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

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's gonna be yes, it can. For some, it might be it can partially do it. And then maybe for others it can you know, it can't do that, at least not yet. And then for some of it, it'll be like kind of replacement. AI will replace, it'll just do it. And then, you know, in other places it'll be augmentation. It'll augment, it'll help people. But yeah, I think that you got to map your product and what AI can do and what it will be able to do. And 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. 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. Paul, thank you so much for being here and welcome 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 bringing that with you here.

Yeah, that's nice to hear.

I wanted to start with a couple of stories. So the first is your story of giving a keynote at Cannes. Can you share what happened there?

Yeah, some things happen in work every member at the time, they don't really scar you. This goes in the book that have scarred for life. Yeah, it's a long story short. I was at Facebook. just over a decade ago, loved it at the time. I think it was a great place to be at the time. And based in San Francisco, I did a lot of talks for Facebook internally and externally. Facebook had a keynote slot, always have a keynote slot at Cannes, the world's biggest advertising festival. And the year prior, Zuck had been interviewed. He was the speaker. He'd been interviewed, gotten a hard time on privacy. It didn't go 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. And yeah, I got out in front of the stage, you know, the world's biggest advertising stage. And I'd say I was like three, four minutes into the talk. A talk I'd given, a very similar talk to what I'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 in my life I'd rehearsed to talk word for word. you know, usually, like, I have talking points, and I'm 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 not remember what to say. I had some version of a panic attack, walked off stage. I was still miked up, cursed. Everyone started laughing. I was like, she's already laughing at me, you know? Oh, my God, this is. But 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, and I was famous that night, you know, out in Cannes afterwards, like on the, 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 is like you could freeze in front of thousands of people, walk off stage and then it works out. Okay.

Yeah. And it all happened kind of, organically, I guess, or very naturally, you know, but yeah, ever since then, every time I walk, go out onto a conference talk stage still today, I ask, like, ask myself, I have this tiny doubt in the back of my head, like it's never happened since, but yeah, you just, I think you have to go with it with these things, you know, like when life kind of throws you these, whatever curve balls, you have got to kind of adapt. And it's not that big a deal. None of these things are that big a deal at the end of the day. You know, you kind of move on, live and learn. So, yeah, but I still hope it doesn't happen again.

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

You can turn it around, yeah.

A second area I wanted to hear from is your time at Google. And there's a couple of products you worked on at Google. Both of them were not what you'd call big successes. And then there's a kind of a transition to Facebook, which was also kind of messy. Can you just share a couple of stories from that time?

Yeah, similar to the kind of like, you know, walking off stage thing. You live and learn. And I was at Google for four years, and I was at Facebook for kind of two and a half years. 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 new social advertising unit that would be like an AdWords or something like that that would destroy Google's revenue, eat them from the inside out. And so being there at the time was fascinating and moving to new companies. At Google, I worked on a lot of failed social projects, like you mentioned, Google Buzz, then later Google+. I think a lot of the motivation for those projects came from a place of fear. It didn't come from a place of let's make a great product for people. Let's really understand the things people struggle with when communicating with family and friends. Let's really, really try and create something wonderful. It came from a place of fear. And so during those times, I kind of learned, I think, how not to lead in places. And by the way, I should say, at the time in Google, there was other things happening that were amazing, like Google were building Google Maps, an incredible product, one of my favorite products, I think one of the best products ever made. They were building Android. I was in the mobile team, in the mobile apps team at the time that Android came out. So I can make incredibly good products. So I just happened to be in the social side, which wasn't as good. Yeah, Google Buzz was kind of a privacy disaster and Google+ 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 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. Like, can people use these products? And I ended up doing a lot of formative research as well in the same session. So I'd kind of say to the team, like, Hey, I'll do the research. I'll answer your questions. But also, I'm going to do this other thing. I'm going to take 20 minutes doing that. And so what we 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, we'd map on all the channels, we'd talk about what worked well, what didn't. And we did this with dozens and dozens of people over the course of maybe 18 months. And the same pattern emerged every single time, which was people need way better ways to communicate with small groups of family and friends. And I kind of look back now and go like, what's up? Or it may be like iMessage if everyone's on Apple, but really obvious in hindsight, but at the time not obvious. And so we kind of tried to build a product around that called Google+. But again, it was kind of motivated from the wrong place. Halfway through the research that I'd kind of done, all this research had been made public through a conference talk and Sook 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. 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, okay, let's build an amazing product for people.

And this was during Google+ being built, you basically shifted.

Yeah, Midway. I'm stressed to even tell you that. The project hadn't been launched. It was still under wraps. It was highly confidential. Google had done a lot of things at the time that were the first for them. I don't know if they've done them since, but things like everyone worked on Google+ was sent to a different building. That building had a different key card. If you didn't work on Google+, you could not get in. All sorts of counter-cultural things. at the time. And as a result, there was a lot of, you know, antagonism internally for Google+. 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. Other people thought I was enlightened, you know, it's a fancy talk to. But it was, it was the right thing for me to do. But at the time, you know, it was, it was a hard thing to do.

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

Yeah, when I left, Google assumed that I was one of the spies. I was quarantined. I told them I was leaving. They forensically analyzed my laptop, all sorts of stuff like that. So it was pretty intense. Looking back, I can understand why that happened. But the root cause for me is that the project hasn't run from a place of fear, competitive fear, which I don't think leads to good things.

