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

23:07And 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, you know, another place is it'll be augmentation. It'll augment, it'll help people as the co -pilot ideas that are going around. But yeah, I think that you got to match your product on what AI can do and what it will be able to do, and then ask yourself, okay, what are we gonna do? Is there an example of that at Intercom where a different company of, here's a problem with Transolve, oh, AI can actually do this fully for us?

23:40Oh, yeah, I like, I'll give you Intercom first. Like again, like, you know, this date's kind of, I think it was never a turning out. Etched in our head, you know, we have like, Fergo, who was our head of machine learning. And Fergo just turns around that day and he's like, okay, then 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, oh, 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.

24:12So we literally ripped up our strategy almost entirely and started again, like from first principles and said, okay, why do people use Intercom? Intercom is a customer support product. And then very soon after that, Sam Altman, who's the founder ahead of OpenAI, said, hey, one of the first industries is going to be disrupted is customer service. Like, yeah. So we did, we totally changed how we think, how we work. And we just went kind of heads down and built a product called Finn. We built other things first, actually Finn came later, I think about it. But we just went, we kind of went all in on it.

24:50It 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. 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 have to do it. Do you share briefly what Finn is? Just for folks that aren't familiar? Finn is, I 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, mostly ticketing based, you'd file a ticket, a lot of do not reply email and kind of so on.

25:34And then came along conversational customer support, which is just basic messaging, like WhatsApp or I message a commentator. Now there's like, you know, 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, well, the results who seem to be in our 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 wrap in their heads around what this means for the role of the teams.

26:13Low to cool stuff, you know, like new types of jobs for people, like conversation designers, a job we have where you design the conversations that Finn does our managers. So anyway, that's what Finn is. Finn is expanded. So Finn is now also in our intercom inbox, the place of people, answer queries, customer support queries. And that Finn's in there too, helping the support reps, like suggesting answers for them to use or helping them like rephrase things. So it's now all the menacing people as well as answering questions by itself. I think you're one of the few companies that has pivoted fully into AI.

26:50And 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 and it depends what you measure as success. So again, there's a lot of hype and buzz with AI. So if you're measuring it by interest, that'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.

27:27They're like, does it actually work? Does it a little bit, again, back to the earlier thing of like the so much hype is a bit of skepticism or 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 of 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? 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 in -band questions would fit in.

28:07So like, we've some customers who see huge success, but it's early, you know? And so like, has it transformed our business like financially, not yet, you know? It's not like this kind of, you know, uh -oh, I go fast growing startups, you know, if you think of intercom as, are like AI intercom as I guess a new startup, even though we're a 900 people, you know, the kind of growth curve, you're looking for this kind of exponential curve, as opposed to like big public company, kind of linear growth curve. With the exponential, when it takes a while, you know, the first kind of year, two years, it's like bottom of that.

28:39And so I think we're still, we're still in the like, trying to figure out exactly what's going on, trying to talk to educate people, but you know, we have enough evidence to believe it's the future for sure. 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 imagine it would be this good? I kind of go back to that like before after thing. So chat GPT, the first version of chat GPT was up before after where we had built, like we've been working like I said in a space, we've had a machine learning team for a long time.

29:12The way our machine learning thing worked before chat GPT was that you have a, there's only manual setup, like a, you know, customer support manager, we have to like orchestrate the box and like teach it what to say and like, you know, just a lot of orchestration, a lot of teaching it. And then chat GPT showed up and it's like, 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 personarily for a lot of these basic things. So that's the way my mind. And then that was just, I would can answer questions, but then you're like, it can reason.

29:44Oh, there's actually like a debate about whether it's just reasoning or deduction or, you know, but it can like work things out. And I'm not going for going down until these like really philosophical things. Like I'm like, we just need to build a, let's go back, build a product or whatever, but it can work things out. And that blew my mind. And like we fed it all, but it just we fed chat GPT. And other companies too, like we played it, you know, other LLM's like a traffic 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.

30:16And I was like, wow, it's really good at writing code. What does that mean? You're kind of, and then you start thinking, like here in Inchcom, we have kind of a one to five ratio. So like a PM has about five engineers on a team. And you look at it this thing, writing code, and you're like, well, what happens next? You know, like do we need as many engineers or will their role change? And they'll start doing different types of things, like reviewing code instead of writing code. So that kind of blew my mind. And then the visual stuff, like I mentioned earlier, I think the visual thing was bigger than the original one.

