of, I can't remember if it's the pro license or whatever like, but if you haven't upgraded to get access to things like GPT-4 Vision, uh, where it can, you can take photos and you have the mobile app. And I got that for dinner la- last Friday night with my wife. I try not to take work to dinner, you know (laughs) with my wife but I wanted to try it and I took some photos of our food and like, you know, it can do all sorts of crazy stuff like tell you how healthy the meal is or, or whatever.

   4. LRLenny Rachitsky19:07

      Oh, wow.

   5. PAPaul Adams19:07

      Anyway, if you don't, you ought to try it. You just gotta try it. So like my advice to people is you've gotta try it, you've gotta set aside the time or it'll pass you by. It does remind me of the mobile, kind of mobile wave about a decade ago. Again, I was at Google at the time. I was working on the mobile team so I guess it was my job to stay on top of things. 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, it will leave them behind.

8. 19:37 – 21:16

   AI in new-product development

   1. PAPaul Adams19:37

   2. LRLenny Rachitsky19:37

      It reminds me, I think at Facebook, Zuck, and also at Airbnb Brian did this is he said, "Any mocks you show me for new product designs have to be on a, in a mobile app or on a mobile web."

   3. PAPaul Adams19:48

      Yeah.

   4. LRLenny Rachitsky19:48

      "They can't, they can no longer be desktop for now."

   5. PAPaul Adams19:50

      Right. Yeah. I met that same with Facebook. Yeah. That's right.

   6. LRLenny Rachitsky19:53

      I guess, do you think that that's a 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're thinking about or have done of just like convincing people this is where you want to spend your time?

   7. PAPaul Adams20:05

      Yeah. It's harder, for sure. It's harder because-

   8. LRLenny Rachitsky20:08

      Because you don't want to force it.

   9. PAPaul Adams20:09

      ... a lot... Yeah. A, a lot of the tech is invisible, you know, like a lot of the things... Like we, we've a machine learning team. We've had one here for a long time. So we've been working in this space for quite a, quite some time. But, it's funny, even if you go back like 18 months, I think if I was on, on, on the, 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, you, you know? So, so things change, and, and my perception of it's changed. But a lot of the, a lot of the improvements are kind of like behind the scenes, you know? There were like large language models or like different types of things people are building in the background like infrastructure. So I don't know what it looks like to, you know, design mobile mock-ups that are like AI mock-ups, but I do think that like people need to, need to start really thinking strategically. Like I, I don't think that, maybe it's just not at mock-up stage. But start to think really strategically about their product and whether it's in the line of the meteor that's coming or not. You know? 'Cause 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, but I think that's actually the head space that I think

9. 21:16 – 23:33

   Questions to ask about your product

   1. PAPaul Adams21:16

      people need to be in.

   2. LRLenny Rachitsky21:17

      Can you impact that further? What do you... What does, what does that look like to, uh, really think deeply about whether your, your product is in the way of the meteor?

   3. PAPaul Adams21:25

      You can get side-tracked by the, by the technology for sure. And I, I do. I just imagine like, "Hey, going out for dinner and taking a photo of my food." You know? You can get side-tracked by the tech and some of it's really cool. I wouldn't start there. I'd start with, uh, 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. Like, "Hey, what is my product for and why do people love it?" And then, and then ask, "Can AI do that?" And for a lot, it's, the answer is gonna be yes, it can. For some it might be it can partially do it and then maybe for others it, mm, you know, it can't do that. At least not yet. And the types of things, yeah, so you're gonna 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. Uh, I'll try... I'll give, give you like a list. It can write, it can summarize, uh, 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 like, uh, the next big thing coming. It can take actions, actually do things. It could like, "Hey..." I mean, "Hey, AI," (laughs) whatever, whatever the AI is called-

   4. LRLenny Rachitsky22:41

      (laughs)

   5. PAPaul Adams22:41

      ... "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, is almost all B2B SaaS products, any product that has multimedia in it, they're in the fi- they're in the meteor line or whatever. I don't know if this metaphor is working but like, you know, the meteor's coming and they're like in its path. And so for a lot of these products, they, 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, you know, in other places it'll be augmentation. It'll augment, like it'll help people, um, with the copilot ideas that are going around. But yeah, I think that you 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 gonna do?"

10. 23:33 – 25:13

    How Intercom pivoted after the release of ChatGPT

    1. PAPaul Adams23:33

    2. LRLenny Rachitsky23:33

       Is there an example of that at Intercom or a different company of here's a problem we're trying to solve. Oh, AI can actually do this fully for us?

