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

00:00:00 - 00:30:00

In this episode, Paul Adams, Chief Product Officer at Intercom, shares stories of failure and discusses the role of AI in product strategy. He emphasizes the importance of adapting and learning from failures, and highlights the success of Google Maps and Android. The conversation then shifts to the tension between high standards and the need to ship fast, and the potential of AI in product development. Adams advises staying informed and experimenting with AI technologies to avoid being left behind.

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00:30:00 - 01:00:00

The podcast episode features a discussion on the capabilities of AI, including code generation, image analysis, and voice and face replication. It explores the potential impact of AI on various professions and the concept of virtual afterlives. The importance of integrating AI into product development and the need for skilled machine learning engineers are emphasized. The challenges of implementing AI within organizations and the importance of considering alternative opinions are also discussed.

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01:00:00 - 01:22:59

The speaker discusses the challenges of product building and team building in a startup environment, emphasizing the importance of finding a balance between specialists and generalists. They also highlight the value of sharing stories of mistakes and crossing boundaries to gain a deeper understanding. The podcast explores the concept of product market fit, the role of storytelling, and the use of job stories and the four forces framework in understanding user needs. The hosts share their favorite AI products and life mottos, while also discussing the freshness and reputation of Guinness in Ireland.

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And 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, that'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 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.

But yeah, I think that you got to mark your product

and what AI can do and what it will be able to do.

And then ask yourself, okay, what are we gonna do?

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 work from our sponsors.

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Paul, thank you so much for being here

and welcome to the podcast.

Thanks, Sonny, 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 of the time.

They don't really scar you.

It just goes in the book that have scarred for life.

Yeah, it's a good long story short.

I was at Facebook just over a decade ago,

loved it at the time.

I think it was a great place to be at the time.

And basically San Francisco,

I did a lot of talks for Facebook internally and externally.

Facebook had a keynote slot,

always had a keynote slot at Cannes,

the world's biggest advertising festival.

And the year prior, Zuck had been interviewed,

he was the speaker, he'd been interviewed.

He'd 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 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, very similar talks 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'll ad-lib

and things get mixed around.

And it's kind of informal.

This was like, you know, media change,

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 mic'd up, cursed, never started laughing.

I was like, she said they're laughing at me.

You know, oh my God, this is...

But I can manage to turn it around.

I walked back out.

I'd kind of been disarmed internally in my head

and the mess of it went well.

But it was, and I was famous that night, you know,

out in Cannes afterwards, like on the sea front,

it's just like Rosé everywhere.

And yeah, I was famous and infamous for my performance.

I feel like you lived the worst nightmare

that everybody has when they're thinking about giving a talk.

And I think what's interesting is you survived.

And I think that's a really interesting lesson.

It's like you could freeze in front of thousands of people,

walk off stage, and then it works out okay.

Yeah, and it all happened kind of organically,

I guess, or very naturally, you know?

But yeah, ever since then,

every time I walk out onto a conference talk stage,

still today, I asked 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 was a kind of a transition to Facebook,

which was also kind of messy.

Can you just share a couple of stories from that time?

Yeah, similar to the face to the kind of like,

you know, walking off stage thing.

You live and learn and I was at Google for four years

and I was on Facebook for kind of two and a half years or so.

And in both of those companies,

this is at the height of the social,

you know, the kind of social tech wave was like at its peak.

Google were very afraid of the existential threat posed

by Facebook.

Facebook were very confident they could pull off

some kind of like new social advertising unit

that would be like an AdWords or something like that.

They would like, you know, destroy Google's revenue,

eat them from the inside out.

And so being there at the time was fascinating

and moving to new companies.

At Google, I worked on a lot of failed social projects,

like you mentioned.

Google Buzz, Google then later, Google Plus.

I think a lot of the motivation for those projects

came from a place of fear.

You know, it didn't come from a place of,

let's make a great product for people.

Let's like really understand the things people struggle

with when communicating with family and friends.

Like that's really, really try and create something wonderful.

It came from a place of fear.

And so during those times, I kind of learned, I think,

how not to lead in places.

And by the way, I should say, you know, at the time in Google,

there was other things happening that were amazing,

like Google were building Google Maps.

Incredible product, one of my favorite products.

I think one of the best products ever made.

They were building Android.

You know, I was kind of in, I was in the mobile team,

in the mobile apps team at the time that Android came out.

So I commit, you know, incredibly good product.

So I just happened to be in the social side,

which wasn't as good.

And yeah, we, Google Buzz was kind of a privacy disaster.

And Google Plus is similar.

And so kind of halfway through, I kind of published research

about groups and how, I've done a ton of research.

You got an interesting kind of side note there is,

at the time I was being asked, I was working in the research,

in the UX team as a researcher.

I was being asked to do a lot of tactical research,

like usability study type stuff.

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

was map out their social network, all the people in it,

their family, their friends, how they communicate.

We'd map on all the channels.

We'd talk about what worked well, what didn't.

And we did this with dozens and dozens of people

over the course of maybe 18 months.

And the same pattern emerged every single time,

which was people need way better ways

to communicate with small groups of family and friends.

And I kind of look back now and go, what's that?

Or maybe an 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.

And so halfway through, the research

that I'd kind of done, all this research,

had been made public through a conference talk.

And I saw it on Facebook.

Now I just got in touch.

One thing led to another.

And I left and joined Facebook, which

was an amazing thing for me personally.

Facebook was amazing, an amazing place at the time

and exciting.

And they were trying to do things for the other reasons,

the kind of good reasons.

Like, hey, let's build an amazing product for people.

And this was during Google+, being built.

You basically shifted.

Yeah, midway.

It hadn't been stressed, I 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 in Google+,

was sent to a different building.

That building had a different key card.

If you didn't work in Google+, you could not get in.

All sorts of counter-cultural things at the time.

As a result, there was a lot of 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,

some people saw me as a trader, understandably.