30:47Like it can parse imagery and like, you know, it can help you see the world. You take a photo of your bike and say, hey, what's wrong? And I'll tell you, what's wrong? How to fix it? You can be traveling, take photos of stuff. It's in a different language. It's like etched in stone on a like 12th century cathedral. You're like, what does that say? And that'll tell you what it says. Like it's just like, I had to do that, you know? This is one I'm actually repeating most people these days. Here in Ireland, if you want to be a radiologist, you know, so like study X -rays and tell people what's wrong and so on and so forth, it's seven years draining to like learn that skill.

31:26So 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, you know, radiologists will not take X -ray. Well, I guess it might take them, but they won't analyze them for sure. They'll look at what AI says, check that it's right. And then that's like kind of, that's like manner time, like, you know, telepation, maybe tell them what kind of course.

32:03So like the job just fundamentally changes. And by the way, that could be amazing. We have here in Ireland, we have like long cues 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 they can say things and it sounds exactly like you and it's really, really good. Like almost English will be like, that sounds like ball. And so I mentioned that the metaverse earlier, I don't know if you saw Zuck talks to Lex, and then see that. So that was my first like, oh, like so it's the met, you know, if we could happen to see this, they met in the metaverse, I think, or some virtual world.

32:42Yeah, it was like a black room. In a black room, yeah. The tech has come on so they can analyze your face and build a 3D model. It's really good, like really, really close. So 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. Hey, you know, like your parent dies and you're, you can still talk to them.

33:22And like, actually the weirdest thing, maybe it's not good for people, I don't know. But that tech is like just at the end of the corner, you know, and the AI can like, I kind of like, your question's mind blowing. There's actually a black mirror episode with that same premise where. That's right. Yeah, and I don't think it ended well. So. No, I like careful. For sure, for sure. Yeah, it is like the, I think we're not already reporting. 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.

33:57So, you know, in this tech is like again, just here, where like if I was a native Spanish speaker and couldn't speak English, you and I could still have this podcast. You know, it's been your voice to be translated in Spanish in real time for me. So 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. Oh, wow. It 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 pretty cool.

34:27You mentioned that your edge 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 product. 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 like a really great machine learning engineers. Like that's where it starts. And if you don't have that, then you've got to find a hard to build, truly, really, truly great things.

35:03You know, so like what I open AI provide, I'm quite on traffic provide and cloud is never... They provide like amazing and amazing technology, but you got to build on top of it. If you really want something brilliant, you got to build on top of it. So like we adapted what they build for our customer support. Maybe someday we need to go build our own LLM. That's just for customer support. Maybe I don't know where that will all go. And maybe everyone will have to 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 like kind of the first thing.

35:38And 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 functions. That's what we've been doing. The are kind of like ML team's way bigger than it was and way bigger than it ever has been in the intercom. And then kind of it forks. So some projects are very heavy on that ML team and it needs some. Other projects are more front end. Like the inbox stuff I mentioned earlier, where we have thin and thin is kind of working. We've built the underlying technology. Now it's a question of like, a few of the human support person answering questions in the inbox.

36:16That'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 and the user experience that feels natural? So that's a really hard design problem. So let's say you're kind of back into like, okay, we've a product team that's like a product manager, a product designer, maybe three, four, maybe five engineers. And they're getting help from the machine learning team. So like we now have both set ups. And increasingly we can do more with the latter.

36:49More teams you 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. I think some people are still in that camp. Like again, I go back to the mobile thing. It's just so many direct parallels with it. Like I said earlier, at Google I worked in the mobile app team. I worked on mobile Gmail, mobile docs. And it was like the mobile team and we were in London. We're like, hey, we're the mobile team in London. And meanwhile over in Mac and view in California, no one cared.

37:27You know, it's like, it was like you're 20 people. We're 200. No one uses this stuff on a phone. No, again, a lot of skepticism. No one's gonna write docs on a phone. Seriously, you got to write a document. They're gonna write a full documents on a phone. Are you crazy? You know, so don't do that. You know, we're trying not to do that. Like don't bolt it on. Don't be like, I would have a bunch of AI people. And we do have some specialists. But January's beacon, we're trying to like have everyone learn about it. Interesting. So I'm curious just specifically what that looks like. Don't bolt it on.

38:01The idea there is don't just have like a site team. That's like they're the AI team. They're gonna add AI to all this stuff. You're finding and let us listen is integrated into every product team. Yeah, and we're still early there. You know, we're still early. So like what we're trying not to do is have like the kind of like AI inbox team. Oh, 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. I'm a big believer in generalists. Like a big, big believer in like, I mean, I guess my background is like, you know, Jack of all trades master and on.