    3. PAPaul Adams23:40

       Oh yeah. Uh, like, at Inter- I'll give you Intercom first. Like again, like, you know, this date's kind of et- uh, I think it was November 29th, like etched in our head. You know, we, we have like Fergal who, who's our head of machine learning and Fergal just turns around that day and he's like, okay, I think he Tweeted something actually. Uh, he had a Tweet that day that was like, "This is it. This is the mo- this is the time, this is the moment, this is the before/after." You know, like I'll actually often talk about people... This is a framework I have like before/after moments. This is a before/after moment. There was before and now there's 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, our- Intercom is a customer support- a customer support product. And then very soon after that, Sam Altman, who's the, you know, founder of... uh, head of OpenAI said, "Hey, one of the first industries that's going to be disrupted is customer service." We're like, "Yep." So we, we did. We totally changed how we think, how we work. And we just went kind of heads down, and built a product called Fin. We built other things first. Actually, Fin came later, no- now that I think about it. But we just went, we kind of went all in on it. It was a little bit of a bet the farm kind of mindset. So we've done it. Uh, I think other companies like Google or 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, they're fine. Um, so, you know, it- it... Yeah. We, we- you just have to. We did. It was hard, but we had to do it.

11. 25:13 – 26:45

    Intercom’s AI chatbot, Fin

    1. PAPaul Adams25:13

    2. LRLenny Rachitsky25:13

       Can you share briefly what Fin is? Just for folks that aren't familiar.

    3. PAPaul Adams25:16

       Fin is... Uh, our first and foremost is an AI chatbot. So if you think about customer service, you know, people have questions, uh, 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 email, and, and kind of so on. And then came along conversational customer support, which is just basic messaging, like, like WhatsApp or iMessage like I mentioned earlier. Now, there's like, you know, bot-first experiences and Fin is an AI chatbot. Uh, AI first, chatbot first. So the first line of defense for a customer support team is Fin, not a person. And so it fundamentally changes. And Fin can do... Our... The results we've seen with Fin are like mind-blowing. Our biggest challenge is actually trying to help customer support teams think about organizational change. You know, it's not a- 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 Fin does. Uh, our managers. So anyway, that's what Fin is. Fin has expanded, so Fin is now also in our Intercom inbox, the place that people answer queries, customer support queries. And now Fin's in there too, helping the support reps, like suggesting answers for them to use, or h- helping them, like, rephrase things or... So it's, it's now augmenting people as well as answering questions

12. 26:45 – 28:53

    The early impact of AI adoption at Intercom

    1. PAPaul Adams26:45

       by itself.

    2. LRLenny Rachitsky26:46

       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?

    3. PAPaul Adams27:05

       Uh, it's very early, honestly, to be able to answer that properly. And it depends what you measure as, as success. So, um, again, there's a lot of hype and buzz with AI. So if- if you're measuring it by, like, um, interest, that's- it's a huge success. You know, a lot of people... Like our- 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 of... 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- and, you know, like in- 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 sh- humanity, you know?" Uh, 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 Fin. So, like, we have 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... A- a- all- I think all fast-growing startups, you know, if you think of Intercom as, or like AI Intercom is, I guess, a new startup, even though we're 900 people, you know, the kind of growth curve you're looking for is kind of exponential curve as opposed to, like, big public company kind of linear growth curve. With the exponential one, it takes a while. You know, the first kind of year, two years is to, like, bottom of that. And so I think we're still, we're still in, in the, like, trying to figure out exactly what's going on, trying to, trying to talk to- educate people. But, you know, we, we have enough evidence to believe it's the future, for sure.

13. 28:53 – 34:27

    Capabilities of AI

    1. PAPaul Adams28:53

    2. LRLenny Rachitsky28:53

       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"?

    3. PAPaul Adams29:02

       I go- I kind of go back to that, like, before-after thing. So ChatGPT, the first version of ChatGPT was a before-after, where we, 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 that you'd have a... There's a lot of manual setup. Like, uh, you know, a customer support manager would have to, like, orchestrate the bot, and, like, teach it what to say, and, like, you know, um, 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 a lot of these basic things. So that blew my mind. And then that was just, "Oh, it can answer questions." But then you're like, "It can reason." Oh, and there's actually, like, a debate about whether it- is this reasoning or deduction or... You know, but it, it can, like, work things out. And I'm not one for going down into these, like, really ph- philosophical things. Like, I'm like, "We just need to get- build a... 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 of st- we fed ChatGPT, um, and other companies too, like, we played with, you know, other LLMs like Anthropic 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. 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, a kind of a 1:5 ratio. So like a PM ha- has about five engineers on a team. And you're looking at this thing writing code, and you're like, "What happens next?" Y- 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, the visual stuff, like I mentioned earlier, 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 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 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 did it do, how'd it do that? You know? This is one I, I'm actually repeating most to people these days. Um, here in Ireland, if you want to be a radiologist, you know, so like study X-rays and, uh, you know, tell people what's wrong and so on and so forth, it's seven years training to, like, learn that, learn that skill. It's seven years to be a radiologist. And then you're just kind of just into the job. AI, it, it seems, is already better at it. So, it, uh, it's already better at it, and it can ingest every X-ray ever made. Uh, 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 they 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 ha- here in Ireland th- we have like long queues for hospitals. Epic waiting lists for, for people getting X-rays. So like, this is a really good thing possibly for people. Here's the craziest one I have. Um, AI can, 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, where you're like, "Th- that sounds like Paul." And so I mentioned that, the metaverse earlier. I don't know if you saw Zuck talks to-

    4. LRLenny Rachitsky32:33

       Mm-hmm.