Other people thought I was enlightened,

you know, to fancy talks too.

But it was the right thing for me to do.

But at the time, 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.

When I left, Google kind of assumed

that I was one of the spies.

I was quarantined, I told them I was leaving.

They forensically analyzed my laptop,

like 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

has been run from a place of competitive fear, which

I don't think leads to good things.

So one of the themes through the story you just shared

is, let's say a 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.

I've personally failed so many times.

Their two stories, and the Google one

is like long, deep tentacles, they're two stories.

I've failed a ton of times.

Like at Intercom, I remember when I was at Facebook,

I was very happy, and I knew Owen and Dez

to the co-founders of Intercom.

And they're trying to persuade me to join Intercom.

We were like, 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 OK to try things.

I'm a big believer in big bets, high risk, high reward.

I don't get as excited about incremental things.

No, I haven't 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 Intercom

are in building software.

Like we have a principle called ship to learn.

And we've actually changed it since.

It's over on the wall here.

Ship fast, ship early, ship often is what it says now.

You say ship to learn.

Ship fast, ship early, ship often.

It's like, in that idea is the idea of failure.

It's not going to go right.

And it's going to go wrong more often than not.

But if you ship early and fast and learn fast,

you can change fast and you can improve fast.

And that's 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.

Like, 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?

Our hot day at a chat GPT launch, November 29th, I think, last year.

Ever since that day, I literally wake up every day thinking about AI,

pretty much, and I read as much as possible

and still feel like I'm way behind in it.

I think for me, when I talk to you about AI,

people typically fall into one of two camps.

You're either all in, 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 technology shift, the way it will shape society.

So I'm all in.

I've gone over the hill or whatever.

I'm over the other side.

So there's people in that camp.

I think there's people in another camp, which is,

I've heard this before, a type, like last year was crypto,

there was Web3, none of those things worked out.

There was the metaverse.

So there's definitely, I think, a lot of skepticism

or maybe cynicism around it.

I can understand why.

The other things didn't really pan out.

Well, the metaverse is kind of what's coming back.

And I kind of think about, I'm trying to remember,

there's a lot of the law where you have the hype

and then the trough of disillusionment

and then you kind of correct the other side.

Yeah, a little curve chart.

Yeah, and I think that's where a lot of people might be,

where the hype, there was so much hype.

It was so noisy and still is a little bit so noisy

that you kind of tune it out a little bit.

And some people have kind of fallen into that camp.

I'm all in in the other camp.

This is going to radically transform society

and it kind of blows my mind,

even seeing new types of things that come out,

like chat GPT vision just came out recently

and 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

with people that are just like,

I want to go deeper down the 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,

I'm in that too, I wake up every day

to too many emails and Slack chats

and people knocking on my door and my desk

and all kinds of things.

So this is the challenge for me too.

You just have to take the time.

There's just no other way for me.

And that to me doesn't mean, it's about priorities.

That 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 have 50 hours a week maybe.

I think beyond that you start to make bad decisions

and things like that, you're tired.

I need to live the rest of your life.

Like you got to put it into your day.

Whether that's like 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 chat GPT,

if you don't have like a kind of,

I can't remember if it's the pro license or whatever.

But if you haven't upgraded to get access to things

like GPT for vision, where you can take photos

and you have the mobile app.

And I got that for dinner last Friday night 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, can do all sorts of crazy stuff.

Like tell you how healthy the meal is or whatever.

Anyway, you got to try it.

You just got to try it.

So like my advice people is you've got to try,

you've got to set aside the time or it will pass you by.

It does remind me 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,

and also Airbnb Brian did this is he said,

any mocks you show me for new product designs

have to be in a mobile app or on a mobile web.

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

Right. Yeah, I'm at that same at Facebook.

Yeah, I guess that's right.

I guess do you think that that's the way it approached 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 are you thinking about

or have done of just like convincing people

this is where you want to spend your time?

Yeah, it's harder for sure.

It's harder because you want to force up.

Yeah, and a lot of the tech is invisible.

You know, like a lot of the things,

like we've been machine learning team

we've had in here for a long time.

So we've been working in the space for quite some time,

but it's funny, even if you go back like 18 months,

I think if I was on your podcast 18 months ago

and you said to me 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.

You know, so things change and my perception of it's changed,

but a lot of the improvements are kind of like

behind the scenes, you know,

there 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, you know,

design mobile mockups that are like AI mockups,

but I do think that like people need to need to start

really thinking strategically.

Like I don't know, maybe it's just not a mockup stage,

but start to think really strategically about their product

and whether it's in the line of the media or it's coming

or not, you know, it's not everything is.

And if so, for some, I think they require a kind

of a foundational strategic change.

Other is it might be less so,

but I think that's actually the head space

that I think people need to be in.

Can you unpack that further?

What do you, what does that look like to really think deeply

about whether your product is in the way of the media?

You can get sidetracked by the technology for sure.

And I do, I just mentioned, okay,

you're gonna have for dinner and taking a photo of my food,

you know, you can get sidetracked by the tech

and some of it's really cool.

I wouldn't start there.

I'd start with the thing your product does,

like what's the core premise behind it?

Why do people use it?

You know, what problem is it solved for them,

that kind of thing.

And then ask the question, so go back to basics.

Okay, what is my product for?

Why do people love it?

And then ask, can AI do that?

And for a lot, it's gonna be, yes, it can.

For some, it might be, it can partially do it.

Maybe for others, it can't do that, at least not yet.

And the types of things, you're gonna need to map

like what your product does against what AI can do.

And like AI can do a lot.

Like it can write, I'll try, I'll give you a 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 thing, 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, yeah, change my flight.

Change my flight to Tuesday, right?

It can do things like that.

And so it can do a lot of things.

It can 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 like, yeah, the media is coming

and they're like in its path.

And so for a lot of these products

that you just need to look at what AI can do.

And then for some of it, it'll be like kind of replacement.