38:35Probably I described myself. Like I've worked as a researcher, a designer, PM. And so I believe in generalists. And so I believe in setting teams up that way. And yes, specialists and matters of time. So machine learning for sure is a deep specialist. And it is kind of we generally much in engineering too. Much prefer people who learn new things. Whether it's like a new, new coding language or framework or, you know, how to design AI interfaces or whatever. Got 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 gonna get to once they realize, oh shit, we really need to get big here.

39:13Or 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 cheer there. Or just like any other lessons about making this transition that you think might be useful to other people. Yeah, one of them mentioned so far. Don't you don't pull it on. Don't keep, I stay up to date, you know, like I mentioned, like read read. 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.

39:44And you know, I'm a great reading list that I get read at a gallery Sunday morning. It's pretty random. I'm on Twitter, which is not called X course 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 get to see a lot more. So I do that and I kind of do it deliberately to try and generate more stuff. I'll search Twitter as well, because those are cool stuff there. There's some newsletters as well and some people that follow. Any newsletters you could call out? They think we're almost interesting. Matt Rickard is one guy who talks a lot about AI.

40:20The blogs of companies too, like you know, open AI have 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 take a minute. Your question earlier, how do you do? Just try a book out of half an hour and just go deep for half an hour and then book market few things, come back to the minute. Like everyone, like you know, it could be so busy. So many distractions. You just got to have to set aside time.

40:50Are there any other tools or apps that you find really helpful? Sounds like chat GPs kind of at the center how you play around with it. Is there anything else that you find really interesting? I'll try other things like BARD. You know, for example, like Google, BARD is Google's kind of AI search engine. Rewind is another like fascinating company rewinds rewinds .ai. Rewind is basically augmented AI for your memory. So install it on your hard on your like local machine and it captures everything and remembers everything. It's all local, so there's no privacy issues. And you gotta try these things to understand what it's any good or useful or where is the boundaries and how does it work and so on.

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42:44When you started rolling out AI and kind of leaning into this direction, did you run into any big challenges or hurdles organizationally or personal interests or opinions that I don't know? Was there anything you're into? That was a big stumbling block and something you had to get over. Yeah, like any company, Intercom is full of diverse opinions about things, you know? And I think with AI, you know, I'm like, I'm all in. I'm a top -class user. I'm all in. I'm leaning forward. The media is coming. I'm soles. You know, 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 your own co -founder and CEO, Dez, co -founder, like me, like a lot of senior leadership team are like, we're in the all -in camp.

43:33And so that helps a lot. Of course, if you're senior leadership team in the company or a call -in, of course, then it kind of trickles that. But equally, like, you know, people sometimes ask, some of the kind of hurdles of being like, you know, why are you all in? And I'm like, an educated guest, a hunch. You know, a lot of it's like, the part of like business strategy and product strategy that you just, it's just hard. It's just kind of, it's like taste. You know, people talk about taste. Product taste. You know, a lot of it is like, it's judgment based on experience. That's all I can say. Like, I don't know.

44:09For 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 there with bigger, some kind of like, using that experience to like go all in. But it's a challenge for people, some people because they don't have that context. Or they disagree with it. You know, we've a lot of debate here about the future. You know, for good I mentioned earlier, gave myself and a few other people, a few other product leaders. And as he gave us like, I don't know, is it a pitch or what?

44:41Play, I don't know. About how maybe all of our road map would AI is wrong. Maybe we're like kind of, I don't know if you think, or if you think we're familiar with the horizons framework, the horizon one, two and three. Yeah, 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? Like, is that like, you're doing whatever. 18 to 36 months out, or I think people do different time, frames different horizons. Anyway, we're like in horizon one, like yeah, and the next year we're going to do this.

45:12And he's like, yeah, but two years from now, if this path, you know, plays out, everything we're doing now is like, going to be irrelevant. And like useless. You're like, okay. You know, and so, and so like those discussions happen. And and the level of ambigame and the ability is off the charts. a lot of the challenges have been navigating that ambiguity and helping people get the conviction I have. You know, without kind of dry and out voices of like, alternative voices and opinions, which are often valid too. What is help people get that conviction? Is it just showing them examples of here?