So one of the themes through the stories you just shared is, let's say failure 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 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, 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 stories, I failed a ton of times, like at Intercom. I remember like, you know, when I was at Facebook, I was very happy and, I knew Owen and us, who are the co-founders of Intercom, and they were trying to persuade me to join Intercom. It was a 10-person company at the time. But Owen said something to me at that time, which has stuck with me ever since. He said, At Facebook, you can design the product. But at Intercom, you can design the company. And that was extremely appealing to me, a great pitch. He's like, Just design the company with us that you want to work in. 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 big bets, high risk, high reward. I don't get as excited about incremental things. Now, having said that, there's of course a place for that too, especially as companies get bigger. But I get excited about big bets. And if you make big bets, you're going to get a lot of it wrong. So a lot of the principles that we built here at Intcom around building software, we have a principle called Ship to Learn. And we've actually changed it since over the wall here. Ship fast, ship early, ship often is what it says now. You say ship to learn, ship fast, ship early, ship often. It's like in that idea is the idea of failure. You know, you're gonna, it's not gonna go right and 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 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.

Yeah, especially when you're in the moment, like, God damn it, everything's going to fall apart. I really messed this one up.

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

Speaking of taking big bets and going all in, I know there's been a huge shift at Intercom to move towards AI and embrace AI. 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.

What day did ChatGPT launch? November 29, I think, last year. Ever since that day, I literally wake up every day thinking about AI pretty much, and I read as much as possible and still feel like I'm way behind in it. I think for me, when I talk to you about AI, people typically fall into one or two camps. You're either All in, like really truly all in. This is a meteor coming towards you. This is bigger than mobile as a kind of technology shift. As big as the internet, maybe it's bigger than the internet itself as a kind of social, you know, technology shift, the way it'll shape society. 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 it's people in another camp, which is, I've heard this before, it's hype. Like, you know, last year was crypto, you know, it was web three, like, none of those things worked out. It was the metaverse, you know, so there's definitely, I think, a lot of skepticism or maybe cynicism around it. And I can understand why, you know, the other things didn't really pan out. No, the metaverse is kind of, it has one that might be coming back, but. And I kind of think about, I'm trying to remember, there's a lot, the law where you have, like, the hype and then the trough of disillusionment and then you kind of come out the other side.

Yeah, a little curve chart.

Yeah, and I think that's where a lot of people might be, where like the hype, there was so much hype, it was so noisy and still is a little bit so noisy that you kind of tune it out a little bit. And some people have kind of fallen into that camp. I am all in on 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 ChatGPT Vision just came out recently and just seeing the things that people can do with it. And we're just scratching the surface still. So we're all in for sure.

Awesome. I want to unpack that. But I think there's also this camp of people that, yes, something big is happening. I just don't have the time to understand, to build, to play around. What have you found and or what advice would you share to people that are just like, I want to go deeper down this rabbit hole. I just don't know where to start because I have so much work to do already and this isn't like a side thing.

The advice I have for people and the advice I have for myself, you know, I'm in that too. Like, I wake up every 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. It doesn't mean that you need to work crazy hours. I don't believe in working crazy hours. I don't know what hours I work. I don't know 50 hours a week maybe. I think beyond that you start to make bad decisions and things like that. You get tired. I need to live the rest of your life. You got to put it into your day. Whether that's setting aside dedicated time to read. Reading is the thing. You got to read. You got to stay up to date and you got to play with things and try things. if you don't have ChatGPT, if you don't have a kind of a pro license or whatever, but if you haven't upgraded to get access to things like GPT-4 Vision, where you can take photos and you have the mobile app. And I got that for dinner last Friday with my wife. I try not to take work to dinner, 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 whatever. Anyway, if you don't, you got to try it. You just got to try it. So like my advice to people is you've got to 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 wave 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 behind. 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 mobile app or on a mobile web. They can no longer be desktop for now.

Right.

Yeah, I read that same with Facebook.

Yeah, I reckon that's right.

I guess do you think that 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 that you're thinking about or have done of just like convincing people this is where you.

Want to spend your time? Yeah, it's harder for sure. It's harder because- They don't want to force it. Yeah, a lot of the tech is invisible, you know, like a lot of the things, like we have a machine learning team we've had on here for a long time. So we've been working in the space for quite some time. But it's funny, even if you go back like 18 months, I think if I was on your podcast 18 months ago and you said to me like, Hey, what do you think about AI? I would say something like, It's not real. Machine learning is real. Let's talk about that. So things change and my perception of it's changed. But a lot of the improvements are kind of like behind the scenes. They were like large language models or different types of things people are building in the background of infrastructure. So I don't know what it looks like to design mobile mock-ups that are like AI mock-ups. But I do think that people need to start really thinking strategically I don't know, maybe it's just not a mock-up stage, but start to think really strategically about their product and whether it's in the line of the meteor that's coming or not. It's not everything is, and if so, for some, I think they require a kind of a foundational strategic change. Others it might be less so, but I think that's actually the headspace that I think people need to be in.

Can you unpack that further? 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 imagine, hey, going out for dinner and taking a photo of my food. 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. What's the core premise behind it? Why do people use it? What problem does it solve for them? That kind of thing. and then ask the question. So go back to basics. Okay, what is my product for? And why do people love it? And then ask, can AI do that? And for a lot, the answer's gonna be yes, it can. For some, it might be it can partially do it, and then maybe for others, it can't do that, at least not yet. And the types of things, you're gonna need to map what your product does against what AI can do, and AI can do a lot. It can write. I'll give you a list. It can write, it can summarize, it can summarize text, it can write text, it can answer queries, it can find facts, it can scan text, it can scan images, it can listen to your voice and repeat it, it can take actions. That's the next big thing coming. It can take actions, actually do things. It could like, hey, I mean, hey AI. whatever the AI is called. Change my flight. Change my flight to Tuesday. It can do things like that. It can 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 media line or whatever. I don't know if this metaphor is working, but the media is coming and they're like, in its path. And so for a lot of these products, 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 got to map your product and what AI can do and what it will be able to do, and then ask yourself, okay, what are we going to do?