    5. PAPaul Adams32:33

       ... Lex Fridman to see that.

    6. LRLenny Rachitsky32:35

       Yep.

    7. PAPaul Adams32:35

       So that was my first like, "Oh." Like, so it's the me- you know, for people who haven't seen it, they met in the metaverse, I think, or some virtual world. (laughs)

    8. LRLenny Rachitsky32:42

       Yeah, it was like a black-

    9. PAPaul Adams32:43

       And they love-

    10. LRLenny Rachitsky32:43

        ... a black room.

    11. PAPaul Adams32:44

        In a black room, yeah. And the tech has come on so they can analyze your face and, you know, m- build a 3D model. It's really good, like really, really close. So that, you can imagine, that's gonna get better. Based on the trajectory of that technology, it's gonna 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, you know? And so like, there's an afterlife. Y- y- uh, hey, you know, like your, your parent dies and you're, you can still talk to them. And like, that 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, it's k- kind of like my, you know, questions, mind-blowing, it's mind-blowing. (laughs)

    12. LRLenny Rachitsky33:35

        There's actually a Black Mirror episode with that same premise, where, uh-

    13. PAPaul Adams33:38

        That's right.

    14. LRLenny Rachitsky33:39

        Yeah, and I don't think it ended well. So maybe-

    15. PAPaul Adams33:41

        No, I, I like-

    16. LRLenny Rachitsky33:43

        ... we should be careful.

    17. PAPaul Adams33:44

        For sure, for sure. Yeah, uh, it is, uh, like the mar- uh, Minority Report and like-

    18. LRLenny Rachitsky33:49

        Mm-hmm.

    19. PAPaul Adams33:49

        ... um, 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.

    20. LRLenny Rachitsky34:05

        Mm-hmm.

    21. PAPaul Adams34:06

        You know, it's being, your, your voice would be translated into Spanish in real time for me. It's like, again, mind-blowing.

    22. LRLenny Rachitsky34:11

        We're actually working on dubbing/translating podcast episodes, which is all done through AI, where it figures out what you're saying-

    23. PAPaul Adams34:17

        Oh, wow.

    24. LRLenny Rachitsky34:18

        ... makes it in s- Spanish, and then also changes your lips to match. And we're trying to launch a couple of those. And that's actually-

    25. PAPaul Adams34:23

        Wow.

    26. LRLenny Rachitsky34:23

        ... very AI based, yeah.

    27. PAPaul Adams34:25

        That's cool.

    28. LRLenny Rachitsky34:26

        Um-

    29. PAPaul Adams34:26

        That's pretty cool.

14. 34:27 – 37:57

    How to structure teams around AI products

    1. LRLenny Rachitsky34:27

       S- you mentioned that your eng team might change your 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, and should change?

    2. PAPaul Adams34:52

       Ultimately, you need, like, really great machine learning engineers. Like, y- that's re- that's where it starts. And if you don't have that, then, um, you know, you're gonna find it hard to build truly really, truly great things. You know, so like, uh, what OpenAI provide, um, and what Anthropic provide, and, uh, you know, Claude is another one, like they, they provide like amazing, an amazing technology. But you gotta build on top of it. If you really want something brilliant, you gotta build on top of it. So like we adapted what they built for customer support. Um, 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. Um, and maybe everyone will have their own LLM for every single business. I d- I don't really know, to be honest. Maybe these companies will provide specialized LLMs. But anyway, that's like kind of the first thing. And, of course, these people are in high demand. So, uh, so you need to, like, invest in building out that function, I think. Really invest in building out the functions. So that's what we've been doing. Uh, the f- you know, our, our kind of like ML team's way bigger than it, uh, was, and way bigger than it ever has been at Intercom. And then, kind of, i- it forks. So some projects are very heavy on that ML team, and, and it needs them. But other projects are more front end. Like, the inbox stuff I mentioned earlier, where, you know, we have Fin, and Fin 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 that is 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 then you're kind of back into like, okay, we have a product team that's like a product manager, 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, we've, we now have both se- 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. So that's kind of one thing. Uh, I think a second thing that comes to mind is, um, not to think about it as bolted on. You know, I think some people are still in that camp. Um, like, again, I go back to the mobile thing. Uh, just there's, there's just so much, so, 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 were like, "Hey, we're the mobile team in London." And meanwhile over in Mountain View in California, no one cared. You know? (laughs) It was like, it was like, "You're 20 people. We're 200. No one uses this stuff on a phone. No one..." And again, a lot of skepticism. "No one's gonna write docs on a phone. Ser- seriously, they're gonna write a document? They're gonna write a full document on a phone? Are you crazy?" You know, so, um, so don't do that. You know, we're, we're trying not to do that. Like don't bolt it on. Don't be like, "Oh, we'll 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.

15. 37:57 – 39:04

    Why all teams should be involved in AI

    1. LRLenny Rachitsky37:57

       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 gonna add AI to all this stuff. You're finding and le- lesson is integrated into every product team.