45:56Something wow, look at this thing. This is unreal. And I think part of, partly what helps imagine is the market year in seems like such a clear opportunity for AI. I feel like it easier pitch than maybe a lot of other markets. Yeah, that's true. For sure. That's true. Yeah, showing people is definitely like the easiest way. I think it's a customer support is definitely that. I, you know, like I said, I saw in a lot of it's like number one customer support. So you're like, okay, the customer is adopted. Adopt, yeah, we adopt our die. It's got our mantra adoptor die. I think that there are other industries where they're on the same journey.

46:34It's just not as obvious. So for example, reporting software, you know, tablo or any kind of reporting product, you know, how do they work? Well, they're like the typical kind of like, you know, read right app, build dashboards, filtering, querying, you know, kind of hardcore querying, kind of query database, got some numbers, showed in a UI, a lot of thought and care goes into like how you present that data to people, it's like a chart that are appropriate, how people make good decisions ultimately. I think, again, this is like, hand -wave, you know, maybe that's all done, dead now, and the reporting product of the future is just a box.

47:15And the box just goes to the database. And the box is just, and how is our best sales went last year? January. Okay. Who is our top performing rep in January? You know, Lenny. 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 I integrate this, think about what problem are you solving as a company, for example, to have a look, helping people visualize data.

47:56And then the question is, can I just do this for you? And then that case, oh, maybe you can't. And that gives you basically a whole strategy. Like, okay, how do we actually do that? Do they? Yeah. It's very hard to, you know, if you're, I don't know if the reporting thing will play out that way, but, you know, if you're like a, a tableau type company, you've tons of designers, you 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 in a given connection that we must change, really hard.

48:33Maybe one last question here, for team members learning and starting to work within this realm, is there anything you find helpful to get them ramped up other than the advice you've already shared, which is just, read a lot of stuff, watch Twitter slash X, subscribe to these newsletters, and then just try it. I also try and read things that say, like, it's all a load of crap. You know, so, like, it's very easy. I've been guilty this many times, back to the, like, mistakes you've made, like, I've been guilty this many times, where, like, I've jumped on a bandwagon, and, it was all wrong. And, like, the older I get, like, the Web 3 thing, I'm like, I don't even know what Web 3 is.

49:12Crypto, I never, I never bought Crypto. Maybe I'm wrong with that. But, I'm not a bandwagon jumper, you know, and I, but I kind of, maybe, you might have been when I was earlier. So, like, and I try these days to read the alternative opinion. Um, people who are skeptical or, or think it's bad. You know, a lot of people think this is terrible for humanity. This technology is going to eat us alive, you know? So, I try and, I try and, like, balance my optimism. I'm kind of a delusional optimistic thinker, so I try and balance that with, and I can take a bit of a guess. That's really the obvious. Is there anything else in this room that you think might be useful to share before we shift to a different topic?

49:58Oh, yeah. The other thing is, don't be afraid. Um, 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're going to need two engineers for a team going forward. That's probably not really a good idea to do that. But, you know, and I think in reality, that's not going to be how it plays out. There's, like, all sorts of, like, you know, low is a great study over the years about how people don't end up losing jobs. Um, the jobs can move around. And also, you know, for customer support, for example, it's a high -attrition job.

50:32So, people say, like, hey, everyone's going to lose their job. A boss is going to take over. It's like, maybe some of that will happen. But probably, to a tradition. I was in, like, people, someone quit and just didn't get backfilled. So, you know, the, the Doomstays scenarios that don't think we'll 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. I love that. Okay. I want to chat about frameworks. You have a lot of interesting frameworks that you've put out there. So, maybe we do kind of a rapid fire through a number of frameworks that you've worked with and find useful.

51:07And the first thing, you actually mentioned this before and after, which I hadn't heard about, is what's the general idea to that concept? But before after, it is, it is literally that simple, I think, like we've rebranded the moment happening. And that would be it before after a moment, you know, we're redesigning our pricing. And then the day that pricing goes live, that would be it 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 talk. You got to talk to customers. It's the only way. You got to talk, talk, talk, talk, learn, learn, learn.

51:39Don't take with the safe -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've got to start learning, where we're right, where we're wrong, what happened, what do people think, you know? Can you talk more about this pricing learning slash mistake you shared? What do you think you did wrong? What happened there? You know, we had a principle called, a line price to value. By the way, I think pricing is incredibly difficult. A lot of the design team were working pricing here. You know, I say to them, it's one of the hardest design problems I know.

52:19Like, I'm the onboarding is another one. Unboring people into a product is also. Like, people are like, hey, 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 where I'm like, a line 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 some people's, you know, for some person, they get like 10 units of value. I think that's about five dollars.