AI will replace, it'll just do it.

And 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 a macro product

and 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

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 like, I'll give you an intercom first.

Like, again, I, you know, this date's kind of,

I think it was never at the time, right?

Like etched in our head, you know,

we have like Fergal who was our head of machine learning

and Fergal just turns around that day and he's like,

okay, 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, head of OpenAI said,

hey, one of the first industries

is going to be disrupted as customer service, right?

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 Fin.

We built other things first, actually Fin came later.

No, 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 Fin is

just for folks that aren't familiar?

Fin is, first and foremost is an AI chat bot.

So if you think about customer service,

you know, people have questions for a business.

And historically that was mostly email and phone

and mostly ticketing based to see it file a ticket.

You know, a lot of do not reply email and kind of so on.

And then came along conversational customer support,

which is just basic messaging like,

like WhatsApp or iMessage I mentioned earlier.

Now there's like, you know, bot first experiences

and Fin is an AI chat bot.

AI first, chat bot first.

So the first line of defense for a customer support team

is Fin, not a person.

And so it fundamentally changes.

And Fin can do, the results we've seen with Fin

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 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

and does our managers.

So anyway, that's what Fin is.

Fin has expanded.

So Fin is now also in our intercom inbox,

the place of people answer queries, customer support queries.

And now Fin's in there too, helping the support reps,

like suggesting answers for them to use

or helping them like 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 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.

So again, there's a lot of hype and buzz with AI.

So if you're measuring it by interest,

it's a huge success.

A lot of people are target customers, 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 like there's so much hype,

there's a bit of skepticism around it.

Does it actually work?

Is it as good as a person?

Hey, and 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 within.

So like we've some customers who see huge success,

but it's early, you know?

And so like how's it transformed our business?

Like financially, not yet, you know?

It's not like this kind of, you know,

oh, I think all fast growing startups,

you know, if you think of inter commas

or like AI inter commas,

I guess a new startup, even though we're 900 people,

you know, the kind of growth curve

you're looking for is kind of exponential curve.

I suppose like big public company kind of linear growth curve

with the exponential one, it takes a while.

You know, the first kind of year, two years,

it's like bottom of that.

And so I think 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 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,

but yeah, but there's not a manual setup,

like a customer support manager,

we 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

literally 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, you know,

but it can like work things out.

And I'm not one for going down into these

like really philosophical things.

Like I'm like, we just need to build it,

let's go back, building the product or whatever,

but it can work things out and that blew my mind.

And like we fed it a whole bunch of,

we fed chatGPT and other companies too,

like we played, you know, other LLMs like on Tropic

and so on, it can work things out.

And that was like kind of mind blowing.

Then you can see it doing things like writing code.

And 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 at Ingecom we have a kind of a one to five ratio.

So like a PM has about five engineers on a team.

You look at this thing, writing code and you're like,

what happens next?

You know, like, do we need as many engineers

or will their role change?

And they'll start doing different types of things

like reviewing code instead of writing code.

So that kind of blew my mind.

And then the visual stuff like I mentioned earlier,

I think the visual thing was bigger than the original one.

Like it can parse imagery and like,

you know, it can help you see the world.

You take a photo of your bike and say, hey, what's wrong?

And I'll tell you what's wrong, how to fix it.

You can be traveling, take photos of stuff.

It's in a different language.

It's like etched in stone on a like 12th century cathedral.

You're like, what does that say?

And it'll tell you what it says.

Like, it's just like, how to do that?

You know, this is what I'm actually repeating

most people these days.

Here in Ireland, if you want to be a radiologist,

you know, so like study x-rays and tell people

what's wrong and so on and so forth.

It's seven years training to like learn that 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.

You know, radiologists will not take x-ray.

Well, I guess it might take them,

but they won't analyze them for sure.

They'll look at what AI says, check that it's right.

And then it's like kind of bedside manner time.

Like, you know, tell the patient,

maybe tell them what kind of course.

So like the job just fundamentally changes.

And by the way, that could be amazing.

We have here in Ireland,

we have like long queues for hospitals,

epic waiting lists for people getting x-rays.

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 here.

Like that sounds like Paul.

And so I mentioned that the metaverse earlier.

I don't know if you saw Zuck talks to Lex Friedman,

see that?

So that was my first like, oh, like,

so it's the meta, if people haven't seen it,

they met in 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, like really, really close.

So 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 can still talk to them.

And like, actually the weirdest thing,

maybe it's not good for people, I don't know.

But that tech is like just around the corner, you know?

And the AI can like, that's kind of like,

your question is mind blowing, it's mind blowing.

There's actually a Black Mirror episode

with that same premise where-

That's right.

Yeah, and I don't think it ended well, so.

No, I like it.

I like it.

Careful.

For sure, for sure.

Yeah, it is like the, I think 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.

So, you know, and this tech is like, again, just here,

where like, if I was a native Spanish speaker

and couldn't speak English,

you and I could still have this podcast.

You know, it's been,

your voice would be translated in Spanish in real time for me.

It's like, again, mind blowing.

We're actually working on dubbing

slash translating podcast episodes,

which is all done through AI,

where it figures out what you're saying makes it Spanish

and then also changes your lips to match.

And we're trying to launch a couple of those

and that's actually very AI based, yeah.

That's cool.

That's pretty cool.

You mentioned that your Eng team might change.

You're thinking like,

because AI can make them much more efficient

and work differently.

I'm curious what you've seen actually change on your team,

either using AI-ish tools or just building AI products.

What do you think is most different?

And I'm curious, from the perspective of a team

that's trying to think about integrating AI

and starting to lean into AI,

what have you seen most change and should change?

Ultimately, you need really great machine learning engineers.

That's where it starts.

And if you don't have that,

then you're gonna find it hard

to build truly, really, truly great things.

So like what OpenAI provides and what Entropic provide

and Claude, they provide like amazing and amazing technology,

but you gotta build on top of it.