    2. PAPaul Adams38:12

       Yeah. And we're still early there, you know? We're still early. So like, um, what we're trying not to do is have like the, the kind of like AI inbox team who o- and they're the only people that work on AI features in the inbox. I think it's much better to have everyone learn about it. I, by the way, I'm a big believer in s- in generalists. Like, a big, big believer in like, um... I mean, I guess my background is like, you know, Jack of all trades, master of none (laughs) is probably how I describe myself. Uh, like I've worked as a researcher, designer, PM, um, and so I believe in generalists. And so I believe in setting teams up that way. And yes, specialism matters at times. Like machine learning for sure is a deep specialism. At Intercom, we generally much, and engineering too, uh, much prefer people who learn new things, whether it's like a new la- new coding language or framework or, you know, how to design AI interfaces or whatever, get more people being able to do

16. 39:04 – 42:44

    Staying up to date on emerging technology

    1. PAPaul Adams39:04

       it.

    2. LRLenny Rachitsky39:05

       I feel like, again, your company is a little bit living in the future, where a lot of companies are gonna 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 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.

    3. PAPaul Adams39:27

       Yeah. What have I mentioned so far? Don't, yeah, don't bolt it on. Um, don't keep ... like stay up to date. You know, like re- I mentioned like read, read. Like I feel like I'm behind all the time. It's moving so fast.

    4. LRLenny Rachitsky39:36

       What are you reading? What do you find is most interesting and informative for reading about what's happening in AI?

    5. PAPaul Adams39:42

       I'd love to tell you that it's incredibly structured and, you know, I have a great reading list that I g- read. I go through every Sunday morning. (laughs) It's, uh, it's pretty random. I'm on Twitter, which is now called X of course, uh, a lot. I follow some people on Twitter. Um, 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 j- try and generate more stuff. I'll search Twitter as well. I think there's loads of cool stuff there. There's some newsletters as well and some people to follow. Um-

    6. LRLenny Rachitsky40:12

       Any newsletters you could call out that you think were, are most interesting?

    7. PAPaul Adams40:15

       Yeah. Matt Rickard is one guy, um, who, who talks a lot about AI. The blogs of companies too. Like, you know, OpenAI have a pretty good blog. Um, and they write papers and summarize them.

    8. LRLenny Rachitsky40:27

       Cool. Well, 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.

    9. PAPaul Adams40:34

       Yeah, perfect. Yeah, yeah. There, there definitely is. I'll dig them out. Your question earlier, how do you do it? You just try. Try to book out half an hour and just go deep for half an hour and then bookmark a few things, come back to them in a, uh, like, like everyone, like, you know, you can be so busy, so many distractions. You just got to have some set aside time.

    10. LRLenny Rachitsky40:50

        Are there any other tools or apps that you find really helpful? Sounds like ChatGPT is kind of at the center of how you play around with it. Is there anything else that you find really interesting?

    11. PAPaul Adams40:59

        I try other things like Bard. You know, for example, at Google, Bard is Google's kind of AI search engine. Uh, Rewind is another like fascinating company. Rewi- I think it's rewind.ai. Um, Rewind is basically augmented AI for your memory. So you install it on your hard- on your, like, local machine and it captures everything and remembers everything. And it's all local, so there's no privacy issues. And, uh, you got to try these things to understand whether it's any good or useful, or where is the boundaries and how does it work and, um, and so on. So I'm a believer in, in that type of thing.

    12. LRLenny Rachitsky41:35

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17. 42:44 – 45:52

    Hurdles implementing AI at Intercom

    1. LRLenny Rachitsky42:45

       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, uh, 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?

    2. PAPaul Adams43:00

       You know, l- like any company, Intercom is full of diverse opinions about things, you know. And I think with AI, you know, I'm like, "I'm all in." I'm not talking about... I'm all in. Like, I'm leaning forward. The meteor is coming. Like, uh, I'm sold, you know. I'm pa- 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, Eoghan, you know, our 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 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, "How do you know? How do you... Why are you all in?" And I'm like, "Uh, an educated guess? A hu- a hunch?" You, you know a lot of it's, a lot of it's like the pa- the, kind of, the, the part of, like, business strategy and product strategy that you ju- it's just har- it's just, kinda... 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, it's judgment based on experience. That's all I can say. Like I, 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 li- I lived through that phase. So I can see the same type of thing happening now but bigger. So I'm, kinda like, u- 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 have a lot of debate here about the future, you know. Fergal, who I mentioned earlier, gave myself and a few other people, uh, a few of the product leaders, and Des, he gave us like a... I don't know, was it a pitch or what? A plie? I don't know, about how maybe all of our roadmap with AI is wrong. M- maybe we're like, kind of... I don't know if you, I don't know if you think or are familiar with the Horizons framework, like Horizon 1, 2 and 3.

    3. LRLenny Rachitsky44:54

       Mm-hmm. Yeah, Amazon.

    4. PAPaul Adams44:56

       Uh,

    5. NANarrator44:56

       more like 2020. Yeah.