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? Oh, yeah.

I'll give you Intercom first. Again, this date's kind of, I think it was never 29th. It etched in our head. We have Fergal, who was our head of machine learning. Fergal just turns around that day and he's like, okay, I think he tweeted something actually. He had a tweet that day that was like, this is it. This is the time, this is the moment, this is the before after. You know, like I actually often talk about people, there's a little framework I have, like before after moments. This is a before after moment. There 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, a customer support product. And then very soon after that, Sam Altman, who's the founder and head of OpenAI, said, Hey, one of the first industries that's going to be disrupted is customer service. 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 don't 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.

Can 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, people have questions for a business. And historically that was mostly email and phone and mostly ticketing based. So you'd file a ticket, a lot of do not reply email and so on. And then came along conversational customer support, which is just basic messaging like WhatsApp or iMessage, like I mentioned earlier. Now there's bot first experiences and Finn is an AI chatbot. AI first, chatbot first. So the first line of defense for a customer support team is Finn, not a person. And so it fundamentally changes. And Finn can do, the results we've seen with Finn are like mind blown. Our biggest challenge is actually trying to help customer support teams think about organizational change. 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 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 or manages. So anyway, that's what Finn is. Finn has expanded. So Finn is now also in our Intercom inbox, the place that people answer queries, customer support queries. And now Finn's in there too, helping the support reps, like suggesting answers for them to use or helping them rephrase things or... So 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. So I'm curious just to unpack a couple more things here. First of all, what kind of impact have you seen after going all in and going in this direction?

It's very early, honestly, to be able to answer that properly. And it depends what you measure as success. Again, there's a lot of hype and buzz with AI. If you're measuring it by interest, it's a huge success. A lot of people, our target customer is customer support, our customer support manager leader. And so they're very curious. They're like, does it actually work? There's a little bit, again, back to the earlier thing of there's so much hype, there's a bit skepticism around it? Does it actually work? Is it as good as a person? Hey, and you know, in customer support, people who tend to work in that role are typically very high empathy, care a lot about people. And so they're like, but is it as good as a person? Like, is it nice, friendly? Like, does it understand humanity, you know? And so there's a lot of curiosity and a lot of interest and a lot of people trying it. We have some customers who are hugely successful with it. They can answer up to 50, 60, 70% of their inbound questions would be Finn. So we've some customers who see huge success, but it's early. And so has it transformed our business financially? Not yet. It's not like this kind of, I think all fast-growing startups, if you think of intercoms or AI intercoms, I guess a new startup, even though we're 900 people, the growth curve, you're looking for this exponential curve. as opposed to big public company linear growth curve. With the exponential one, it takes a while. The first year or two years is the bottom of that. And so I think we're still in the trying to figure out exactly what's going on, trying to talk to educate people. But we have enough evidence to believe it's the future for sure.

Are there any examples of either this product or other instances of AI just kind of blowing your mind or just like, wow, I never imagined it would be this good.

I kind of go back to that like before after thing. So ChatGPT, the first version of ChatGPT was a before after where we had built, like we've been working, like I said, in this space, we've had a machine learning team for a long time. The way our machine learning thing worked before ChatGPT was, yeah, but there's not a manual setup. Like a, you know, 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 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. There's actually like a debate about whether is this reasoning or deduction or But it can work things out. And I'm not one for going down into these really philosophical things. We just need to go back building the product or whatever. But it can work things out. And that blew my mind. And we fed it a whole bunch of stuff. We fed ChatGPT and other companies too. We played with other LLMs like Anthropic and so on. It can work things out. And that was kind of mind-blowing. Then you can see it doing things like writing code. And I was like, wow, it's really good at writing code. What does that mean? And then you start thinking, like here at Intercom, we have a one to five ratio. So a PM has about five engineers on a team. And you're looking at this thing writing code and you're like, what happens next? Do we need as many engineers or will their role change? And they'll start doing different types of things like reviewing code instead of writing code. So that kind of blew my mind. And then the visual stuff, like I mentioned earlier, I think the visual thing was bigger than the original one. It can parse imagery and it can help you see the world. You take a photo of your bike and say, Hey, what's wrong? And it'll tell you what's wrong, how to fix it. You can be traveling, take photos of stuff. It's in a different language. It's etched in stone on a 12th century cathedral. You're like, what does that say? And it'll tell you what it says. It's just like, how to do that? This is one I'm actually repeating most people these days. Here in Ireland, if you want to be a radiologist, so like study x-rays and tell people what's wrong and so on and so forth. It's seven years training to learn that skill. So seven years to be a radiologist, and then you're just kind of just into the job. AI, it seems, is already better at it. So, it's already better at it and it can ingest every x-ray ever made. Like no human can ever read and think about and synthesize every x-ray ever made. So, of course it's better. And then you're like, okay, what happens now? I guess the whole job changes. 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 so, like, this is a really good thing, possibly, for people. Here's the craziest one I have. AI can listen to your voice and copy it. So, it can say things and it sounds exactly like you. And it's really, really good, like almost indistinguishable. We're like, that sounds like Paul. And so I mentioned the metaverse earlier. I don't know if you saw Zuck talked to Lex Friedman, see that? So that was my first like, oh, so it's the metaverse. If we haven't seen it, the metaverse.