If you really want something brilliant,

you gotta build on top of it.

So we adapted what they build for 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 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 like kind of the first thing.

And of course, these people are in high demand.

So you need to invest in building out that function, I think.

Really invest in building out the function.

So that's what we've been doing.

Our ML teams way bigger than it was

and way bigger than it ever has been at Intercom.

And then kind of it forks.

So some projects are very heavy on that ML team

and it needs them, but other projects are more front end.

Like the inbox stuff I mentioned earlier,

where we have Finn and Finn is kind of working.

We've built the underlying technology.

Now it's a question of like,

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'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 setups

and increasingly we can do more with the latter,

more 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.

You know, I think some people are still in that camp.

Like again, I go back to the mobile thing.

There's just so many direct parallels with it.

Like I said earlier,

at Google I worked in the mobile app team.

I worked on mobile Gmail, mobile docs.

And it was like the mobile team.

And we were in London.

We're like, hey, we're the mobile team in London.

And meanwhile over in Mattingview in California,

no one cared, you know, it's like,

it was like, you're 20 people, we're 200.

No one uses this stuff on a phone.

No, and again, a lot of skepticism.

No one's gonna write docs on a phone.

Seriously, they're gonna write a document.

They're gonna write a full document on a phone.

Are you crazy?

You know, so don't do that.

You know, we're trying not to do that.

Like don't bolt it on.

Don't be like, I would have a bunch of AI people.

And we do have some specialists,

but generally speaking,

we're trying to like have everyone learn about it.

Interesting.

So I'm curious just specifically

what that looks like.

Don't bolt it on.

The idea there is don't just have like a side team

that's like, they're the AI team.

They're gonna add AI to all this stuff.

You're finding and let us the lesson

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

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, by the way, I'm a big believer in generalists.

Like a big, big believer in like,

I mean, I guess my background is like, you know,

Jack of all trades master and on.

That's probably how I describe myself.

Like I've worked as a researcher, designer, PM.

And so I'm believing generalists.

And so I believe in setting teams up that way.

And yes, specialism matters at times.

Like machine learning for sure is a deep specialism.

And at Intercom, we generally, in engineering too,

much prefer people who learn new things,

whether it's like a new coding language or framework

or, you know, 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 gonna get to

once they realize, oh, shit,

we really need to get big here

or they're already working on it.

I'm curious if there's other,

maybe pitfalls you ran into that you think people should

try to avoid and something you could share there

or just like any other lessons about making this transition

that you think might be useful to other people.

Yeah, what I've mentioned so far, don't,

yeah, don't bolt it on.

Don't keep, I stay up to date.

You know, like I mentioned, I like read, read.

Like I feel like I'm behind all the time.

This is moving so fast.

What are you reading?

What do you find is most interesting

and informative for reading about what's happening in AI?

I'd love to tell you that it's incredibly structured.

And, you know, a great reading list

that I got every Sunday morning.

It's pretty random.

I'm on Twitter, which is now called Xcourse a lot.

I follow some people on Twitter.

I actually use the recommended feed in Twitter a lot.

I think because I interact and look at a lot of AIs,

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.

I guess those are cool stuff there.

There's some newsletters as well

and some people I follow.

Any newsletters you could call out

that you think are most interesting.

Yeah, Matt Rickard is one guy who talks a lot about AI.

The blogs of companies too, like, you know,

open AI have a pretty good blog

and they write papers and summarize them.

Cool.

If there's any other ones you think of either people

on Twitter to follow or newsletters,

email me after and then we'll add them to the show notes.

Yeah, perfect.

Yeah, yeah, there definitely is.

I'll dig them out.

Your question earlier, how do you do it?

Just try, just book out half an hour

and just go deep for half an hour

and then bookmark a few things, come back to the minute.

Like everyone, like, you know, it could be so busy,

so many distractions,

you just got to have to set aside time.

Are there any other tools or apps

that you find really helpful?

Sounds like ChatGPT is kind of the center

of how you play around with it.

Is there anything else that you find really interesting?

I'll try other things like BARD.

You know, for example, like Google,

BARD is Google's kind of AI search engine.

Rewind is another, like, fascinating company.

I think it's Rewind.ai.

Rewind is basically augmented AI for your memory.

So you install it on your hard, on your, like, local machine

and it captures everything and remembers everything.

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's the boundaries and how's it work and so on.

So I'm a believer in that type of thing.

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When you started rolling out AI

and kind of leaning into this direction,

did you run into any big challenges

or hurdles organizationally or personal interests

or opinions?

I don't know, was there anything you ran into?

That was a big stumbling block

and something you had to get over.

Like any company, Intercom is full of diverse opinions

about things, 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, 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,

like 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 you're senior leadership team

in the company, you're 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,

are you know, are you, why are you all in?

And I'm like, an educated guess, a hunch.

You know, a lot of it's like the kind of the,

the part of like business strategy and product strategy

that you just, it's just hard.

It's just kind of, it's like taste.

You know, people talk about taste, product taste.

Who has product taste?

And a lot of it is like,

it's judgment based on experience.

That's all I can say.

Like, I don't know.

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've a lot of debate here about the future.

You know, Ferg, 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, was it a pitch

or what, I don't know, about how maybe all of our road map

with AI is wrong.

Maybe we're like kind of, I don't know if you think

are familiar with the horizons framework,

like Horizon 1, 2, and 3, or like Amazon.

Yeah, so like Horizon 1 is kind of the medium,

short to medium term, like next 12 months,

12, 18 months, Horizon 2 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 1 land.

We're like, yeah, and then next year we're gonna do this.

And he's like, yeah, but two years from now,

if this path plays out, everything we're doing now

is like going to be irrelevant and like useless.

You're like, okay, you know, and so like those discussions

happen and the level of ambiguity is off the charts.

So a lot of the challenges have been navigating

that ambiguity and helping people get the conviction

I have, you know, without kind of drowning out voices

of like alternative voices and opinions,

which are often valid too.