    6. LRLenny Rachitsky44:56

       Yeah.

    7. PAPaul Adams44:57

       So like Horizon 1 is, yeah, kind of, the medium, 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?" Or I think people use different time frames for different Horizons. Anyway, we're like in Horizon 1 land. We're like, "Yeah, in the next year we're gonna do this." And he was 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, um, and, like, useless." And you're like, "Oh, okay." Y- you know. (laughs) And so, (laughs) and so, like, those discussions happen. And, uh, and it, the, 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, um, alternative voices and opinions, which, which are often valid

18. 45:52 – 49:52

    Building conviction around AI

    1. PAPaul Adams45:52

       too.

    2. LRLenny Rachitsky45:53

       What has helped people get that conviction? Is it just showing them examples of here's something... "Wow, look at this thing, this is unreal." And I think part of, partly what helps, I imagine, is the market you're in seems like such a clear opportunity for AI, feels like an easier pitch than maybe a lot of other markets?

    3. PAPaul Adams46:09

       Yeah, that's true, for sure. That's true. Yeah, I, t- yeah, showing people is definitely, like, the easiest way. I, I think, yes, the customer support is definitely the... I, I, you know, like I said earlier, Sam Altman's like, "Number one, customer support."

    4. LRLenny Rachitsky46:22

       (laughs)

    5. PAPaul Adams46:22

       Uh, so you're like, "Okay." (laughs) "I guess we should adapt." Adapt, yeah, we... Adapt or die is kinda our mantra, adapt or die.

    6. LRLenny Rachitsky46:29

       Mm-hmm.

    7. PAPaul Adams46:30

       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, kinda like, you know, read-write app, build dashboards, filtering, querying, you know, kinda hardcore querying, kinda 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, different types of charts that are appropriate. Um, help people make good decisions ultimately. I, 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, um, "What was our best salesman last year?" "January." "Okay. Uh, who was our top performing rep in January?" You know, "Lenny." L- like, the reporting products of the future might look like that. And so project management tools is another one. There's a bunch of products that I think are just outside the most obvious customer support one and, and yet equally ripe for a newcomer to come with a completely different paradigm and potentially take over.

    8. LRLenny Rachitsky47:45

       I like that this connects back to your very first point about trying to think about where AI integrates is 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?"

    9. PAPaul Adams48:06

       Yeah. And it's very hard to... You know, if you're... I don't know if the reporting thing will play out that way, but you know, if you're like a, a Tableau type company, you have tons of designers who design dashboards and filters and querying type, like, workflow. Like, what do they do? The UI is the box, y- y- you know? So it's really hard to... It's, it's really hard to get into your head like, we must... If you believe, and if you have conviction that we must change, really hard.

    10. LRLenny Rachitsky48:33

        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?

    11. PAPaul Adams48:49

        I also try and read things that say, like, it's all a load of crap, you know (laughs) ? You know? So, like, it, it's very easy, I've been guilty of 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. A- and I feel the older I get, like the Web3 thing, I'm like, "I don't even know what Web3 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 e- 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, 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? So I try and I try and, like, balance my optimism. I, I'm kind of a delusionally optimistic thinker so I try and balance out with the negativity (laughs) I guess. You know?

    12. LRLenny Rachitsky49:50

        That's really good advice.

19. 49:52 – 50:56

    Why you shouldn’t fear AI

    1. PAPaul Adams49:52

       Yeah.

    2. LRLenny Rachitsky49:52

       Is there anything else in this realm that you think might be useful to share before we shift to a different topic?

    3. PAPaul Adams49:58

       Oh, yeah. The only other thing I was... is don't be afraid, um, m- 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 only need two engineers per team going forward," that's probably not really a good idea to do that, you know? And, and I think in reality that's not gonna be how it plays out. Like, there's all sorts of, like... You know, there's loads of great studies over the years about how people don't end up losing jobs. Um, 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 gonna lose their job. A bot's gonna take over," it's, like, m- maybe some of that will happen but probably through attrition, as in, like, people... someone quit and just didn't get backfilled. So, you know, the, 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, um, and I think you kind of have to lean into it.

    4. LRLenny Rachitsky50:54

       I love that.

20. 50:56 – 51:54

    Paul’s “before-after” framework

    1. LRLenny Rachitsky50:56

       Okay. I wanna chat about frameworks. We 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 a- you actually mentioned this before and after, which I hadn't heard about, is what's, what's the general idea to that concept?

    2. PAPaul Adams51:14

       Before/after, it, it, it is, 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 'cause it was, like, noth- yeah, 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 gotta talk to customers. It's the only way. You gotta 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 the, once you've passed the a- into the after, you've gotta start learning were we right, were we wrong? What happened? What do people think, you know?

21. 51:54 – 54:54

    Pricing lessons from Intercom

    1. LRLenny Rachitsky51:54

       Can you talk more about this pricing learning/mistake you shared? What do you think you did wrong? What happened there?