I think, or some virtual world.

Yeah, it was like a black room. In a black room, yeah. And the tech has come on so they can analyze your face and build a 3D model. It's really good, really, really close. So you can imagine that's going to get better. based on the trajectory of that technology, it's going to get better. And so the voice thing and the face thing means both of those things are almost indistinguishable from a real person. And AI will be able to ingest all the things people say and do. And when people die, it'll be able to replicate that person. And so there's an afterlife. Hey, your parent dies and you can still talk to them. and 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 kind of like, you know, questions 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. Yeah. It is like the Minority Report and like, the voice translation thing is another one. I can't remember, maybe it's in Mission Impossible, where it can take a voice, translate it, and translate it in real time. And this tech is again just here, where if I was a native Spanish speaker and couldn't speak English, you and I could still have this podcast. Your voice would be translated in Spanish in real time for me. It's again mind-blowing.

We're actually working on dubbing slash translating podcast episodes, which is all done through AI, where it figures out what You're saying makes it Spanish and then also changes your lips to match. And we're trying to launch a couple of those. And that's actually very AI based.

Yeah. That's cool. That's where you go.

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 should change?

Ultimately, you need really great machine learning engineers. That's where it starts. And if you don't have that, then you've got to find it hard to build truly, really, truly great things. So what OpenAI provide, and what Anthropic provide, and Claude is another, they provide 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 we adapted what they build for customer support. Maybe someday we'll need to go build our own LLM that's just for customer support. Maybe, I don't know where that will all go. Maybe everyone will have their own LLM for every single business. I don't really know, to be honest. Maybe these companies will provide specialized LLMs. But anyway, that's kind of the first thing. and of course these people are in high demand. So you need to invest in building out that function, I think. Really invest in building out the function. That's what we've been doing. Our ML team is way bigger than it was and way bigger than it ever has been at Intergom. And then it forks. So some projects are very heavy on that ML team and it needs them, but other projects are more front-end. like the inbox stuff I mentioned earlier, where we have Finn and Finn is kind of working. We've built the underlying technology. Now it's a question of if you have a human support person answering questions in the inbox, that's like a natural chat kind of conversational interface, pretty straightforward. What happens when there's now like an AI assistant in there? How do they talk and what do they do and when do they interject and how do you represent that in the user experience that feels natural? So that's a really hard design problem. So then you're kind of back into like, okay, we have a product team that's like a product manager, product designer, maybe three, four, maybe five engineers, and they're getting help from the machine learning team. So we now have both setups. And increasingly, we can do more with the latter, more teams who can build on the foundational technology that we've been building over the last kind of 12 months or so. So that's kind of one thing. I think a second thing that comes to mind is not to think about it as bolted on. You know, I think some people are still in that camp. Like, again, I go back to the mobile thing. It's just, it'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're like, Hey, we're the mobile team in London. And meanwhile, over in Mountain View in California, no one cared. 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 a phone. Seriously, you're going to write a document? They're going to write a full document on a phone? Are you crazy? So don't do that. We're trying not to do that. Don't bolt it on. Don't be like, I would have a bunch of AI people, and we do have some specialists, but generally speaking, we're trying to 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 a side team that's like, they're the AI team. They're going to add AI to all this stuff you're finding unless the lesson is integrated into every product team.

Yeah, and we're still early there. We're still early. What we're trying not to do is have the kind of AI inbox team, and they're the only people who work on AI features in the inbox. I think it's much better to have everyone learn about it. By the way, I'm a big believer in generalists, a big, big believer in, I guess my background is Jack of all trades, master of none. That's probably how I describe myself. I've worked as a researcher, designer, PM. I believe in generalists and so I believe in setting teams up that way. And yes, specialism matters at times. Machine learning for sure is a deep specialism. And at Intercom, we generally, in engineering too, much prefer people who learn new things, whether it's a new coding language or framework or how to design AI interfaces or whatever. 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 you ran into that you think people should try to avoid and something you could share there, or just like any other lessons about making this transition that you think might be useful to other people.

Yeah, what I've mentioned so far, don't bolt it on. Stay up to date. I mentioned I feel like I'm behind all the time. It's moving so fast.

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

I'd love to tell you that it's incredibly structured, you know, a great reading list that I get every Sunday morning. It's pretty random. I'm on Twitter, which is called xcourse, a lot. I follow some people on Twitter. I actually use the recommended feed in Twitter a lot. I think because I interact and look at a lot of AI, I get to see a lot more. So I do that and I kind of do it deliberately to try and generate more stuff. I'll search Twitter as well. There's loads of cool stuff there. There's some newsletters as well and some people I follow.

Any newsletters you could call out that.

You think were our most interesting? Yeah, Matt Rickard is one guy who talks a lot about AI. The blogs of companies too, like OpenAI have a pretty good blog and they write papers and summarize them. Cool.

If there'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.

Yeah, perfect. Yeah, yeah. There definitely is. I'll dig them out. Your question earlier, how do you do? Just try. Try a book out half an hour and just go deep for half an hour and then bookmark a few things, come back to them in it. Like everyone, like, you know, it could.

