Kevin Systrom: Instagram #243

Transcript

00:00:00 The following is a conversation with Kevin Systrom,

00:00:02 cofounder and long time CEO of Instagram,

00:00:06 including for six years after Facebook’s acquisition

00:00:09 of Instagram.

00:00:10 This is the Lex Friedman podcast.

00:00:13 To support it, please check out our sponsors

00:00:15 in the description.

00:00:16 And now, here’s my conversation with Kevin Systrom.

00:00:21 At the risk of asking the Rolling Stones

00:00:24 to play Satisfaction, let me ask you about

00:00:26 the origin story of Instagram.

00:00:28 Sure. So maybe some context.

00:00:30 You, like we were talking about offline,

00:00:32 grew up in Massachusetts, learned computer programming there,

00:00:36 liked to play Doom II, worked at a vinyl record store.

00:00:40 Then you went to Stanford, turned down Mr. Mark Zuckerberg

00:00:46 and Facebook, went to Florence to study photography.

00:00:49 Those are just some random, beautiful,

00:00:51 impossibly brief glimpses into a life.

00:00:54 So let me ask again, can you take me through

00:00:56 the origin story of Instagram, given that context?

00:00:59 You basically set it up.

00:01:01 All right, so we have a fair amount of time,

00:01:04 so I’ll go into some detail.

00:01:05 But basically what I’ll say is,

00:01:09 Instagram started out of a company actually called Bourbon,

00:01:13 and it was spelled B U R B N.

00:01:16 And a couple of things were happening at the time.

00:01:19 So if we zoom back to 2010, not a lot of people remember

00:01:22 what was happening in the dot com world then,

00:01:25 but check in apps were all the rage.

00:01:29 So.

00:01:30 What’s a check in app?

00:01:31 Gowalla, Foursquare, Hotpotato.

00:01:34 So I’m at a place, I’m gonna tell the world

00:01:36 that I’m at this place.

00:01:37 That’s right.

00:01:38 What’s the idea behind this kind of app, by the way?

00:01:41 You know what, I’m gonna answer that,

00:01:42 but through what Instagram became

00:01:45 and why I believe Instagram replaced them.

00:01:48 So the whole idea was to share with the world

00:01:49 what you were doing, specifically with your friends, right?

00:01:53 But there were all the rage,

00:01:54 and Foursquare was getting all the press.

00:01:56 And I remember sitting around saying,

00:01:57 hey, I wanna build something,

00:01:58 but I don’t know what I wanna build.

00:02:00 What if I built a better version of Foursquare?

00:02:04 And I asked myself, well, why don’t I like Foursquare

00:02:07 or how could it be improved?

00:02:10 And basically I sat down and I said,

00:02:13 I think that if you have a few extra features,

00:02:16 it might be enough.

00:02:17 One of which happened to be posting a photo

00:02:19 of where you were.

00:02:20 There were some others.

00:02:21 It turns out that wasn’t enough.

00:02:23 My co founder joined, we were going to attack Foursquare

00:02:27 and the likes and try to build something interesting.

00:02:30 And no one used it.

00:02:31 No one cared because it wasn’t enough.

00:02:32 It wasn’t different enough, right?

00:02:35 So one day we were sitting down and we asked ourselves,

00:02:38 okay, it’s come to Jesus moment.

00:02:40 Are we gonna do this startup?

00:02:43 And if we’re going to,

00:02:44 we can’t do what we’re currently doing.

00:02:46 We have to switch it up.

00:02:47 So what do people love the most?

00:02:48 So we sat down and we wrote out three things

00:02:51 that we thought people uniquely loved about our product

00:02:54 that weren’t in other products.

00:02:57 Photos happened to be the top one.

00:02:59 So sharing a photo of what you were doing,

00:03:01 where you were at the moment

00:03:03 was not something products let you do really.

00:03:06 Facebook was like, post an album of your vacation

00:03:09 from two weeks ago, right?

00:03:11 Twitter allowed you to post a photo,

00:03:13 but their feed was primarily text

00:03:15 and they didn’t show the photo in line

00:03:17 or at least I don’t think they did at the time.

00:03:19 So even though it seems totally stupid

00:03:23 and obvious to us now, at the moment then,

00:03:27 posting a photo of what you were doing at the moment

00:03:29 was like not a thing.

00:03:32 So we decided to go after that

00:03:34 because we noticed that people who used our service,

00:03:36 the one thing they happened to like the most

00:03:38 was posting a photo.

00:03:40 So that was the beginning of Instagram.

00:03:41 And yes, like we went through and we added filters

00:03:44 and there’s a bunch of stories around that.

00:03:46 But the origin of this was that

00:03:48 we were trying to be a check in app,

00:03:49 realized that no one wanted another check in app.

00:03:52 It became a photo sharing app,

00:03:54 but one that was much more about what you’re doing

00:03:56 and where you are.

00:03:57 And that’s why when I say,

00:03:58 I think we’ve replaced check in apps,

00:04:01 it became a check in via a photo

00:04:03 rather than saying your location

00:04:06 and then optionally adding a photo.

00:04:08 When you were thinking about what people like,

00:04:11 from where did you get a sense

00:04:13 that this is what people like?

00:04:14 You said, we sat down, we wrote some stuff down on paper.

00:04:18 Where is that intuition that seems fundamental

00:04:21 to the success of an app like Instagram?

00:04:26 Where does that idea,

00:04:27 where does that list of three things come from exactly?