52:53So, I was like, I'd pay you $5 ,000 for those 10 units of value. You know, so. The biggest mistake was, we cut a lot of mistakes compared to it. And this is an area where I think we were in risk of verse. We've ended up with too many pricing models. We've built on top of old, you know, competitive mistakes. And it took a brave decision to say, we're going to start again. Well, this feels like it could be this old episode just talking through your pricing lessons and journey. Maybe as just as they're a nugget of wisdom, you could share for someone that's trying to think about pricing right now based on your, your experience.

53:31The number one thing I would, I would say is keep it simple. It's so tempting to, like with us, for example, like a lot of SaaS products, you know, have add -ons where you're like, hey, you know, we built X, and that's like 10 bucks, or 100 ,000, then what kind of product you're selling. We built X, and that's the price of X. Hey, we've just built Y. Y is awesome, and it's a new thing you can do, and it unlocks all these new capabilities. People shouldn't get that for free, because it's a new thing they didn't have. So that's charged like more for Y. But that doesn't really work with you either.

54:05Okay, let's look at an add -on. Oh yeah, cool, people just add -on. But then later, then you've got people who have the add -on, and people who don't, and then you're like, you can add another thing. And so like tiering, we've added tiers, we've like, you know, different products, tiers, add -ons, tiering in the add -on. Oh, yeah. You know, people kind of understand their bill. So my advice is keep it simple, reject, like 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. Okay, think I was talking about scars for a life earlier?

54:51That'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 K -no model, I think we're familiar with that. But it's very simple. It's kind of like, I guess we took the K -no model and tried to make it a really crazy simple version of it. Again, like, I'm a little bit allergic to things like this. I can't even hate myself for bringing up the K -no model. I'm allergic to like, people over -intellectualizing frameworks. And like, you know, oh, well, if you've seen the news, different laws, whatever, I'm like, keeping simple, practical, and pragmatic.

55:27And then let's all go back to work and start building the product so that customers can benefit, because that's actually all that matters. And so, difference versus table stakes, very simple. 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 last thing for a lot of these was, we were different and better in these Google projects, in ways people didn't care about.

56:07You know, like, we're 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. It'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 like a kind of entry requirement, or like table stakes. You know, to play the game, you've got to have a certain amount of things. And so, they're table -stake features. They're often very boring. You know, they're like real basic stuff, boring stuff, and easy to ignore and easy to not build.

56:44And again, a mistake would 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 the year or two, where we were like all the differentiation to realize that everyone loved it and really wanted to buy what 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 robot was built based on those, like trading off, where do we need more differentiation, or trading off, where do we need to invest more table stakes?

57:17So these days, the base of income 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 of that 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 things do things matters more to us? Not what it tells us actually to our customers right now. The other thing that we've talked a lot about here internally is, if you're a startup, and you're entering some kind of any kind of established category, customer support for us, big established category massive, a lot of table stakes, built up over years, decades, you know, service now, service cloud, sales for a send -ask, like decades of table stake feature building.

58:00So to play the game, you need a lot of the table stakes, unless you have incredible differentiation. So from the early years of income, people just buy us alongside service cloud or send -ask. They just buy us alongside. They're like this intercom thing, we were like messenger first, modern messaging, and 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 send -ask for intercom.

58:35But 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 a awareness of what's happening. And I think there's also interesting, it's an interesting strategy as a startup. Like, do we spend years doing table stakes and then launch, or as it go, the way intercom went, like differentiate first, we'll build everything else later.

59:12Wonder when it makes sense to go one or the other. Yeah, and it probably depends on the market, different categories and all sorts of things, yeah. Yeah. Awesome. Okay. The next framework is something that you got swinging the pendulum. What is that about? I actually kind of mentioned that in an example of it earlier. Hmm. Like the differentiation and table stakes was swinging the pendulum. So, swinging the pendulum means you take a step back from everyday work life. And you kind of make the observation that there's something in an undesirable state. So like, you know, maybe it's, we've, whoa, we've all the differentiation in the world, but people can't adopt a product because we've never built any of these table stakes.

59:54That's like undesirable. Or, oh, we've now built all these table stakes and we've not been investing in differentiation. And actually, we're not that attractive to people. Because switching product is like a pain. And we're not just not attractive to people. We need to like, okay, so this undesirable state. And then you go and fix it. And 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. Like, we're missing all the table stakes. Okay. We're over there, you know.