Be so busy, so many distractions, you just gotta have to set aside time. Are there any other tools or apps that you find really helpful? Sounds like ChatGPT is kind of 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. For example, like Google, Bard is Google's AI search engine. Rewind is another fascinating company. I think it's rewind.ai. Rewind is basically augmented AI for your memory. So you install it on your local machine and it captures everything and remembers everything. It's all local, so there's no privacy issues. and you 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 so on. So I'm a believer in that type of thing.

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Yeah, 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 media is coming. Like, I'm sold, you know, 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, Daz, 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, you know, how.

Are you all in?

And I'm like, an educated guess, a hunch, you know, a lot of it's, a lot of it's like the kind of the, the part of, like, business strategy and product strategy that it 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. 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 but bigger. So I'm kind of like using that experience to like go all in. But it's a challenge for people, some people because they don't have that context or they disagree with it. you know, we have a lot of debate here about the future. You know, Fergal, I mentioned earlier, gave myself and a few other people, a few other product leaders, and Daz, he gave us like a, I don't know, was it a pitch or what? I don't know. About how maybe all of our roadmap with AI is wrong. Maybe we're like kind of, I don't know if you think are familiar with the Horizons framework, like Horizon one, two.

And three, you know, where like Amazon yeah.

So, so, like, Horizon one is kind of the medium, short to medium term, like, next 12 months, 12 to 18 months. Horizon two being like, hey, what's happening? Whatever, 18 to 36 months out. Or I think people use different time frames, different Horizons. Anyway, we're, like, in Horizon one land. We're like, yeah. And then next year, we're going to do this. And he's like, yeah, but two years from now, if this path, you know, plays out, everything we're doing now is, like, going to be irrelevant and. And. like, useless. You're like, okay, you know, and so, and so, like, those discussions happen. And, and 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 drying out voices of, like, alternative voices and opinions, which are often valid too.

What does help 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 feels like an easier pitch than maybe a lot of other markets.

Yeah, that's true. For sure. That's true. Yeah, showing people is definitely the easiest way. I think, yes, customer support is definitely that, you know, like I said, our Sam Altman's like number one customer support. So you're like, okay, I guess we should adapt. Yeah, we adapt 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. So for example, reporting software, you know, Tableau or any kind of reporting product, How do they work? Well, they're like the typical read, write app, build dashboards, filtering, querying, kind of hardcore querying, query a database, get some numbers, show it in a UI. A lot of thought and care goes into 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, who was our best sales one last year? January. Okay. Who is our top performing rep in January? You know, many. Like, the reporting product of the future might look like that. And so project management tools is another one. It was a bunch of products that I think are just outside the most obvious customer support one and yet equally ripe for a newcomer to come with a completely different paradigm and potentially take over.

I like that this connects back to your very first point about trying to think about where AI integrates. Let's 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, and maybe you can. And that gives you basically a whole strategy of like, okay, how do we actually do that with AI?

Yeah, and it's very hard to, you know, if you're, I don't know if the reporting thing will play out that way, but, you know, if you're like a Tableau type company, you have tons of designers who design dashboards and filters and querying type like workflow. Like, what do they do? The UI is the box, you know, so it's really hard to, 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 of 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. And, like, the. The older I get, like, the web 3 thing, I'm like, I don't even know what web 3 is. Crypto. I never. I never bought crypto. Maybe I'm wrong about that, but I'm not a bandwagon jumper. you know, and I. But I kind of maybe might have been when I was earlier. So, like. And I try these days to read the alternative opinion. People who are skeptical or. Or think it's bad. You know, a lot of people think this is terrible for Humanity. This technology is gonna eat us alive, you know? So I try and. I try and, like, balance my optimism. I'm kind of a delusionally optimistic thinker, so I try and balance that with. A negativity, I guess.

That's really good advice. Yeah. Is there anything else in this realm that you think might be useful to share before we shift to a different topic?

Oh, yeah. The other thing is don't be afraid. 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 only need two engineers per team. going forward. That's probably not really a good idea to do that, you know? And I think in reality, that's not gonna be how it plays out. Like, there's all sorts of, like, you know, loads of great studies over the years about how people don't end up losing jobs. The jobs get moved around. And also, you know, for customer support, for example, it's a high attrition job. So people say, like, hey, everyone's going to lose their job. A bot's going to take over. It's like maybe some of that will happen, but probably to attrition. As in, someone quit and just didn't get backfilled. So, you know, the doomsday scenarios that I don't think would play out as much, but for sure, 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. 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 act, you actually mentioned this before and after, which I hadn't heard about. What's the general idea to that concept?

Before after, it is literally that simple, I think. Like we have a rebrand at the moment happening, and that'll be a before after moment. We're redesigning our pricing, and then the day that pricing goes live, that'll be a before after, because it was like nothing's the same. And so, We need to go back out and talk to people again. Like, I'm a big believer in talking, you got to talk to customers. It's the only way. You got to talk, talk, talk, learn, learn, learn. Don't take what they say face value. Go deeper. And so, you know, a lot of these before-after moments, once you've passed the app into the after, you 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?

We had a principle called Align Price to Value. By the way, I think pricing is incredibly difficult. A lot of the design team who are comprising here, I say to them, it's one of the hardest design problems I know. Onboarding is another one. Onboarding people into a product is also, people are like, oh, hey, you just designed a few steps and it's pretty easy. People follow the steps. again, 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. 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 you $5,000 for those 10 units of value. 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.

Well, this feels like it could be a solo episode just talking through your pricing lessons and journey. Maybe is just, is there a nugget of wisdom you could share for someone that's trying to think about pricing right now based on your experience?