00:04:31 Only after having studied machine learning now

00:04:33 for a couple of years, I like, I have a…

00:04:36 You have understood yourself?

00:04:39 I’ve started to make connections,

00:04:40 like we can go into this later,

00:04:43 but obviously the connections between machine learning

00:04:50 and the human brain, I think are stretched sometimes.

00:04:53 At the same time, being able to back prop

00:04:57 and being able to look at the world, try something,

00:05:01 figure out how you’re wrong, how wrong you are,

00:05:04 and then nudge your company in the right direction

00:05:08 based on how wrong you are,

00:05:10 is a fascinating concept.

00:05:12 And I don’t, we didn’t know we were doing it at the time,

00:05:14 but that’s basically what we were doing, right?

00:05:17 We put it out to, call it a hundred people,

00:05:20 and you would look at their data.

00:05:22 You would say, what are they sharing?

00:05:25 Like what resonates, what doesn’t resonate?

00:05:26 We think they’re gonna resonate with X,

00:05:29 but it turns out they resonate with Y.

00:05:31 Okay, shift the company towards Y.

00:05:33 And it turns out if you do that enough quickly enough,

00:05:36 you can get to a solution that has product market fit.

00:05:39 Most companies fail because they sit there

00:05:42 and they don’t, either they’re learning rates too slow,

00:05:45 they sit there and they’re just,

00:05:46 they’re adamant that they’re right,

00:05:47 even though the data is telling them they’re not right,

00:05:50 or they’re learning rates too high

00:05:53 and they wildly chase different ideas

00:05:55 and they never actually settle on one

00:05:57 where they don’t groove, right?

00:05:59 And I think when we sat down

00:06:00 and we wrote out those three ideas,

00:06:01 what we were saying is, what are the three possible,

00:06:05 whether they’re local or global maxima in our world, right?

00:06:10 That users are telling us they like

00:06:12 because they’re using the product that way.

00:06:14 It was clear people liked the photos

00:06:16 because that was the thing they were doing.

00:06:18 And we just said, okay, like,

00:06:20 what if we just cut out most of the other stuff

00:06:22 and focus on that thing?

00:06:25 And then it happened to be a multi billion dollar business

00:06:27 and it’s that easy by the way.

00:06:30 Yeah, I guess so.

00:06:32 Well, nobody ever writes about neural networks

00:06:34 that miserably failed.

00:06:36 So this particular neural network succeeded.

00:06:38 Oh, they sell all the time, right?

00:06:40 Yeah.

00:06:41 But nobody writes about it.

00:06:41 The default state is failing.

00:06:42 Yes.

00:06:44 When you said the way people are using the app,

00:06:48 is that the loss function for this neural network

00:06:51 or is it also self report?

00:06:52 Like, do you ever ask people what they like

00:06:54 or do you have to track exactly what they’re doing,

00:06:58 not what they’re saying?

00:07:00 I once made a Thanksgiving dinner, okay?

00:07:03 And it was for relatives and I like to cook a lot.

00:07:08 Okay.

00:07:09 And I worked really hard on picking the specific dishes

00:07:13 and I was really proud because I had planned it out

00:07:17 using a Gantt chart and like it was ready on time

00:07:19 and everything was hot.

00:07:21 Nice.

00:07:22 Like, I don’t know if you’re a big Thanksgiving guy,

00:07:23 but like the worst thing about Thanksgiving

00:07:25 is when the turkey is cold and some things are hot

00:07:28 and some things, anyway.

00:07:29 You had a Gantt chart.

00:07:30 Did you actually have a chart?

00:07:31 Oh yeah.

00:07:32 OmniPlan, fairly expensive, like Gantt chart thing

00:07:36 that I think maybe 10 people have purchased in the world,

00:07:39 but I’m one of them and I use it for recipe planning

00:07:42 only around big holidays.

00:07:44 That’s brilliant, by the way.

00:07:45 Do people do this kind of…

00:07:47 Overengineering?

00:07:48 It’s not overengineering, it’s just engineering.

00:07:50 It’s planning.

00:07:51 Thanksgiving is a complicated set of events

00:07:54 with some uncertainty with a lot of things going on.

00:07:57 You should be able, you should be planning it in this way.

00:07:59 There should be a chart.

00:08:00 It’s not overengineering.

00:08:01 I mean, so what’s funny is, brief aside.

00:08:04 Yes, it’s brilliant.

00:08:06 I love cooking, I love food, I love coffee,

00:08:08 and I’ve spent some time with some chefs

00:08:10 who like know their stuff.

00:08:12 And they always just take out a piece of paper

00:08:15 and just work backwards in rough order.

00:08:17 Like it’s never perfect, but rough order.

00:08:20 It’s just like, oh, that makes sense.

00:08:21 Why not just work backwards from the end goal, right?

00:08:24 And put in some buffer time.

00:08:26 And so I probably over specified it a bit using a Gantt chart,

00:08:29 but the fact that you can do it,

00:08:32 it’s what professional kitchens roughly do.

00:08:35 They just don’t call it a Gantt chart,

00:08:36 or at least I don’t think they do.

00:08:38 Anyway, I was telling a story about Thanksgiving.

00:08:40 So here’s the thing.

00:08:42 I’m sitting down, we have the meal,

00:08:44 and then I got to know Ray Dalio fairly well

00:08:49 over maybe the last year of Instagram.

00:08:53 And one thing that he kept saying was like,

00:08:55 feedback is really hard to get honestly from people.