What has helped people get that conviction

is just showing them examples of here's something,

wow, look at this thing, this is unreal.

And I think partly what helps I imagine

is the market you're in seems like

such a clear opportunity for AI feels like an easier pitch

than maybe a lot of other markets.

Yeah, that's true, for sure.

That's true.

Yeah, showing people is definitely like the easiest way.

I think, yes, the customer support is definitely

like I said, or Sam was like, number one, customer support.

So you're like, okay,

I guess we should adapt, 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,

you know, how do they work?

Well, they're like the typical kind of like,

you know, read, write app, build dashboards,

filtering, querying, you know, kind of hardcore querying,

kind of query database, get some numbers, show it in a UI,

a lot of thought and care goes into like,

how you present that data to people,

the 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 Hozer best sales one last year, January.

Okay, who is our top performing rep in January, you know,

Lenny, like the reporting products of the future

might look like that.

And so project management tools is another one.

It was a bunch of products that I think are just outside

the most obvious customer support one

and yet equally ripe for a newcomer to come

with a completely different paradigm

and potentially take over.

I like that this connects back to your very first point

about trying to think about where AI integrates us,

think about what problem are you solving as a company?

For example, Tableau, helping people visualize data.

And then the question is, can AI just do this for you?

And in that case, oh, maybe you can.

And that gives you basically a whole strategy of like,

okay, how do we actually do that with AI?

Yeah, it's very hard to, you know, if you're,

I don't know if the reporting thing will play out that way,

but you know, if you're like a Tableau type company,

you've tons of designers who 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 your 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 lot of crap, you know?

So like it's very easy.

I've been guilty of this many times.

Back to like mistakes you've made,

like I've been guilty of this many times where like,

I've jumped on a bandwagon and it was all wrong.

And like the older I get, like the web three thing,

I'm like, I don't even know what web three is.

I never bought crypto.

Maybe I'm wrong about that.

But I'm not a bandwagon jumper, you know?

But I kind of maybe might have been when I was earlier.

So like, and I try these days to read

the alternative opinion.

People who are skeptical or 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 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're gonna 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, those are 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 saying like, hey, everyone's gonna lose their job.

A boss gonna take over.

It's like, maybe some of that will happen,

but probably to attrition.

I was like, people, someone quit

and just didn't get backfilled.

So, you know, the doomsday scenarios

that I don't think will play out as much.

But for sure, like, you know,

it's easy to kind of be afraid of it.

And I think you kind of have to lean into it.

I love that.

Okay, I wanna 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.

And 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?

Before, after is literally that simple, I think.

Like we've a rebrand at the moment happening.

And that'll be it before, after a moment, you know?

We're redesigning our pricing.

And then the day that pricing goes live,

that'll be it before, after.

Cause it was like, nothing's the same.

And so we need to go back out and talk to people again.

Like I'm a big believer in talking.

You gotta talk to customers.

It's the only way.

You gotta 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 gotta 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 align price to value.

By the way, I think pricing is incredibly difficult.

A lot of the design team who are comprising here,

you know, I say to them like,

it's one of the hardest design problems I know.

Like, and onboarding is another one.

Onboarding people into a product is also,

like 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 align 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 prices of 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 $5,000

for those 10 units of value.

You know, so the biggest mistake was,

a lot of mistakes compounded.

And this is an area where I think we were a risk averse.

We ended up, 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 gonna start again.

Well, this feels like it could be his own episode.

Just talking through your pricing lessons and journey.

Maybe as 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,

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

and 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 they didn't have.

So let's charge like more for Y.

That doesn't really work with the other.

Okay, let's look at an add-on.

Oh yeah, cool, people just add-on.

But then, like later, then you've got like

people who have the add-on and people who don't

and then you're like, add another thing.

And so like tiering, we've like added tiers.

We've like, you know, cut with products, tiers, add-ons,

tiering in the add-on, you know,

people can't understand their bill.

So my advice is keep it simple.

Reject like fight so hard to not,

to like resist the temptation to add extra ways

in which you price.

Amazing, I didn't think about going into this topic

but I'm glad that we touched on it.

Okay, I think I was talking about scars for life earlier.

That's another scar for life.

All right, let's keep talking about some frameworks.

Another that I found that I loved

is something that you call differentiation

versus table stakes, what's that about?

It's kind of like the Kano model,

if you're familiar with that, but it's very simple.

It's kind of like, I guess we took the Kano model

and just tried to make a really crazy,

simple version of it.

Again, like I'm a little bit allergic to things like this.

I can't even hate myself for bringing up the Kano model.

I'm allergic to like people over-intellectualizing frameworks

and like, 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 table stakes, very simple.

I think people who adopt a product or buy a product

or switch to a product,

there's kind of two driving forces.

One is the attraction of the new solution.

And that's basically differentiation.

So what's different and better?

But critically, what's different and better

in ways that customers care about?

Again, back to all the failed projects,

my lesson for 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 wanna check out that.

That looks cool.

I wanna check that out.

That looks better than what I have today.

But on the other side,

there's like a kind of entry requirement

or like table stakes.

You know, to play the game,

you gotta have a certain amount of things.

And so they're table stake features.

They're often very boring.

You know, they're like real basic stuff, boring stuff

and easy to ignore and easy to not build.

And again, a mistake with Intercom maybe over the years

is that we were much more attracted to the differentiation

and built a lot of that.

So we went through different iterations of our roadmap,

sometimes like changing over the course of a year or two

where we were like 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 roadmap was built based on those,

but like trading off,

why do we need more differentiation or trading off?

Why do we need to invest more table stakes?

And so these days, the place that Intercom today is like,

we're kind of 50-50 probably in terms of resources,

but it has swung 70-30 in both directions at times.

The last piece about it is,

I think it's really powerful to like look at a roadmap

or look at a proposed roadmap and ask yourself,

which of these two things matters more to us,

not with 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,

service now, service cloud, Salesforce, Zendesk,

like decades of table stake feature building.