    2. PAPaul Adams52:00

       You know, we had a principle called, um, align price to value. By the way, I, uh, I, like, I think pricing is incredibly difficult. Uh, l- a lot of the des- a lot of the design team who work in pricing here, you know, I say to them, like, "It's one of them hardest design problems I know." Like, I think onboarding is another one. Onboarding people into a product is also... L- like, people are like, "Oh, hey, you just design a few steps and it's pretty easy. People follow the steps." Again, like, deceptively difficult to 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 product. Uh, easy to say and incredibly hard to do. Value is subjective. The price is s- people's... You know, s- for some person, you know, they get, like, ten units of value, they're like, "I think that's about $5." Someone else is like, "I'd pay $5,000 for those ten units of value." You know (laughs) ? So the biggest mistake was we c- a lot of mistakes compounded and this is an area where I d- I think we were risk averse. We ended up, we've ended up with too many pricing models. We've built on top of old... you know, com- competitive mistakes and it took a brave decision to say, "We're gonna start again."

    3. LRLenny Rachitsky53:18

       Wow. This feels like it could be its own episode, just talking through your pricing-

    4. PAPaul Adams53:22

       (laughs)

    5. LRLenny Rachitsky53:22

       ... 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, your experience?

    6. PAPaul Adams53:31

       Uh, the number one thing I would, I, I would say is keep it simple.

    7. LRLenny Rachitsky53:34

       Mm.

    8. PAPaul Adams53:34

       Keep it simple. It's so, it's so tempting to, like, like, with us for example, a lot of SaaS products, you know, have add-ons where you're like, "Hey, you know, we, we, we built X and that's, like, 10 bucks." Or, "100,000," depend- what kind of product you're selling. "We built X and that's the price of X. Hey, we've just got 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 'cause it's a new thing they didn't have. So let's charge, like, more for Y." "Uh, wha- but that doesn't really work with the other... Okay, let's make it an add-on." Oh, yeah. Cool. 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, "Can you add another thing?" And so, like, tiering, we've, like, added tiers. We've, like, you know, co- diff- with products, tiers, add-ons, tiering in the add-on. Like, oh, my God. You know? People can't understand their bill. So my advice is keep it simple. Reject, like, fight so hard to not... to, like, resist the temptation to-... add extra ways in which you price.

    9. LRLenny Rachitsky54:43

       Amazing. Uh, I didn't think about going into this topic, but I'm glad that we touched on it. (laughs)

    10. PAPaul Adams54:48

        (laughs) Yeah. I was talking about scars for life earlier, that's another scar for life.

    11. LRLenny Rachitsky54:53

        Oh, man.

22. 54:54 – 59:22

    Paul’s “differentiation vs. table stakes” framework

    1. LRLenny Rachitsky54:54

       All right. 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?

    2. PAPaul Adams55:03

       It's kind of like the Kano model, if you're familiar with that. But-

    3. LRLenny Rachitsky55:05

       Mm-hmm.

    4. PAPaul Adams55:06

       ... it's very simple. It's kind of like, uh, I guess we took the Kano model and tried to make a really crazy simple version of it. Again, like I, I'm a little bit allergic to things like this. I kind of even hate myself for bringing up the Kano model. I'm allergic to, like, people over-intellectualizing frameworks, and like, you know, "Oh, well have you seen the new different law of whatever?" I'm like, keep things simple, practical and pragmatic, and then let's all, a- again, go back to work and start building the product so that customers can benefit, 'cause that's actually all that matters. Um, and so, s- difference versus table stakes. Very simple. I think people who, uh, 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, you know, that's f- basically differentiation. What, 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 a- in these Google projects in ways people didn't care about. You know? Like, um, well all sorts of Google projects, like Google Wave was an amazingly innovative product that no one really cared about. So be, be different and better in ways people care about. So that's the attraction, that's like, "Oh, I wanna check out that. That looks cool, I wanna check that out. That looks better than what I have today." But on the other side, there's like a kinda entry requirement, or like table stakes. You know, to, to play the game, you gotta have a certain amount of things. And so there are 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, of a, 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, 'cause 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 b- like trading off where we need more differentiation or trading off where 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 roadmap or look at a proposed roadmap and ask yourself, "Which of these thing, two things matters more to us?" And, uh, not to us actually, to our customers right now. The other thing that, 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, big established category, massive. A lot of table stakes built up over years, decades. You know, you know, ServiceNow, Service Cloud, Salesforce, Zendesk, like decades of table stake feature building. So to play the game, you need a, a lot of the table stakes, unless you have incredible differentiation. So from the early years of Intercom, people just buy us alongside Service Cloud or Zendesk. They just buy us alongside. They're like, "This Intercom thing." Like, we were like messenger first, modern messaging, modern UX. They were like, "We want that for our customers, alongside the big giant bag of table stakes, 'cause 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, and then hence kind of dev- 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.

    5. LRLenny Rachitsky58:47

       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 a l- 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, like differentiate first, we'll build everything else later? Wonder when it makes sense to go one or the other.

    6. PAPaul Adams59:15

       Yeah. Uh, and it probably depends on-

    7. LRLenny Rachitsky59:17

       The market?