The number one thing I would say is keep it simple. Keep it simple. It's so tempting to, like with us, for example, a lot of SaaS products have add-ons where you're like, Hey, we built X and that's like 10 bucks or 100,000, depending on what kind of product you're selling. We built X and that's the price of X. Hey, we've just built Y. Y is awesome and it's a new thing you can do and unlocks all these new capabilities. People shouldn't get that for free because it's a new thing that they didn't have. So let's charge more for Y. 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 later, now you've got people who have the add-on and people who don't. And then you're like, I'm gonna add another thing. And so like, tiering, we've added tiers, we've cut with products, tiers, add-ons, tiering in the add-on. Oh my God, people can't understand their bill. So my advice is keep it simple, reject, fight so hard to not to resist the temptation to add extra ways in which you price.

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

I think I was talking about Scars for Life earlier. That's another scar.

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?

It's kind of like the Kano model, people familiar with that, but it's very simple. It's kind of like, I guess we took the Kano model and tried to make a really crazy simple version of it. Again, I'm a little bit allergic to things like this. I can't even hate myself for bringing up the Kano model. I'm allergic to people over-intellectualizing frameworks and 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 versus stable sakes, very simple. I think people who adopt a product or buy a product or switch to a product, there's kind of two driving forces. One is the attraction of the new solution. And that's basically differentiation. So what's different and better? But critically, what's different and better in ways that customers care about. Again, back to all the failed projects, my lesson from a lot of these was we were different and better in these Google projects in ways people didn't care about. You know, like all sorts of Google projects, like Google Wave was an amazingly innovative product that no one really cared about. So be different and better in ways people care about. So that's the attraction. That's like, oh, I want to check out that. That looks cool. I want to check that out. That looks better than what I have today. But on the other side, there's a kind of entry requirement or table stakes. To play the game, you gotta have a certain amount of things. And so their table stake features, they're often very boring. They're 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 changing over the course of a year or two, where we were 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. And then the roadmap was built based on those trading off, why do we need more differentiation or trading off, why do we need to invest more table stakes? So these days, the place of Ingram Micro 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 any kind of established category, customer support for us, big established category, massive. A lot of table stakes built up over years, decades. ServiceNow, Service Cloud, Salesforce, Zendesk, like decades of table stake feature building. So to play the game, you need a lot of the table stakes unless you have incredible differentiation. So 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, we were like Messenger first, modern messaging, a modern UX. They were 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 you're a startup, you need to invest a lot more in differentiation. And then over the years, I think you start to balance the books a bit.

I think what's interesting about this is one, it just gives you a way to think about looking at your roadmap. How much are we actually doing? And are we doing too much table stakes? Are we doing too much differentiation? So it gives you kind of an awareness of what's happening. And I think there's also, Interesting. It's an interesting strategy as a startup. Do we spend years doing table stakes and then launch, or does it go the way Intercom went, differentiate first, we'll build everything else later? Wonder when it makes sense to go one or the other.

Yeah, and it probably depends on the market. Different categories and all sorts of things.

Yeah. Awesome. Okay, 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. Like the different differentiation and table stakes with swinging the pendulum. So swinging the pendulum means you take a step back from everyday work life and you kind of make the observation that something's in an undesirable state. So, like, you know, maybe it's we. Whoa. We've all the differentiation in the world, but people can't adopt the product because we've never built any of these. table stakes. That's 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 not just not attractive to people. We need to like, okay, so this undesirable state. And then, so you go and fix it. But the temptation is that you overcorrect. And we've done this so many times in so many domains, everything from okay, we don't have enough differentiation. A year later, oh, wait a minute. We're missing all the table stakes. Okay, we're over there. So product building is one. People is another one. Building our teams and people. Another big one was maybe, I don't know, maybe five years into Intercom, we were on this high growth trajectory, really good classic startup before our pricing problems. 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 you know what we're doing? We need to hire some experts. We need to hire some experts. Like, you know, if we're going to go up market, we need to up market. People have 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 have done it before. And what they did was brought the culture and ways of working of their prior company to Intercom. And so we totally overcorrected. Didn't work out 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 and building teams is another where we really overcorrected to find out, okay, it's a balance here. Related to that one, the hiring one is like generalists and specialists, kind of similar theme. People who've done it before or people who are specialized. And we hired a bunch of specialists only to realize that they're not adaptable. And in intercom, we believe in kind of, we have a lot of ambiguity and we lean into the ambiguity. People who are highly specialized can thrive in big companies, really thrive. They're invaluable employees, but in a fluid start-up-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 and have a bit of specialism, not much, but enough to try and figure it out. You know? So we hire a lot of those kind of people today.

First of all, I love all these stories of things that didn't work out because a lot of people don't like sharing these. And this is what people want to hear. It's like, here's not everything was perfect. Here's a lot of mistakes that were made along the way. And it 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 think of when I say that?

In talking to people about this before, sometimes the conclusion of the conversation is something like, it's the only way to do it. Like you actually can't do it a different way. And so maybe the question is really like, how high up, how high does the pendulum go versus like, you gotta swing it. and then it's like, how far do you swing it? And for sure, you're right. With AI, we are like, we're actually, we're swinging it pretty high. Maybe I overestimated it earlier. If AI is in the differentiation camp to kind of mix the frameworks, we're still building a lot of table stakes features too, like building depth into the product. And that's 50 50. I think I mentioned 50 50 earlier. So that's 50 50. So we're not totally swinging this. We're not like, 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. It reminds me, you know where the boundary is, is what I was going to say. It reminds me of a story, like back to the olden days stories. I remember when I went, I remember at Google, privacy was really top of mind to the point that it would block decisions, block product progress, just privacy, circular conversations. so many circular conversations and nothing ever got built or shipped. I worked on a project for a year at Google and we shipped nothing in the year. Just circular conversations, which killed me at the time. So when I went to Facebook, I realized they have a different approach to privacy. Again, I'm not advocating it's necessarily good. It certainly didn't help their brand, but there was 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, 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, you know, you don't really have a choice. You 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 correct of course is.