00:08:59 And I sat down after dinner, I said,

00:09:02 guys, I want feedback.

00:09:03 What was good and what was bad?

00:09:05 Yes.

00:09:06 And what’s funny is like,

00:09:07 literally everyone just said everything was great.

00:09:11 And I like personally knew I had screwed up

00:09:13 a handful of things, but no one would say it.

00:09:17 And can you imagine now not something as high stakes

00:09:20 as Thanksgiving dinner, okay?

00:09:22 Thanksgiving dinner, it’s not that high stakes.

00:09:25 But you’re trying to build a product

00:09:26 and everyone knows you left your job for it

00:09:28 and you’re trying to build it out

00:09:29 and you’re trying to make something wonderful

00:09:32 and it’s yours, right?

00:09:33 You designed it.

00:09:35 Now try asking for feedback

00:09:37 and know that you’re giving this to your friends

00:09:39 and your family.

00:09:42 People have trouble giving hard feedback.

00:09:45 People have trouble saying, I don’t like this

00:09:48 or this isn’t great, or this is how it’s failed me.

00:09:51 In fact, you usually have two classes of people.

00:09:56 People who just won’t say bad things,

00:09:58 you can literally say to them,

00:10:00 please tell me what you hate most about this

00:10:02 and they won’t do it.

00:10:03 They’ll try, but they won’t.

00:10:05 And then the other class of people

00:10:06 are just negative period about everything

00:10:09 and it’s hard to parse out like what is true and what isn’t.

00:10:14 So my rule of thumb with this is

00:10:17 you should always ask people,

00:10:20 but at the end of the day, it’s amazing what data

00:10:22 will tell you.

00:10:23 And that’s why with whatever project I work on, even now,

00:10:27 collecting data from the beginning on usage patterns,

00:10:30 so engagement, how many days of the week do they use it?

00:10:34 How many, I don’t know if we were to go back to Instagram,

00:10:36 how many impressions per day, right?

00:10:39 Is that growing?

00:10:40 Is that shrinking?

00:10:41 And don’t be like overly scientific about it, right?

00:10:44 Cause maybe you have 50 beta users or something.

00:10:48 But what’s fascinating is that data doesn’t lie.

00:10:52 People are very defensive about their time.

00:10:57 They’ll say, oh, I’m so busy,

00:10:59 I’m sorry I didn’t get to use the app.

00:11:00 Like I’m just, you know,

00:11:03 but I don’t know you were posting on Instagram

00:11:05 the whole time.

00:11:07 So I don’t know at the end of the day,

00:11:09 like at Facebook there was, you know,

00:11:11 before time spent became kind of this loaded term there.

00:11:15 The idea that people’s currency in their lives is time.

00:11:21 And they only have a certain amount of time to give things,

00:11:23 whether it’s friends or family or apps or TV shows

00:11:26 or whatever, there’s no way of inventing more of it,

00:11:28 at least not that I know of.

00:11:32 If they don’t use it, it’s because it’s not great.

00:11:35 So the moral of the story is you can ask all you want,

00:11:39 but you just have to look at the data.

00:11:41 And data doesn’t lie, right?

00:11:45 I mean, there’s metrics, there’s data can obscure

00:11:51 the key insight if you’re not careful.

00:11:54 So time spent in the app, that’s one.

00:11:57 There’s so many metrics you can put at this

00:11:59 and they will give you totally different insights,

00:12:02 especially when you’re trying to create something

00:12:04 that doesn’t obviously exist yet.

00:12:07 So, you know, measuring maybe why you left the app

00:12:12 or measuring special moments of happiness

00:12:17 that will make sure you return to the app

00:12:20 or moments of happiness that are long lasting

00:12:22 versus like dopamine short term, all of those things.

00:12:26 But I think I suppose in the beginning,

00:12:29 you can just get away with just asking the question,

00:12:33 which features are used a lot?

00:12:35 Let’s do more of that.

00:12:37 And how hard was the decision?

00:12:40 And I mean, maybe you can tell me what Instagram

00:12:43 looked in the beginning,

00:12:44 but how hard was it to make pictures

00:12:47 of the first class citizen?

00:12:48 That’s a revolutionary idea.

00:12:50 Like at whatever point Instagram became this feed of photos,

00:12:56 that’s quite brilliant.

00:12:58 Plus, I also don’t know when this happened,

00:13:02 but they’re all shaped the same.

00:13:05 It’s like a…

00:13:06 I have to tell you why, that’s the interesting part.

00:13:09 Why is that?

00:13:10 So a couple of things.

00:13:11 One is data, like you’re right.

00:13:16 You can overinterpret data.

00:13:18 Like imagine trying to fly a plane by staring at,

00:13:22 I don’t know, a single metric like airspeed.

00:13:25 You don’t know if you’re going up or down.

00:13:27 I mean, it correlates with up or down,

00:13:28 but you don’t actually know.

00:13:29 It will never help you land the plane.

00:13:32 So don’t stare at one metric.

00:13:33 Like it turns out you have to synthesize a bunch of metrics

00:13:36 to know where to go.

00:13:37 But it doesn’t lie.

00:13:40 Like if your airspeed is zero,

00:13:41 unless it’s not working, right?

00:13:43 If it’s zero, you’re probably gonna fall out of the sky.

00:13:46 So generally you look around and you have the scan going.

00:13:50 Yes.

00:13:51 And you’re just asking yourself,

00:13:52 is this working or is this not working?

00:13:56 But people have trouble explaining how they actually feel.