1:00:27So product building is one. People is another one. Building our teams and people. Like another big one was, maybe, I don't know, maybe five years into come. We were, you know, what are those kind of high growth trajectory, really kind of good, classic startup, before our pricing problems. And 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, where's 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 open market.

1:01:06People have done it before. So, you know, that was like undesirable state. Fix it by hiring people who have done it before. Let me hire 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 over corrected. 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 hiring and building teams, as a matter of where we really over corrected to find out, like, okay, it's a balance here.

1:01:43Related to that one, the greatest hiring one is like generalists and specialists. It's kind of similar theme. People who have done it before or people who are specialized. And we hired a bunch of specialists. Specialists only to realize that they're not adaptable. And in intercom, you know, we believe in kind of, we've a lot of ambiguity and we lean into the ambiguity. And people who are highly specialized can thrive in big companies. Really thrive. They're invaluable employees. But in a fluid start -up 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, it's higher someone who has done a bit of it.

1:02:26And a bit of a specialist, not much, but enough to find figured 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 don't work out, because a lot of people don't like sharing these. And this is what people want to hear. Like, here's not everything was perfect. Here's a lot of mistakes. They're made all 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.

1:02:57It 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 of 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 got to 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 swingin' pretty high. Maybe I overestimated earlier, like, you know, if AI is like in the differentiation camp to kind of mix the frameworks, we're still building a lot of table stakes features too, like building depth into the product.

1:03:42And that's 50 -50. You know, I think I mentioned 50 -50 earlier. So that's 50 -50. So we're not totally swinging it. We're not like, you know, it's swung, but we're also kind of doing the other thing, balancing things out. So I think you probably have to swing it. Reminds you to know where the boundary is, what I was going to say. Reminds you a story, you know, like, back to the olden days, stories. 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.

1:04:18And nothing ever got built or shipped. I worked on a project for a year at Google, and we shipped nothing in the year. Just circular conversations, which killed me at the time. So when I went to Facebook, I realized they have a different approach to privacy. And again, I'm not advocating, isn't that really good? It certainly didn't help their brand. But it was kind of an idea that to know where the boundary is, you got to cross it. And crossing is painful. But if you don't cross it, you'll never know. So if you think you're going up to the boundary, then you stop before it turns out it's actually miles over there.

1:04:54So I think with a lot of this stuff, you don't really have a choice. You're going to cross the boundary. Feel the pain. Be humble enough to realize you didn't get it right. And go down or whatever the right course act, correct, of course, is. Yeah, get that pen just a little off. They even pivot thing that it's on and then, oh, and then let's fix that pen just a little bit. 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, you know, with product market fit, pretty basic, well understood.

1:05:36Very important. The way I just write product market fit is you've got to build the right product for the right market. I think, by the way, as an aside, a lot of not enough people think about the market side of that equation. A lot of product people don't think about the market side. But for me, it's very simple. Like the market is the people, the problems they have, and how important the problems are to them. To have a good market, you need a lot of people with the same problem. And they need to care a lot about it. Going back to the Google social stuff, we found a lot of people with the same problem.

1:06:07They 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. That's the market's the way the product's the what. And I just, I don't know in my career again, so a bunch of products that were built, there were good products in good markets, and they failed. And I couldn't work it out. And eventually I came back to this idea that like, and maybe someone might say, Paul, that's marketing. You're talking about marketing. But like, story, the stories wrong are the stories missing.

1:06:45And so sometimes it would be a great product and a great market, explained in a convoluted way. Like that, I see that a lot. I used to see that a lot at Google again. Just explained in a very complicated way, over -intellectualized. And as a result, people are like, what? What are you talking about? And so the story is really important, as important. And actually sometimes you'll see like, not great products. Certainly worse on paper. Trying to remember like the Spotify competitor back in the day, people were like, I'm close to maybe a... Audio? Yeah, audio. Audio was one of the East where like...

1:07:23I like your lot. Yeah, people, like, great. People all I've ever heard of audio was amazing product. 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 if that's true, but arguably the better. I know it's been Spotify's incredible product. But the story, they got the story wrong. And so again, I think all product people, whether you're a designer, a product manager, people in research, data science, need to think about the story all the time, work of marketing, work of product marketing, and like learn about how to explain the product as much as how to build the product.

1:08:04Makes me think about positioning and how important that is. And we had April Dunford on the podcast, and very recently talked a lot about that. Yeah, yeah, yeah, she's excellent. Yeah, it is really that like, why are you better? You know, and can you explain why you're better? It was such an important point. A final area I wanted to touch on is jobs to be done. So we had the co -creator of jobs to be done on the podcast. We had a stream I'm Christian on 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?