So to play the game,

you need a lot of the table stakes

unless you have incredible differentiation.

So from the early years of Intercom,

people just buy us alongside service cloud or Zendesk.

They just buy us alongside.

They're like this Intercom thing.

We were like messenger first, modern, messaging,

and modern 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 kind of, if you're a startup,

you need to invest a lot more in differentiation.

And then over the years,

I think you start to balance the books a bit.

I think what's interesting about this is one,

it just gives you a way to think about looking at your roadmap.

How much are we actually doing?

And are we doing too much table stakes?

Are we doing too much differentiation?

So it gives you kind of an awareness of what's happening.

And I think there's also interesting,

it's an interesting strategy as a startup.

Like do we spend years doing table stakes

and then launch or as they go,

the way Intercom went, like differentiate first,

we'll build everything else later.

Wonder when it makes sense to go one or the other.

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 call

swinging the pendulum.

What is that about?

I actually kind of mentioned an example of it earlier.

Like the differentiation table stakes

was swinging the pendulum.

So swinging the pendulum means you take a step back

from everyday work life and you kind of make the observation

that something's in an undesirable state.

So like, you know, maybe it's,

whoa, we've all the differentiation in the world,

but people can't adopt the product

because we've never built any of these table stakes.

That's like undesirable.

Or, oh, we've now built all these table stakes

and we've not been investing in differentiation.

And actually, we're not that attractive to people

because switching product is like a pain

and we're not just not attractive to people.

We need to like, okay, so there's undesirable state.

And then, so you go and fix it,

but the temptation is that you 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, so 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 intercom,

we were on this kind of high growth trajectory,

really kind of 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 we know what we're doing?

We need to hire some experts.

We need to hire some experts.

Like, you know, if we're gonna go up market,

we need to up market people who've done it before.

So, you know, that was like, undesirable state.

Fix it by hiring people who've done it before.

Let me hire loads of people who've done it before.

And what they did was brought the culture

and ways of working of their prior company to intercom.

And so we totally over corrected.

Didn't work out for it 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

was a matter where we really over corrected

to find out, like, okay, it's a balance here.

Related to that one,

related to hiring one is like, generalists and specialists.

Kind of similar theme.

People have done it before.

Or people who are specialized.

And we hired a bunch of specialists only to realize

that they're not adaptable.

And in intercom, 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-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 I'm a bit of a specialist, 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 don't work out.

Cause 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 on the way.

And it feels like this framework

as a result of just doing this too many times,

is the main lesson here,

generally avoid swinging the pendulum too far.

Because sometimes it's worth it.

Like in this case of AI is like,

no, we're going all in or in mobile.

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

or 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 pretty high.

Maybe I overestimated earlier,

like, 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.

And that's 50-50, I think I mentioned 50-50 earlier.

So that's 50-50.

So we're not totally swinging it.

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.

Reminds me, to know where the boundary is,

is what I was gonna say.

Reminds me of 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.

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, not advocating it's necessarily good.

It certainly didn't help their brand.

But there was kind of an idea that,

to know where the boundary is, you gotta cross it.

And crossing, it's painful.

But if you don't cross it, you'll never know.

So if you think you're going up to the boundary,

then you stop before it,

it turns out it's actually miles over there.

You know, so I think with a lot of this stuff,

you don't really have a choice.

You're going to kind of cross the boundary.

Feel the pain.

Be humble enough to realize you didn't get it right.

And kind of go again or whatever the right course act,

corrective courses.

Yeah, get that pendulum off the even like pivot thing

that it's on and then,

oh, and then let's fix that pendulum.

Let's put it back.

Yeah, yeah.

Okay.

Another framework that I read about briefly,

and I love the general idea of it already,

which is something that I think you call

product market story fit.

Yeah.

What is that?

So yeah, with product market fit,

pretty basic, well understood, very important.

You know, the way I just 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.

They didn't really care.

They didn't really care.

Like, you know, what the hell, it's 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.

You know, it's the why.

If that's the market, so who the products to 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 story's wrong or the story's missing.

And so sometimes it would be a great product

in a great market, explained in a convoluted way.

Like that, I see that a lot.

I used to see that a lot at Google again.

Just explained in a very complicated way,

over-intellectualized.

And as a result, people are like, what are you talking about?

You know, you don't get their attention.

And so the story is really important, as important.

And actually sometimes you'll see like, not great products,

certainly worse on paper.

I'm trying to remember like the Spotify competitor

back in the day, people were like,

what was the name of it?

Ardio?

Yeah, Ardio.

Ardio was one of these where like, yeah, people like,

great, like people, all I've ever heard of Ardio

was amazing product, it's failed.

You know, and why did it fail?

Spotify and Ardio are the same market.

They were solving the same set of problems.

Ardio was arguably the better product at the time.

I don't know if that's true, but Ardio is the better.

I also 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, 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.

Mm-hmm.

Makes me think about positioning

and how important that is.

And we had April Dunford on the podcast

very recently talking a lot about that.

Yeah, yeah, yeah, she's excellent.

Yeah, it is really, like, why are you better, you know?

And can you explain why you're better?

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 Sriram 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 to do this.

Like we worked with Bob West, you know,

who was age of eight years ago, Bob's right guy.

And we kind of followed that model of jobs to be done

more than the ODI, I think is the other skill at all.

Anyway, I'll try and say this in a simple way.

We've found jobs to be really good.

You're very, very useful.

But in a very simple way,

you can come 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 it's just in peculiarities

of one version, who cares?

Like no one cares.

Oh, well, I don't care, they care, obviously.

But I'm like, your customers don't care.

Like people are trying to build a product for it, don't care.

No one cares.

That's like a cool intellectual debate,

but like kind of for me, maybe 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 product.

And so for us, which else 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?

Again, energy, there's a lot of energy around it.