00:14:02 So just, it’s about synthesizing both of them.

00:14:05 So then Instagram, right?

00:14:07 We were talking about revolutionary moment

00:14:10 where the feed became square photos basically.

00:14:14 And photos first and then square photos.

00:14:16 Yeah.

00:14:19 It was clear to me that the biggest,

00:14:21 so I believe the biggest companies are founded

00:14:26 when enormous technical shifts happen.

00:14:30 And the biggest technical shift that happened

00:14:32 right before Instagram was founded

00:14:34 was the advent of a phone that didn’t suck.

00:14:38 The iPhone, right?

00:14:38 Like in retrospect, we’re like, oh my God,

00:14:40 the first iPhone that almost had,

00:14:43 like it wasn’t that good.

00:14:44 But compared to everything else at the time,

00:14:46 it was amazing.

00:14:48 And by the way,

00:14:50 the first phone that had an incredible camera

00:14:54 that could like do as well as the point and shoot

00:14:57 you might carry around was the iPhone 4.

00:15:01 And that was right when Instagram launched.

00:15:02 And we looked around and we said,

00:15:04 what will change?

00:15:05 Because everyone has a camera in their pocket.

00:15:08 And it was so clear to me that the world

00:15:11 of social networks before it was based in the desktop

00:15:16 and sitting there and having a link you could share, right?

00:15:20 And that wasn’t gonna be the case.

00:15:21 So the question is what would you share

00:15:23 if you were out and about in the world?

00:15:26 If not only did you have a camera that fit in your pocket,

00:15:29 but by the way, that camera had a network attached to it

00:15:31 that allowed you to share instantly.

00:15:34 That seemed revolutionary.

00:15:36 And a bunch of people saw it at the same time.

00:15:37 It wasn’t just Instagram.

00:15:38 There were a bunch of competitors.

00:15:41 The thing we did, I think was not only,

00:15:44 well, we focused on two things.

00:15:45 So we wrote down those things, we circled photos

00:15:47 and we said, I think we should invest in this.

00:15:50 But then we said, what sucks about photos?

00:15:52 One, they look like crap, right?

00:15:55 They just, at least back then.

00:15:57 Now my phone takes pretty great photos, right?

00:16:01 Back then they were blurry, not so great, compressed, right?

00:16:06 Two, it was really slow, like really slow to upload a photo.

00:16:12 And I’ll tell a fun story about that

00:16:13 and explain to you why they’re all the same size

00:16:15 and square as well.

00:16:18 And three, man, if you wanted to share a photo

00:16:21 on different networks, you had to go to each

00:16:24 of the individual apps and select all of them

00:16:26 and upload individually.

00:16:27 And so we were like, all right, those are the pain points.

00:16:30 We’re gonna focus on that.

00:16:31 So one, instead of, because they weren’t beautiful,

00:16:36 we were like, why don’t we lean into the fact

00:16:37 that they’re not beautiful?

00:16:38 And I remember studying in Florence,

00:16:40 my photography teacher gave me this Holga camera

00:16:42 and I’m not sure everyone knows what a Holga camera is,

00:16:44 but they’re these old school plastic cameras.

00:16:48 I think they’re produced in China at the time.

00:16:52 And I wanna say the original ones were like

00:16:54 from the 70s or the 80s or something.

00:16:55 They’re supposed to be like $3 cameras for the every person.

00:16:58 They took nice medium format films, large negatives,

00:17:05 but they kind of blurred the light

00:17:07 and they kind of like light leaked into the side.

00:17:10 And there was this whole resurgence where people looked

00:17:13 at that and said, oh my God, this is a style, right?

00:17:16 And I remember using that in Florence and just saying,

00:17:19 well, why don’t we just like lean into the fact

00:17:20 that these photos suck and make them suck more,

00:17:24 but in an artistic way.

00:17:26 And it turns out that had product market fit.

00:17:28 People really liked that.

00:17:29 They were willing to share their not so great photos

00:17:31 if they looked not so great on purpose, okay?

00:17:35 The second part.

00:17:37 That’s where the filters come into picture.

00:17:40 So computational modification of photos

00:17:42 to make them look extra crappy to where it becomes art.

00:17:46 Yeah, yeah.

00:17:47 And I mean, add light leaks, add like an overlay filter,

00:17:51 make them more contrasty than they should be.

00:17:54 The first filter we ever produced was called X Pro 2.

00:17:58 And I designed it while I was

00:17:59 in this small little bed and breakfast room

00:18:02 in Todos Santos, Mexico.

00:18:03 I was trying to take a break from the bourbon days.

00:18:06 And I remember saying to my co founder,

00:18:08 I just need like a week to reset.

00:18:11 And that was on that trip worked on the first filter

00:18:15 because I said, you know, I think I can do this.

00:18:17 And I literally iterated one by one over the RGB values

00:18:22 in the array that was the photo and just slightly shifted.

00:18:26 Basically there was a function of R, function of G,

00:18:29 function of B that just shifted them slightly.

00:18:32 It was in rocket science.

00:18:34 And it turns out that actually

00:18:36 made your photo look pretty cool.

00:18:38 It just mapped from one color space to another color space.

00:18:42 It was simple, but it was really slow.

00:18:44 I mean, if you applied a filter,

00:18:47 I think it used to take two or three seconds to render.

00:18:50 Only eventually would I figure out how to do it on the GPU.

00:18:53 And I’m not even sure it was a GPU, but it was using OpenGL.