1:08:40How 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 finding people to listen. Like we worked with Bob West, you know, who's aged eight years ago, and Bob's right guy. And we kind of followed that model of jobs to be done more than the ODI, I think it's the other school of thought. Anyway, I'll try and say this in a simple way. We found jobs fun too really good. You're very, very useful. But in a very simple way, you're getting back to the idea 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 peculiarities of one version of, who cares?

1:09:27Like no one cares. Oh, well, I don't care. They care. But I'm like, your customers don't care. Like people you're trying to build the product for don't care. No one cares. That's like a cool intellectual debate, but like kind of for me, maybe it's too extreme. It doesn't really have any place in work, you know, like in the work we do. We're just trying to build a great, great product. And so for also jobs we don't, it was a really good way of us centering on the customer problem, like focusing on like 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?

1:10:09Again, energy, do there a lot of energy around it? Maybe the energy thing might have come from talking about actually, and I think about it. I think it did actually. I think 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 that energy they have. You know, like it's very easy to see if someone's apathetic versus like into it. So we've had it pretty good. And we invented this job stories thing kind of by accident. Our camera exactly happened, but like I wrote it this way of writing a job story, basically.

1:10:41What we didn't call it job story, someone else called it that we just at the time were like, there was this kind of a remember, you know, there's like a trigger in an act. Anyway, we didn't even give it the thing a name. Someone else named it, I think. And I'm just trying to build a great product, you know? So like we've had it really good in that way, really simple. And then the other one that we use a lot still here is the four forces, which is just like framework out of jobs we've done. The four forces being like different for people, the different forces when people try and switch product.

1:11:16And some of us, the differentiation table stakes stuff like the attraction of the new solution, the reasons that you might not adopt it habits people have anxieties. Like here's another kind of funny story to tell you how much the four forces is really good. Here's a funny story. I say an earlier that like Owen and Daz are 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, talk to me and fed me my anxieties. And like, you know, like, whatever, like, I'm like, you know, basically work me on the four forces.

1:11:53And 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 this same exact thing with jobs to be done. I find it really useful as a framework for the podcast and 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.

1:12:29Yeah. And it's interesting and fun to talk about sometimes. It's a 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 recommended most to other people? Yeah. The two books I recommend to everyone always.

1:13:06I've copies in my office here. It's not how good you are. It's how good you want to be. It's about by Paul Arden, who was 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 the right way. Everyone does. The second book I recommend to everyone and buy for people and give to those principles by Ray Dalio. I'm a big fan Ray Dalio. I think he's incredible. I'm a big print believer in principles. A lot of us at Ingecom are. I always get those two books. And they're totally different. The Paul Arden book is, you can read it in 20 minutes.

1:13:35Principles is like, that thick. What is a favorite recent movie or TV show that you really enjoyed? No, it's recent as the bear, which I came to late. The reason I actually love to 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. I put myself through college and stuff. And like the grind is part of life. It's in a set. But the grind is a necessity to get things done and get make great things happen sometimes. And I like that about it. I really like that about it. What is a favorite interview question you like to ask candidates?

1:14:13Yeah, I'll give you a slightly different answer. I don't really have said in two questions for candidates. And I don't like, I don't like, I don't like, I don't like questions that rely on memory. You know, a lot of like, tell me with the last time you did X, you know, here's an amazing question I got given recently by a list that used to work here. I had to do referral calls. So like you're interviewing someone, you want to give them the job and they've got referees. And of course the referees they have are like the best people that they ever worked with in their favorite managers. So this question is, what feedback will I be giving this person in their first performance review?

1:14:45And it'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's such a good question. I love it. It's a great amazing question. All right. What a gem. Thank you for sharing that. What is a favorite product you've recently discovered that you really love? I know it's kind of like maybe cheating, but I go back to a lot of the AI products. I think I think Chachi Pt Vision is mind blowing. I've been playing with rewind lately. I was a bit late to it. Daz and Kira and a bunch of people here coming up, Fanders, VentureCon.

1:15:21Love rewind. Use it and love things amazing. It's a bit late to that, but it's just like augmented memory. It's kind of like, my kind of mind blowing. So rewinds me fun. And they just came out with a little audio thing that can record your actual day. Yeah, I'm not so sure about that. Yeah, I got some chat some black. Yeah, I'm not so sure about that. Yeah, I don't know. I don't know if it's real. It kind of looked like not a real product when they launched it, but I think it's real. Well, and Tiffy toes into the what's okay and not okay and with the AI and you know, yeah, yeah. It's good theory though, for sure.