    8. PAPaul Adams59:17

       ... uh, different cate- different categories-

    9. LRLenny Rachitsky59:18

       Yeah.

    10. PAPaul Adams59:18

        ... and all sorts of things. Yeah.

    11. LRLenny Rachitsky59:20

        Yeah. Awesome. Okay.

23. 59:22 – 1:05:21

    What “swinging the pendulum” means and examples from Intercom

    1. LRLenny Rachitsky59:22

       The next framework is, uh, something that you call swinging the pendulum.

    2. PAPaul Adams59:26

       (laughs)

    3. LRLenny Rachitsky59:26

       What is that about?

    4. PAPaul Adams59:28

       Uh, I actually kinda mentioned an example of it earlier.

    5. LRLenny Rachitsky59:30

       Mm-hmm.

    6. PAPaul Adams59:31

       Um, like the diff- you know, differentiation and table stakes was swinging the pendulum. So swinging the pendulum means you take a step back from, uh, everyday work life and you kind of make the observation that something's in an undesirable state. So like, you know, maybe it's we t- well, we've all the differentiation in the world, but people can't adopt the product because we've never built any of these table stakes. 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, 'cause switching product is like a pain, and we're not, just not a- attractive to people. We need to like... Okay, so this undesirable state. And then so you go and fix it, uh, but the temptation is that you over-correct. 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, we don't, like we're missing all the table stakes. Okay, phew, we're over there, you know? So product building is one. People is another one. Building out teams and people. Like another big one was, uh, maybe, I don't know, maybe five years into Intercom, we were, you know, we were on this kinda like high, high growth trajectory, really kinda good classic startup, uh, before our pricing problems. And, um-

    7. LRLenny Rachitsky1:00:44

       Mm-hmm.

    8. PAPaul Adams1:00:45

       ... we kinda 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? We be, we need to hire some experts. We need to hire some experts. Like, you know, if we're gonna 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, and what they did was brought the culture and ways of working of their prior company to Intercom. And so when we totally over-corrected, didn't work out for, for, in a lot of cases. In most cases it didn't work out, 'cause we weren't, we weren't trying to be a bigger company that already exists. We were trying to be us, you know? So, um, th- I think hiring, hiring and building teams was another where we really over-corrected to find out, like, "Okay, it's a balance here." Related to that one, related to hiring one is, like, generalists and specialists, kind of similar theme, people who've done it before, or people who are specialized. And we hi- we hired a bunch of specialists, specialists only to realize that they're not adaptable. And in Intercom, you know, we are, 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-y 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, uh, 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.

    9. LRLenny Rachitsky1:02:34

       First of all, I love all these stories of things that didn't work out, 'cause 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."

    10. PAPaul Adams1:02:42

        Yeah.

    11. LRLenny Rachitsky1:02:42

        "Here's a lot of mistakes that were made along the way." And feels like this framework is 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, it was worth going all in. Is there kind of a... I guess, yeah, what do you, what do you think of when I sh- when I say that?

    12. PAPaul Adams1:03:04

        In talking to people about this before, sometimes the conclusion of the conversation is something like, "You, it's the only way to do it. Like, you actually can't do it a different way." Uh, and, and so maybe the question's really like, "How ext- how high up, how high does the pendulum go?" Versus like, "You gotta swing it." And then it's like, "How far do you swing it?" And sh- for sure you're right. With AI we are like... We're actually, we're, we're swinging pretty high. Maybe I overe- overestimated it earlier. Like, you know, if AI is like in a differentiation camp to kind of mix the frameworks, we, we're still building a lot of table stakes features too, like building depths 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, we're not totally swinging it. We're not like... You know, it's, it's swung but, uh, we're also kind of doing the other thing and balancing things out. So, uh, I think you probably have to swing it. Uh, it reminds me, to know where the boundary is is what I was going to say.

    13. LRLenny Rachitsky1:04:02

        Mm-hmm.

    14. PAPaul Adams1:04:02

        Remind, it reminds me of a story, you know, kind of, uh, like back to the olden days stories.

    15. LRLenny Rachitsky1:04:07

        (laughs)

    16. PAPaul Adams1:04:07

        I remember when 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 their brand. But the, there was a, there was, um, kind of an idea that to know where the boundary is, you gotta 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, 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've 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 ac- corrective course is.

    17. LRLenny Rachitsky1:05:12

        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.

    18. PAPaul Adams1:05:18

        Yeah. Yeah.

    19. LRLenny Rachitsky1:05:20

        Okay.

24. 1:05:21 – 1:08:23

    Paul’s “product market story fit” framework

    1. LRLenny Rachitsky1:05:21

       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.

    2. PAPaul Adams1:05:31

       Yeah.

    3. LRLenny Rachitsky1:05:31

       What is that?