00:18:56 But anyway, I would eventually figure that out

00:18:59 and then it would be instant, but it used to be really slow.

00:19:02 By the way, anyone who’s watching or listening,

00:19:06 it’s amazing what you can get away with in a startup

00:19:10 as long as the product outcome is right for the user.

00:19:14 Like you can be slow.

00:19:15 You can be terrible.

00:19:16 You can be, as long as you have product market fit,

00:19:21 people will put up with a lot.

00:19:22 And then the question is just about compressing,

00:19:25 making it more performant over time

00:19:27 so that they get that product market fit instantly.

00:19:30 So fascinating because there’s some things

00:19:32 where those three seconds would make or break the app,

00:19:37 but some things you’re saying not.

00:19:39 It’s hard to know when, it’s the problem Spotify solved

00:19:43 for making streaming like work.

00:19:48 And like delays in listening to music is a huge negative,

00:19:53 even like slight delays.

00:19:55 But here you’re saying, I mean,

00:19:57 how do you know when those three seconds are okay

00:20:00 or are you just gonna have to try it out?

00:20:03 Because to me, my intuition would be

00:20:07 those three seconds would kill the app.

00:20:09 Like I would try to do the OpenGL thing.

00:20:12 Right, so I wish I were that smart at the time.

00:20:17 I wasn’t, I just knew how to do what I knew how to do.

00:20:21 And I decided, okay, like,

00:20:23 why don’t I just iterate over the values and change them?

00:20:27 And what’s interesting is that

00:20:31 compared to the alternatives, no one else used OpenGL.

00:20:35 So everyone else was doing it the dumb way.

00:20:37 And in fact, they were doing it at a high resolution.

00:20:40 Now comes in the small resolution

00:20:42 that we’ll talk about in a second.

00:20:45 By choosing 512 pixels by 512 pixels,

00:20:48 which I believe it was at the time,

00:20:51 we iterated over a lot fewer pixels than our competitors

00:20:54 who were trying to do these enormous output like images.

00:20:58 So instead of taking 20 seconds,

00:21:00 I mean, three seconds feels pretty good, right?

00:21:03 So on a relative basis, we were winning like a lot.

00:21:06 Okay, so that’s answer number one.

00:21:08 Answer number two is we actually focused

00:21:11 on latency in the right places.

00:21:13 So we did this really wonderful thing when you uploaded.

00:21:17 So the way it would work is you’d take your phone,

00:21:20 you’d take the photo and then you’d go to the,

00:21:25 you’d go to the edit screen where you would caption it.

00:21:28 And on that caption screen, you’d start typing

00:21:31 and you’d think, okay, like what’s a clever caption?

00:21:34 And I said to Mike, hey, when I worked on the Gmail team,

00:21:36 you know what they did?

00:21:37 When you typed in your username or your email address,

00:21:40 even before you’ve entered in your password,

00:21:43 like the probability once you enter in your username

00:21:47 that you’re going to actually sign in is extremely high.

00:21:51 So why not just start loading your account

00:21:52 in the background?

00:21:53 Not like sending it down to the desktop,

00:21:55 that would be a security issue,

00:21:59 but like loaded into memory on the server,

00:22:01 like get it ready, prepare it.

00:22:03 I always thought that was so fascinating and unintuitive.

00:22:06 And I was like, Mike, why don’t we just do that?

00:22:08 But like, we’ll just upload the photo

00:22:10 and like assume you’re going to upload the photo.

00:22:14 And if you don’t, forget about it, we’ll delete it, right?

00:22:17 So what ended up happening was people would caption

00:22:20 their photo, they’d press done or upload

00:22:24 and you’d see this little progress bar just go foop.

00:22:27 It was lightning fast, okay?

00:22:30 We were no faster than anyone else at the time,

00:22:32 but by choosing 512 by 512 and doing it in the background,

00:22:37 it almost guaranteed that it was done

00:22:39 by the time you captioned.

00:22:41 And everyone when they used it was like,

00:22:43 how the hell is this thing so fast?

00:22:47 But we were slow, we just hid the slowness.

00:22:49 It wasn’t like, these things are just like,

00:22:51 it’s a shell game, you’re just hiding the latency.

00:22:55 That mattered to people like a lot.

00:22:58 And I think that, so you were willing to put up

00:23:01 with a slow filter if it meant

00:23:03 you could share it immediately.

00:23:04 And of course we added sharing options

00:23:06 which let you distribute it really quickly,

00:23:08 that was the third part.

00:23:11 So latency matters, but relative to what?

00:23:14 And then there’s some like tricks,

00:23:17 you get around to just hiding the latency.

00:23:20 Like I don’t know if Spotify starts downloading

00:23:23 the next song eagerly, I’m assuming they do.

00:23:25 There are a bunch of ideas here that are not rocket science

00:23:29 that really help.

00:23:31 And all of that was stuff you were explicitly

00:23:35 having a discussion about, like those designs

00:23:37 and you were having like arguments, discussions.

00:23:41 I’m not sure it was arguments, I mean,

00:23:43 I’m not sure if you’ve met my co founder Mike,

00:23:45 but he’s a pretty nice guy and he’s very reasonable.

00:23:48 And we both just saw eye to eye and we’re like,

00:23:51 yeah, it’s like, make this fast or at least seem fast,

00:23:56 it’ll be great.

00:23:57 Honestly, I think the most contentious thing

00:23:59 and he would say this too initially,

00:24:02 was I was on an iPhone 3G, so like the not so fast one.