Yeah, get that pendulum off the even pivot thing that it's on and then, oh, and then let's fix that pendulum. Let's put it back.

Yeah.

Okay. Another framework that I read about briefly, and I love the general idea of it already, which is something that I think you call product market story fit. Yeah. What is that?

So, yeah, with product market fit, Pretty basic, well understood, very important. The way I describe product market fit is you've got to build the right product for the right market. I think, by the way, as an aside, not enough people think about the market side of that equation. A lot of product people don't think about the market side. But for me, it's very simple. The market is the people, the problems they have, and how important the problems are to them. To have a good market, you need a lot of people with the same problem. and they need to care a lot about it. 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 is fine. 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. Yeah, it's the what if that's the market's the who the product's the what. And I just, I don't know, in my career again, so a bunch of products that were built, they were good products. in good markets and they failed and I couldn't work it out. And eventually I came back to this idea that like, and maybe someone might say, Paul, it's marketing. You're talking about marketing. But like, story, the story is wrong or the story is missing. And so sometimes it would be a great product in a great market explained in a convoluted way. Like that, I see that a lot. I used to see that a lot at Google again. just explained in a very complicated way, over intellectualized. And as a result, people are like, what? What are you talking about? You don't get their attention. And so the story is really important, as important. And actually, sometimes you'll see not great products, certainly worse on paper. I'm trying to remember the Spotify competitor back in the day, people were like, what was the name with it?

Rdio?

Yeah, Ordeo. Ordeo was one of these where like, I hear a lot. Yeah, people like great, like people, all I've ever heard of Ordeo was amazing product. It's failed, you know, and why did it fail? Spotify and Ordeo at the same market, they were solving the same set of problems. Ordeo was arguably the better product at the time. I don't know if that's true, but arguably the better, I always think Spotify is an incredible product. But the story, they've 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 of 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. And we had April Dunford on the podcast very recently talked a lot about that.

Yeah, yeah, she's excellent. It is really that like, why are you better? You know, and can you explain why you're better?

That's 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 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 work doesn't work? Whatever comes up?

Yeah. I'll be totally honest at the risk of offending people if they listen. Like we worked with Bob West, who's age of the eight 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 JotForm 2 really good, 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 so many people who spend so much of their energy debating the nuances and peculiarities of one version. Who cares? No one cares. Oh, I don't care. They care, obviously. But I'm like, your customers don't care. People you're trying to build a product for don't care. No one cares. That's a cool intellectual debate. But kind of for me, maybe it's too extreme. It doesn't really have any place in work. In the work we do, we're just trying to build a great product. And so for us, which is what we've done, it was a really good way of us centering on the customer problem, focusing on not getting distracted, basically in research, like good, solid research informed insight that told us like the thing people were trying to do, like what is the thing people are trying to do? Again, energy, do they have a lot of energy around it? Maybe the energy thing might have come from talking to Bob actually now that I think about it. I think it did actually. I think like the idea of like this idea that, you know, you need people who have a lot of energy around the problem and you kind of have to interview them for that most of the time. to feel the energy they have. It's very easy to see if someone's apathetic versus into it. So we've had it pretty good. And we invented this job stories thing kind of by accident. I can't remember exactly what happened, but 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, there's like a trigger in an app. Anyway, We didn't even give the thing a name. Someone else named it, I think. And I was like, we're just trying to build a great product. So we found it really good in that way, really simple. And then the other one that we use a lot still here is the four forces, which is this framework out of Jobs to Be Done. The four forces being different for 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. Here's another kind of funny story to tell you how much the four forces is really good. Here's a funny story. I was saying earlier that Owen and Des were trying to convince me to leave Facebook, which I loved at the time, join and to 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, and like, you know, basically worked me on the Four Forces. And I was like, that is genius. 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, but I think there's this like endless set of processes and ways of optimizing that gets people distracted and often just kind of slows everything down.

Yeah, yeah. And it's interesting and fun to talk about sometimes, like really fascinating, you know, but 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, 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.

What are two or three books that you've recommended most to other people?

Yeah, the two books I recommend to everyone always, I have 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 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 is Principles by Ray Dalio. I'm a big fan of Ray Dalio. I think he's incredible. I'm a big believer in principles. A lot of us at Intercom are. I always get those two books and they're totally different. The Paul Aiden book is you can read in 20 minutes. Principles is like that thick.

What is a favorite recent movie or TV show that you've really enjoyed?

Most recent is the Bear, which I came to late. The reason I actually love the show is because I think it somewhat celebrates the grind and I think that's important. I worked in coffee shops a lot when I was younger. when I put myself through college and stuff. And, like, the grind is part of life and part, it's an, the grind is a necessity to get things done and get, 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? Yeah, I'll give you a slightly different answer.