Maybe the energy thing might have come from talking

to Bob actually, I think about it.

I think it did actually.

I think like the idea of like this idea

that you need people who have a lot of energy

around the problem and you kind of have to interview them

for that most of the time to feel the energy they have.

You know, like it's very easy to see

if someone's apathetic versus like into it.

So we've had a pretty good and we invented this job

stories thing kind of by accident.

I can't remember exactly what happened,

but like I wrote out this way of writing a job story, basically.

Well, we didn't call it job story.

Someone else called it that.

We just at the time were like,

there was this, I can't even remember,

you know, it was like a trigger and an act.

Anyway, we didn't even give it the thing a name.

Someone else named it, I think.

And I'm just like, we're just trying

to build a great product, you know?

So like we'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 four people.

There's different forces when people try and switch product.

And 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 was saying earlier that like Owen and Daz

are trying to convince me to leave Facebook, which I loved

at the time, join and 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, I'm 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.

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 recommended most to other people?

Yeah, the two books I recommend to everyone always.

I've copies in my office here.

It's not how good you are.

It's how good you want to be.

It's a book by Paul Arden who has worked

in advertising a long time ago.

It's an excellent book.

It kind of shows people that you feel unlimited potential

if you think about it the right way.

Everyone does.

The second book I recommend to everyone

and buy for people and gives to them is Principles

by Ray Dalio.

I'm a big fan.

Ray Dalio, I think he's incredible.

I'm a big believer in Principles.

A lot of us at Intercom are.

I always get those two books.

And they're totally different.

The Paul Arden book, you can read it in 20 minutes.

Principles is like that thick.

What is a favorite recent movie or TV show

that you've really enjoyed?

Most recent is The Bear, which I came to late.

The reason I actually love the show

is because I think it somewhat celebrates the grind.

And I think that's important.

I worked in coffee shops a lot when I was younger.

I put myself through college and stuff.

And the grind is part of life.

It's a necessity to get things done

and make great things happen sometimes.

And I liked that about it.

I really liked that about it.

What is a favorite interview question

you like to ask candidates?

Yeah, I'll give you a slightly different answer.

I don't really have said a few questions for candidates.

And I don't like, I don't like,

I don't ask questions adversely.

I don't like questions that rely on memory.

Tell me what 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 like you're interviewing someone,

you want to give them the job and they've got referees.

And of course the referees they have

are like the best people that they ever worked with

and their favorite managers.

So this question is, what feedback will I be giving

this person in their first performance review?

That's an amazing question,

because the person can't dodge it.

There's an answer and it's incredibly enlightening.

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

Yeah, on reference calls.

That is such a good question.

I love it.

It's a great, amazing question.

All right, what a gem.

Thank you for sharing that.

What is the 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 chat, GPT 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.

I'm a bit late to that,

but it's just like augmented memory.

It's 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.

Yeah, I'm not so sure about that.

Yeah, I got some plaque.

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.

And it's if you chose into the what's okay and not okay

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 a posted on my monitor

that says only work on what matters most.

It's on my monitor, it's posted.

And if someone falls off, I have to write it again.

Only work on what matters most.

I'm 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 they're related is

stop worrying about things you can't control.

And so I have two of those.

And so only work on matters most,

stop worrying about things you can't control.

It just like reduces the temperature.

Again, like life lessons learned,

I send a lot of dumb emails in my past, you know,

like red energy, oh my God, what are they thinking?

You know, like you wake up and double

into 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, get a coffee, come back.

Is it really moderate, you know?

Beautiful.

That's second one I think I learned first

from seven habits of highly effective people.

You read that?

Yeah.

I've just think about the focus,

the circle that you have things you 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 they're little models.

Yeah, the post-it on the monitor is a real life hack

I found a few years ago.

It's like, it's kind of dumb in a way.

The post's on the monitor.

It's in the way, you know?

Well, you actually put it on the monitor

in the way of your screen.

Yeah.

Oh wow.

It's in 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 and your dad

taught you?

The biggest one, again, is 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 really stressful,

all sorts of personal stuff going on.

And the reason they did the thing

at work that you didn't like is because of that.

And so I try and think, be nice.

You don't know what's going on.

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 it

because it's kind of too much of a, I don't know,

fluffy chosen or whatever.

I 1,000% resonate with that.

I've been told I'm too nice.

And I had to become a little less nice.

But I still can't lose that.

So I fully bite into that.

My parents taught me a similar lesson.

Yeah.

And sometimes it's hard.

I'd never fired anyone before I joined Intercom, for example.

I really did not like doing it.

And since then, I've done it quite a few times

in a bunch of different circumstances

and realized it always works out for both sides.

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?

Can I cheat and say Guinness?

Is that food?

Absolutely.

The Guinness in Ireland, people talk about this.

And it's true.

The Guinness in Ireland is much, much better

for a whole bunch of reasons.

It's basically a fresh product and it's brewed here.

It's kind of like, the way they think about it is like milk.

Milk goes off, Guinness goes off.

The 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 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.

But the fish here is incredible.

You can get incredible fish.

In Ireland, it's obviously an island, so there's a lot of fish.

On the Guinness front, is there any way

to get the good stuff, not in Ireland?

Or is that just, you got to go?

No, there is actually.

You just need to be near a brewery.

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 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,

who works in Guinness.

One of my friends does a lot of work in Guinness.

I think the boat carrier, the Guinness,

goes down through the Panama Canal, back up to San Francisco.

So you're like, it's 12 weeks old or something.

Wow.

Did not think we would be learning about the travel path

of Guinness from.

At least this is what I've heard.

The Guinness is so many myths.

You just don't really know what's true,

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 that is everywhere, basically, P-A-D-D-A-Y.

It's like patty with an extra A. So P-A-D-D-A-Y.

That's everywhere.

So patty at Gmail, at patty.

That's my handle everywhere.

So that's where you can find me.