00:24:08 And he had a brand new iPhone 4, that was cheap.

00:24:10 Nice.

00:24:12 And his feed loaded super smoothly,

00:24:15 like when he would scroll from photo to photo,

00:24:18 buttery smooth, right?

00:24:20 But on my phone, every time you got to a new photo,

00:24:22 it was like, kachunk, kachunk, allocate memory,

00:24:25 like all this stuff, right?

00:24:27 I was like, Mike, that’s unacceptable.

00:24:29 He’s like, oh, come on, man, just like upgrade your phone.

00:24:31 Basically, he didn’t actually say that,

00:24:33 he was nicer than that.

00:24:35 But I could tell he wished like,

00:24:36 I would just stop being cheap and just get a new phone.

00:24:39 But what’s funny is we actually sat there working

00:24:41 on that little detail for a few days before launch.

00:24:45 And that polished experience,

00:24:48 plus the fact that uploading seemed fast

00:24:51 for all these people who didn’t have nice phones,

00:24:54 I think meant a lot because far too often,

00:24:57 you see teams focus not on performance,

00:25:00 they focus on what’s the cool computer science problem

00:25:03 they can solve, right?

00:25:05 Can we scale this thing to a billion users

00:25:08 and they’ve got like a hundred, right?

00:25:11 Yeah.

00:25:12 You talked about loss function,

00:25:14 so I want to come back to that.

00:25:16 Like the loss function is like,

00:25:17 do you provide a great, happy, magical,

00:25:20 whatever experience for the consumer?

00:25:23 And listen, if it happens to involve something complex

00:25:25 and technical, then great.

00:25:27 But it turns out, I think most of the time,

00:25:32 those experiences are just sitting there waiting to be built

00:25:34 with like not that complex solutions.

00:25:38 But everyone is just like so stuck in their own head

00:25:40 that they have to overengineer everything

00:25:42 and then they forget about the easy stuff.

00:25:45 I mean, also, maybe to flip the loss function there is,

00:25:48 you’re trying to minimize the number of times

00:25:50 there’s unpleasant experience, right?

00:25:54 Like the one you mentioned where when you go

00:25:56 to the next photo, it freezes for a little bit.

00:25:58 So it’s almost, as opposed to maximizing pleasure,

00:26:00 it’s probably easier to minimize the number of like,

00:26:04 the friction.

00:26:05 Yeah.

00:26:06 And as we all know, you just make the pleasure negative

00:26:10 and then minimize everything, so.

00:26:13 We’re mapping this all back to neural networks.

00:26:14 But actually, can I say one thing on that,

00:26:16 which is I don’t know a lot about machine learning,

00:26:19 but I feel like I’ve tried studying a bunch.

00:26:23 That whole idea of reinforcement learning

00:26:26 and planning out more than the greedy single experience,

00:26:30 I think is the closest you can get

00:26:34 to like ideal product design thinking,

00:26:38 where you’re not saying,

00:26:39 hey, like, can we have a great experience just this one time?

00:26:43 But like, what is the right way to onboard someone?

00:26:46 What series of experiences correlate most with them

00:26:50 hanging on long term, right?

00:26:52 So not just saying, oh, did the photo load slowly

00:26:55 a couple of times, or did they get a great photo

00:26:57 at the top of their feed?

00:27:00 But like, what are the things that are gonna make

00:27:01 this person come back over the next week,

00:27:04 over the next month?

00:27:06 And as a product designer asking yourself,

00:27:08 okay, I wanna optimize, not just minimize bad experiences

00:27:11 in the short run, but like,

00:27:13 how do I get someone to engage over the next month?

00:27:17 And I’m not gonna claim at all that I thought that way

00:27:20 at all at the time, because I certainly didn’t.

00:27:23 But if I were going back and giving myself any advice,

00:27:25 it would be thinking, what are those second order effects

00:27:28 that you can create?

00:27:30 And it turns out having your friends on the service

00:27:33 is an enormous win.

00:27:34 So starting with a very small group of people

00:27:37 that produce content that you wanted to see, which we did,

00:27:40 we seeded the community very well, I think.

00:27:43 Ended up mattering, and so.

00:27:46 Yeah, you said that community is one

00:27:48 of the most important things.

00:27:49 So it’s from a metrics perspective,

00:27:52 from maybe a philosophy perspective,

00:27:55 building a certain kind of community within the app.

00:27:57 See, I wasn’t sure what exactly you meant by that

00:28:00 when I first heard you say that.

00:28:01 Maybe you can elaborate, but as I understand now,

00:28:04 it can literally mean get your friends onto the app.

00:28:09 Yeah, think of it this way.

00:28:12 You can build an amazing restaurant or bar or whatever,

00:28:16 right, but if you show up and you’re the only one there,

00:28:20 is it like, does it matter how good the food is?

00:28:24 The drinks, whatever?

00:28:25 No, these are inherently social experiences

00:28:28 that we were working on.

00:28:30 So the idea of having people there,

00:28:35 like you needed to have that,

00:28:36 otherwise it was just a filter out.

00:28:39 But by the way, part of the genius,

00:28:41 I’m gonna say genius, even though it wasn’t really genius,

00:28:43 was starting to be marauding as a filter app was awesome.

00:28:50 The fact that you could,

00:28:51 so we talk about single player mode a lot,

00:28:53 which is like, can you play the game alone?

00:28:56 And Instagram, you could totally play alone.