    4. PAPaul Adams1:05:33

       So yeah, with product/market fit, pretty basic, well understood, very important. You know, the way I describe product/market fit is you 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, 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, 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, but they didn't really care. They didn't really care. Like, you know, what they had was fine. Uh, so like, a lot of people with the same problem and a lot of energy around the problem, and the product is the solution to that. You know, it's the what. If that's the, if the market's the who, the product's the what. And, I just, I don't know, in my career again, saw a bunch of products that were built that 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, "Uh, well, it's marketing. Is, you're talking about marketing." (laughs) But like, story. The story's 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, I used to see that a lot at Google, again. Just explained in a very complicated way, over-intellectualized. And, and as a result, people are like, "I, what? What are you talking about?" You know, you don't get their attention. And so the story is really important, as important. And, and actually sometimes you'll see like not great products, uh, certainly worse on paper. I'm trying to remember like the Spotify competitor back in the day, people who were like... Oh, what was the name of it?

    5. LRLenny Rachitsky1:07:19

       Audio?

    6. PAPaul Adams1:07:20

       Yeah, Audio.

    7. LRLenny Rachitsky1:07:21

       Mm-hmm.

    8. PAPaul Adams1:07:21

       Audio was one of these for like-

    9. LRLenny Rachitsky1:07:23

       I like Audio a lot.

    10. PAPaul Adams1:07:23

        ... yeah, people were like, great. Like, um, people, all I've ever heard of Audio was amazing product.

    11. LRLenny Rachitsky1:07:29

        Mm-hmm.

    12. PAPaul Adams1:07:30

        Uh, it's failed, you know? And why did it fail? Spotify and Audio had the same market, they were solving the same set of problems. Audio was arguably the better product at the time. I don't know as that, if that's true, but arguably the better... I also think Spotify is an incredible product, so. But, you know, the story, they, there's, they got the story wrong. And so again, I think all product people, whether you're a designer, product manager, people in research, data science, need to think about the story all the time. Work with marketing, work with product marketing, and like learn about how to explain the product as much as how to build the product.

    13. LRLenny Rachitsky1:08:03

        Mm-hmm. Makes me think about positioning and how important that is. And we had, uh, April Dunford on the podcast very recently-

    14. PAPaul Adams1:08:11

        Oh, yeah.

    15. LRLenny Rachitsky1:08:11

        ... talking a lot about that.

    16. PAPaul Adams1:08:12

        Yeah, yeah. Yeah, she's excellent.

    17. LRLenny Rachitsky1:08:14

        She's amazing.

    18. PAPaul Adams1:08:14

        Yeah, it, it, it is really the, the like, why are you better? You know? And can you explain why you're better?

    19. LRLenny Rachitsky1:08:21

        Uh, that's such an important point.

25. 1:08:23 – 1:11:01

    His take on JTBD

    1. LRLenny Rachitsky1:08:23

       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, uh, Shriram 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 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 works, doesn't work? Whatever comes up.

    2. PAPaul Adams1:08:47

       Yeah. I'll be totally honest, at the risk of offending people if they listen. Uh, like I, like we worked with Bob Westa, you know, who, who, who's, um, age years ago, I think Bob's a great 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. Uh, very, very useful, but in a very simple way. Again, back to this idea-

    3. LRLenny Rachitsky1:09:11

       Mm-hmm.

    4. PAPaul Adams1:09:11

       ... of like 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 just, and, and p- and peculiarities of one version of... Uh, who cares? Who can... Like, no one cares. Oh, well, I don't care. They care, obviously. But I, I, I'm like cus- your customers don't care. Like, people you're trying to build a product for don't care. No one cares. That's like a cool intellectual debate. But like, and it, uh, kind of for me, maybe this is too extreme, it gener- it doesn't really have any place in work, you know, like in the, in, in the work we do. We're just trying to build like a great, great product. And so for us, the 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 are 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've come from talking to Bob actually, now that I think about it. I think it did actually, I think like the idea of like this idea that, um, 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, so we found it pretty good and, and we, we invented this job stories thing kind of by accident. I, I can't remember exactly what happened, but like I wrote out this way of writing a job story bas- Well, we didn't call it job stories, someone else c- called it that. We just, at the time were like, there was this... I can't even remember, you know, it was like a trigger and an i- Anyway, um, 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.

26. 1:11:01 – 1:12:54

    How Intercom uses the “four forces” framework

    1. PAPaul Adams1:11:01

       And then the other one that we use a lot still here is, um, uh, the four forces, which is just like framework out of Jobs to Be Done. Uh, the four forces being like, um, different for people, there's different forces when people try and switch product. And some of it's the differentiation table stake stuff, like the attraction of the new solution, the reasons that you might not adopt it, habits, people have anxieties. Or like here's another kinda like funny story, uh, to tell you how much the four forces is really good. Here's a, here's a funny story. I was saying earlier that like Owen and Des were trying to convince me to leave Facebook, which I loved at the time, join Intercom. They, uh, 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 (laughs) uh, whatever, like, and like, you know, basically worked me on the four forces. And I was like, "That is, that is genius. That is ingenious." Maybe it's a bit, you know, but it's ingenious. Uh, and so it's in- just, the four forces is incredibly good at, um, helping understand why people make decisions.

Episode duration: 1:23:00