I'd love, yeah, I'd love people to reach out to me.

I'd genuinely learn.

I'd love to hear from people who think my AI talk is nonsense.

And it's more like a crypto Web 3.

I'd love to hear people who have alternative opinions

and challenge mine.

That's how I kind of like to learn and get better.

So if people have those opinions, I'd love to hear them.

I'd love to talk to them.

Be careful what you wish for.

The YouTube comments are always a spicy place.

We'll see what we see.

Awesome, Paul.

Thank you again so much for being here.

Yeah, thanks, Danny.

I really appreciate it.

Bye, everyone.

Thank you so much for listening.

If you found this valuable, you can subscribe to the show

on Apple Podcasts, Spotify, or your favorite podcast app.

Also, please consider giving us a rating or leaving a review,

as that really helps other listeners find the podcast.

You can find all past episodes or learn more about the show

at LenniesPodcast.com.

See you in the next episode.

Machine-generated transcript that may contain inaccuracies.

Keywords

AI, product strategy, failure, conviction, pricing, organizational change, product development, emerging technology, Intercom, implementing AI

People

Paul Adams

Companies

Intercom, Google, Facebook, Dyson

Organizations and Institutions

Facebook, Google, Dyson

References

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Paul Adams is the longtime chief product officer at Intercom, where he leads the product management, product design, data science, and research teams. Before Intercom, Paul was the global head of brand design at Facebook, a senior user researcher at Google, and a product designer at Dyson. He’s also a best-selling author, a podcast host, and a public speaker. In today’s episode, we discuss:

• Practical advice on integrating AI into your organization

• Tips and tools for learning AI as a PM

• Hilarious stories from Google and Facebook

• How to build conviction with skeptical coworkers

• Lessons learned from pricing at Intercom

• How Intercom implemented JTBD

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Brought to you by Eppo—Run reliable, impactful experiments | Hex—Helping teams ask and answer data questions by working together | HelpBar by Chameleon—The free in-app universal search solution built for SaaS

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Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today’s transcript will be live by 8 a.m. PT.

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Where to find Paul Adams:

• X: https://twitter.com/Padday

• LinkedIn: https://www.linkedin.com/in/pauladams/

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Where to find Lenny:

• Newsletter: https://www.lennysnewsletter.com

• X: https://twitter.com/lennysan

• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/

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In this episode, we cover:

(00:00) Paul’s background

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

(07:28) Insights from Google+ days

(12:31) Learning from failure

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

(15:17) Integrating AI into product strategy

(17:31) Making time for AI learning

(19:37) AI in new-product development

(21:16) Questions to ask about your product

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

(25:13) Intercom’s AI chatbot, Fin 

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

(28:53) Mind-blowing capabilities of AI

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

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

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

(42:44) Hurdles implementing AI at Intercom

(45:52) Building conviction around AI

(49:52) Why you shouldn’t fear AI

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

(51:54) Pricing lessons from Intercom

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

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

(1:05:21) Paul’s “product market story fit” framework

(1:08:23) His take on JTBD

(1:11:01) How Intercom uses the “four forces” framework

(1:12:54) Lightning round

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Referenced:

• Intercom: https://www.intercom.com/

• The New ChatGPT Can “See” and “Talk.” Here’s What It’s Like: https://www.nytimes.com/2023/09/27/technology/new-chatgpt-can-see-hear.html

• Fergal Reid on X: https://twitter.com/fergal_reid

• Intercom’s AI chatbot, Fin: https://www.intercom.com/drlp/fin

• Mark Zuckerberg: First Interview in the Metaverse | Lex Fridman Podcast #398: https://www.youtube.com/watch?v=MVYrJJNdrEg

• Black Mirror “Joan Is Awful” episode: https://www.imdb.com/title/tt20247352/

• Mission: Impossible on Prime Video: https://www.amazon.com/Mission-Impossible-Tom-Cruise/dp/B000X4IRE4

• Anthropic: https://www.anthropic.com/

• Claude: https://claude.ai/

• Matt Rickard’s newsletter: https://substack.com/@mattrickard

• OpenAI’s blog: https://openai.com/blog

• Google Bard: https://bard.google.com/

• Rewind: https://www.rewind.ai/

• The Three Horizons Framework: https://medium.com/fact-of-the-day-1/the-three-horizons-framework-9d7ac0fbea21

• Sam Altman on X: https://twitter.com/sama

• Tableau: https://www.tableau.com/

• Kano model: https://www.productplan.com/glossary/kano-model/

• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennyspodcast.com/the-ultimate-guide-to-jtbd-bob-moesta-co-creator-of-the-framework/

• Hot takes and techno-optimism from tech’s top power couple | Sriram and Aarthi: https://www.lennyspodcast.com/hot-takes-and-techno-optimism-from-techs-top-power-couple-sriram-and-aarthi/

• Outcome-Driven Innovation: JTBD Theory in Practice: https://jobs-to-be-done.com/outcome-driven-innovation-odi-is-jobs-to-be-done-theory-in-practice-2944c6ebc40e

• The Four Forces Framework: https://thefourforces.com/four-forces-framework/

• It’s Not How Good You Are, It’s How Good You Want to Be: https://www.amazon.com/Its-Not-How-Good-Want/dp/0714843377/

• Principles: Life and Work: https://www.amazon.com/Principles-Life-Work-Ray-Dalio/dp/1501124021

• The Bear on Hulu: https://www.hulu.com/series/the-bear-05eb6a8e-90ed-4947-8c0b-e6536cbddd5f

• “Terry (Olivia Colman) and Richie peel mushrooms” scene from The Bear: https://www.youtube.com/watch?v=f7D8THR_osU

• The 7 Habits of Highly Effective People: Powerful Lessons in Personal Change: https://www.amazon.com/Habits-Highly-Effective-People-Powerful/dp/0743269519

• Guinness: https://www.guinness.com/

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

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Lenny may be an investor in the companies discussed.

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