00:28:57 You could filter your photos,

00:28:59 and a lot of people would tell me,

00:29:00 I didn’t even realize that this thing was a social network

00:29:04 until my friend showed up.

00:29:06 It totally worked as a single player game.

00:29:09 And then when your friends showed up,

00:29:10 all of a sudden it was like,

00:29:11 oh, not only was this great alone,

00:29:14 but now I actually have this trove of photos

00:29:16 that people can look at and start liking,

00:29:19 and then I can like theirs.

00:29:20 And so it was this bootstrap method

00:29:23 of how do you make the thing not suck

00:29:25 when the restaurant is empty?

00:29:27 Yeah, but the thing is, when you say friends,

00:29:29 we’re not necessarily referring to friends

00:29:31 in the physical space.

00:29:32 So you’re not bringing your physical friends with you.

00:29:35 You’re also making new friends.

00:29:36 So you’re finding new community.

00:29:38 So it’s not immediately obvious to me

00:29:40 that it’s almost like building any kind of community.

00:29:45 It was both.

00:29:46 And what we learned very early on

00:29:48 was what made Instagram special

00:29:50 and the reason why you would sign up for it

00:29:51 versus say, just sit on Facebook

00:29:53 and look at your friends photos.

00:29:55 Of course we were live,

00:29:56 and of course it was interesting

00:29:58 to see what your friends were doing now.

00:30:00 But the fact that you could connect with people

00:30:02 who like took really beautiful photos in a certain style

00:30:05 all around the world, whether they were travelers,

00:30:07 it was the beginning of the influencer economy.

00:30:11 It was these people

00:30:12 who became professional Instagrammers way back when.

00:30:16 But they took these amazing photos

00:30:18 and some of them were photographers professionally.

00:30:24 And all of a sudden you had this moment in the day

00:30:25 when you could open up this app

00:30:27 and sure you could see what your friends were doing,

00:30:28 but also it was like, oh my God,

00:30:30 that’s a beautiful waterfall or oh my God,

00:30:33 I didn’t realize there was that corner of England

00:30:35 or like really cool stuff.

00:30:38 And the beauty about Instagram early on

00:30:40 was that it was international by default.

00:30:43 You didn’t have to speak English to use it, right?

00:30:46 You could just look at the photos, works great.

00:30:49 We did translate, we had some pretty bad translations,

00:30:52 but we did translate the app.

00:30:54 And even if our translations were pretty poor,

00:30:57 the idea that you could just connect with other people

00:31:00 through their images was pretty powerful.

00:31:02 So how much technical difficulties

00:31:05 there with the programming?

00:31:07 Like what programming language you were talking about?

00:31:09 What was?

00:31:09 Zero, like maybe it was hard for us,

00:31:12 but I mean, there was nothing.

00:31:16 The only thing that was complex about Instagram

00:31:18 at the beginning technically was making it scale.

00:31:23 We were just plain old Objective C for the client.

00:31:27 So it was iPhone only at first?

00:31:29 iPhone only, yep.

00:31:30 As an Android person, I’m deeply offended, but go ahead.

00:31:34 This was 2010.

00:31:35 Oh, sure, sure.

00:31:36 Sorry, sorry.

00:31:37 Android’s getting a lot better, right?

00:31:39 So.

00:31:40 I take it back, you’re right.

00:31:41 If I were to do something today,

00:31:43 I think it would be very different

00:31:44 in terms of launch strategy, right?

00:31:46 Android’s enormous too.

00:31:49 But anyway, back to that moment,

00:31:51 it was Objective C and then we were Python based,

00:31:56 which is just like, this is before Python was really cool.

00:32:00 Like now it’s cool

00:32:01 because it’s all these machine learning libraries

00:32:03 like support Python and right.

00:32:05 Now it’s super, now it’s like cool to be Python.

00:32:08 Back then it was like, oh, Google uses Python.

00:32:10 Like maybe you should use Python.

00:32:12 Facebook was PHP.

00:32:14 Like I had worked at a small startup

00:32:16 of some ex Googlers that used Python.

00:32:19 So we used it and we used a framework called Django,

00:32:22 still exists and people use for basically the backend.

00:32:27 And then you threw a couple interesting things in there.

00:32:30 I mean, we used Postgres, which was kind of fun.

00:32:32 It was a little bit like Hipster database

00:32:34 at the time, right?

00:32:34 Versus MySQL.

00:32:36 MySQL, like everyone used MySQL.

00:32:37 So like using Postgres was like an interesting decision.

00:32:40 Right?

00:32:42 But we used it because it had a bunch of geo features

00:32:45 built in because we thought we were gonna be

00:32:46 a check and app, remember?

00:32:47 It’s also super cool now.

00:32:49 So you were into Python before it was cool

00:32:51 and you were into Postgres before it was cool.

00:32:53 Yeah, we were basically like,

00:32:55 not only Hipster photo company,

00:32:57 Hipster tech company, right?

00:33:00 We also adopted Redis early and like loved it.

00:33:04 I mean, it solves so many problems for us

00:33:06 and turns out that’s still pretty cool.

00:33:09 But the programming was very easy.

00:33:11 It was like, sign up a user, have a feed.

00:33:13 There was nothing, no machine learning at all, zero.

00:33:17 Can you get some context?

00:33:18 How many users at each of these stages?

00:33:20 Are we talking about a hundred users, a thousand users?

00:33:24 So the stage I just described,

00:33:25 I mean that technical stack lasted

00:33:27 through probably 50 million users.