1:15:57What is a favorite life motto that you often come back to share with people, find helpful for yourself? Yeah, I have a post to that my monitor that says only work on what matters most. It's all my monitor post it and I've sometimes falls off and I've to write it again. Only work on what matters most and like it's amazing. I go into work, somebody emails me and I'm like, oh God, you know, I'm like only work on what matters most. The second one is, and the related is stop worrying with things you can't control. And so I have two of those and so only work on matters most stop worrying with things you can't control.

1:16:34It just like reduces the temperature. Again, like life lessons learned, I send a lot of dumb emails in my past, you know, like red energy. Oh my God, why are they thinking, you know, like you wake up in Dublin to a San Francisco email and you're like, oh God, you know, keyboard. And if your monitor says these two things, you just don't do that. You just take a breath, got a coffee, come back. Is it really matter, you know, beautiful. That's like, and when I think I learned first from seven habits of highly effective people, you read that. I've just think about the focus, the circle that you have things you can control.

1:17:13And 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 don't really help on myself. Yeah. I love that you have it as a post. I feel like I need to make post it to all these lessons people share as their little models. Yeah, the post it on the monitor is real life hack. I found a few years ago. It's like it's kind of don't win away. The post on the monitor. It's in the way, you know, 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.

1:17:45That's what I make myself look at it. Yeah. Wow. I haven't heard of people putting it over freshness real estate on their monitor. Yeah. That works. What's the most valuable lesson your mom and your dad taught you? The biggest one, again, so reductive and simple is to be nice to people. I think being nice goes way further than people really realize. One thing that I've learned again, the hard way through life is you've no idea what's going on in people's lives. You've no idea. People could have all sorts of like really stressful, all sorts of personal stuff going on. And the reason they did the thing I worked that you didn't like is because of that.

1:18:29And so like, I try and think like, be nice. You don't know what's going on. Like you might learn later. 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 us. It's kind of too much of a, I don't know, Sophie, choose them 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 bite into that. My parents taught me a similar lesson. Yeah, it, and sometimes it's hard. I'd never fired anyone before I joined Intercom, for example. I didn't read, I really did not like doing it.

1:19:17And since then, I've done it quite a few times and a bunch of different circumstances and realized it always works out for both sides. And the nicest thing to do is to do the harder thing. You know, it's actually the nicer thing to do. People are like relieved in this example. It's, it's a better, it's a nicer thing to do. So it's, it can be a complicated one. I love it. Final question. You're Irish, you're based in Ireland. What is an Irish food? You think people should definitely try out if they ever visit Ireland. Can I cheat and say Guinness? Is that food? Absolutely. I'm the Guinness in Ireland.

1:19:57And I, 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 fresh product and it's brewed here. It's kind of like the way thing about it is it's like milk. Milk goes off, Guinness goes off. You know, Guinness is fast and it's you, the old and matured day's old tends to start deteriorating. So Guinness in Ireland is amazing because it's made here. The other thing I think that Ireland does really well is fish. Ireland has not had, by the way, the greatest reputation for culinary excellence over the years. I think Irish food in the States in particular isn't not good.

1:20:29But the fish here is incredible. You can get incredible fish. Ireland is obviously an island. So there's a lot of fish. On the Guinness front, is there any way to get the good stuff not in Ireland? Or is that just, you got to go? No, there is actually. You just need to be near a brewery. You need to be like, and so Guinness is brewed in Nigeria. It's a huge Guinness market in Nigeria. Not now that. I think they actually use a different recipe, but it's brewed there. I think the brewery in the US is somewhere in 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.

1:21:09The Guinness in San Francisco tends to be really bad. I remember talking to someone about this. So we're actually Guinness. One of my friends does a lot of work in Guinness. I think the boat carrier of the Guinness goes down through the Panama Canal, back up to San Francisco. So you're like, it's 12 weeks old or something. Wow. Did not think we would be learning about the travel path of Guinness from... I think 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.

1:21:41Two final questions. Working folks finding 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 -D -A -Y. That's everywhere. So Paddy at Gmail, Ak 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 it's more like a crypto web3. 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.

1:22:22So if people have opinions, I'd love to hear them. I'm just talking. 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. You really appreciate it. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or a leaving review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenyspodcast .com.

1:22:57See you in the next episode.