I don't already have sit in a few questions for candidates, and I don't like, I don't like, I ask question universally. I don't like questions that rely on memory. You know, a lot of, like, Tell me about the last time you did X. Here's an amazing question I got given recently by Alyssa who used to work here. I had to do referral calls. So you're interviewing someone, you want to give them the job, and they've got referees. And of course, the referees they have are the best people that they ever worked with, and their favorite managers. So this question is, what feedback will I be giving this person in their first performance review? That's an amazing question because the person can't dodge it. You know, there's an answer and it's incredibly enlightening.

And that's a question you ask on reference calls?

Yeah, on reference calls.

That is such a good question. I love it.

It's a great, amazing question. Yeah.

All right. What a gem. Thank you for sharing that. What is a favorite product you've recently discovered that you really love?

I know this is kind of like maybe cheating, but I go back to a lot of the AI products. I think ChatGPT vision is mind blowing. I've been playing with Rewind lately. I was a bit late to it. Dez 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, again, my kind of mind-blowing.

So Rewind's been fun. And they just came out with a.

Little audio thing that can record your.

Actual day and you can just- yeah.

I'm so sure about that.

Yeah, got some, got some flack.

Yeah, I'm sure.

I have a doubt about it.

Yeah.

I don't know. I don't know if it's real. It kind of looked like not a.

Real product when they launched it, but I think it's real. Well, and it's tippy toes into the what's okay and not okay and with AI and, you know. Yeah, yeah.

It's 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 it posted on my monitor. that says only work on what matters most. It's on my monitor to post it. And if something falls off, I have to write it again. Only work on what matters most. And like, it's amazing. I go into work, someone emails me, and I'm like, oh, God, you know, I'm like, only work on what matters most. The second one is, and they're related, is stop worrying about things you can't control. And so I've two of those. And so only work on what matters most. stop wearing things you can't control. It just reduces the temperature. Again, life lessons learned. I sent a lot of dumb emails in my past, like Red Energy, oh my God, what are they thinking? You wake up in Dublin to a San Francisco email, you're like, oh, God. You know, keyboard. And if your monitor says these two things, you just don't do that. You just take a breath, get a coffee. I'm back. Is it reading matter, you know? Beautiful.

That second one, I think I learned first from Seven Habits of Highly Effective People. Have you read that? Yeah.

I've just think about the focus, the.

Circle that you have things you can control. And then there's like the circle of things you can influence, and then 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 people share as their little mottos.

Yeah, the posted 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 is on the monitor.

It's in the way, you know?

Wait, you actually put it on the monitor in the way of your screen? Yeah. Oh, wow. It's in the bottom, the bottom left, like just covering 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 or your dad taught you?

The biggest one, and again, it's so reductive and simple, is to be nice to people. I think being nice goes way further than people really realize. One thing that I've learned, again, the hard way through life, is you have no idea what's going on in people's lives. You have no idea. People could have all sorts of like really stresSful, all sorts of personal stuff going on. And the reason they did the thing in work that you didn't like is because of that. And so, like, I try and think, like, be nice. You don't know what's going on. Like, you might learn later. Don't, you know, don't, like, don't act in a way you would regret. I think being nice in life goes far further than most people give a credit for it because it's kind of too much of a, I don't know, fluffy truism or whatever.

I 1000% resonate with that. I've been told I'm too nice and I had to become a little less nice, but I still can't lose that. So I fully buy into that. My parents taught me a similar lesson.

Yeah, 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. And the nicest thing to do is to do the harder thing. It's actually the nicer thing to do. People are relieved in this example. It's a better, it's a nicer thing to do. So it can be a complicated one.

I love it. Final question. You're Irish. You're based in Ireland. What is an Irish food you think people should definitely try out if they ever visit Ireland? Yeah.

Can I cheat and say Guinness? Is that food?

Absolutely.

The Guinness in Ireland, people talk about this and like, it's true. The Guinness in Ireland is much, much better for a whole bunch of reasons. It's basically a fresh product and it's brewed here. It's kind of like the way they think about it is like milk, milk goes off, Guinness goes off. You know, Guinness is less than a few days, older than a few days old, tends to start deteriorating. So Guinness in Ireland is amazing because it's made here. The other thing I think is that Ireland does really well as a fish. Ireland has not had, by the way, the greatest reputation for culinary excellence over the years. I think Irish food in the States in particular is not good. But the fish here is incredible. You can get incredible fish. Ireland'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 gotta go?

No, there is actually. You just need to be near a brewery. You need to be like, and so Guinness is brewed in Nigeria. There's a huge Guinness market in Nigeria. I think they actually use a different recipe, but it's brewed there. I think the brewery in the US is somewhere 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. One of my friends does a lot of working Guinness. I think the boat carrying the Guinness goes down through the Panama Canal, back up to San Francisco.

So you're, 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.

Amazing, Paul, you are awesome. Thank you so much for being here. Two final questions. Where can folks find you online if they want to reach out?

And how can listeners be useful to you? I have a handle is everywhere, basically P-A-D-D-A-Y. It's like Paddy with an extra A. so P-A-D-D-A-Y that's everywhere. So paddy@gmail.com, paddy, it's my kind of handle everywhere. So that's where you can find me. I'd love, yeah, I'd love people to reach out to me, like genuinely learn. I'd love to hear from people who think my AI talk is nonsense and, you know, it's more like a crypto web three or, you know, I'd love to hear people who have alternative opinions and challenge mine. That's how I kind of like to learn and get better. So if people have those opinions, I'd love to hear them. I'd love to talk to them.

Be careful what you wish for. The YouTube comments are always a spicy place. We'll see. We'll see what we see. Awesome, Paul.

Thank you again so much for being here.

Yeah, thanks, Danny.

I really appreciate it.

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