00:33:32 I mean, seriously, like you can get away with a lot

00:33:36 with a pretty basic stack.

00:33:38 Like I think a lot of startups try to overengineer

00:33:41 their solutions from the beginning to like really scale

00:33:43 and you can get away with a lot.

00:33:45 That being said, most of the first two years of Instagram

00:33:48 was literally just trying to make that stack scale.

00:33:51 And it wasn’t, it was not a Python problem.

00:33:54 It was like literally just like, where do we put the data?

00:33:58 Like it’s all coming in too fast.

00:33:59 Like how do we store it?

00:34:01 How do we make sure to be up?

00:34:02 How do we like, how do we make sure we’re

00:34:05 on the right size boxes that they have enough memory?

00:34:09 Those were the issues, but.

00:34:11 Can you speak to the choices you make at that stage

00:34:14 when you’re growing so quickly?

00:34:16 Do you use something like somebody else’s

00:34:18 computer infrastructure or do you build in house?

00:34:22 I’m only laughing because we, when we launched

00:34:25 we had a single computer that we had rented

00:34:30 in some color space in LA.

00:34:32 I don’t even remember what it was called.

00:34:34 Cause I thought that’s what you did.

00:34:35 When I worked at a company called Odio that became Twitter.

00:34:38 I remember visiting our space in San Francisco.

00:34:41 You walked in, you had to wear the ear things.

00:34:43 It was cold and fans everywhere, right?

00:34:46 And we had to, you know, plug one out, replace one.

00:34:49 And I was the intern, so I just like held things.

00:34:52 But I thought to myself, oh, this is how it goes.

00:34:54 And then I remember being in a VC’s office.

00:34:58 I think it was a benchmarks office.

00:35:00 And I think we ran into another entrepreneur

00:35:02 and they were like, oh, how are things going?

00:35:03 We’re like, ah, you know, try to scale this thing.

00:35:06 And they were like, well, I mean

00:35:08 can’t you just add more instances?

00:35:09 And I was like, what do you mean?

00:35:11 And they’re like instances on Amazon.

00:35:13 I was like, what are those?

00:35:15 And it was this moment where we realized how deep

00:35:18 in it we were because we had no idea that AWS existed

00:35:22 nor should we be using it.

00:35:24 Anyway, that night we went back to the office

00:35:27 and we got on AWS, but we did this really dumb thing.

00:35:29 We’re so sorry to people listening.

00:35:32 But we brought up an instance, which was our database.

00:35:38 It’s gonna be a replacement for our database.

00:35:40 But we had it talking over the public internet

00:35:43 to our little box in LA that was our app server.

00:35:46 Very nice. Yeah.

00:35:48 That’s how sophisticated we were.

00:35:49 And obviously that was very, very slow.

00:35:52 Didn’t work at all.

00:35:53 I mean, it worked, but didn’t work.

00:35:55 Only like later that night did we realize

00:35:58 we had to have it all together.

00:36:00 But at least like if you’re listening right now

00:36:02 and you’re thinking, you know, I have no chance.

00:36:04 I’m gonna start to start, but I have no chance.

00:36:06 I don’t know.

00:36:07 We did it and we made a bunch

00:36:08 of really dumb mistakes initially.

00:36:10 I think the question is how quickly do you learn

00:36:12 that you’re making a mistake?

00:36:13 And do you do the right thing immediately right after?

00:36:16 So you didn’t pay for those mistakes by failure.

00:36:20 So yeah, how quickly did you fix it?

00:36:23 I guess there’s a lot of ways to sneak up to this question

00:36:26 of how the hell do you scale the thing?

00:36:28 Other startups, if you have an idea,

00:36:30 how do you scale the thing?

00:36:31 Is it just AWS and you try to write the kind of code

00:36:37 that’s easy to spread across a large number of instances,

00:36:41 and then the rest is just put money into it?

00:36:45 Basically, I would say a couple of things.

00:36:48 First off, don’t even ask the question,

00:36:51 just find product market fit, duct tape it together, right?

00:36:55 Like if you have to.

00:36:56 I think there’s a big caveat here, which I want to get to,

00:37:00 but generally all that matters is product market fit.

00:37:03 That’s all that matters.

00:37:04 If people like your product,

00:37:06 do not worry about when 50,000 people use your product

00:37:10 because you will be happy that you have that problem

00:37:12 when you get there.

00:37:13 I actually can’t name many startups

00:37:18 where they go from nothing to something overnight

00:37:22 and they can’t figure out how to scale it.

00:37:24 There are some, but I think nowadays,

00:37:27 it’s a, when I say a solved problem,

00:37:29 like there are ways of solving it.

00:37:33 The base case is typically that startups

00:37:35 worry way too much about scaling way too early

00:37:38 and forget that they actually have to make something

00:37:39 that people like.

00:37:40 That’s the default mistake case.

00:37:43 But what I’ll say is once you start scaling,

00:37:48 I mean, hiring quickly,

00:37:49 people who have seen the game before

00:37:51 and just know how to do it,

00:37:52 it becomes a bit of like, yeah,

00:37:56 just throw instances of the problem, right?

00:37:59 But the last thing I’ll say on this

00:38:00 that I think did save us,

00:38:03 we were pretty rigorous about writing tests

00:38:06 from the beginning.

00:38:08 That helped us move very, very quickly

00:38:11 when we wanted to rewrite parts of the product

00:38:14 and know that we weren’t breaking something else.

00:38:17 Tests are one of those things where it’s like,

00:38:18 you go slow to go fast.

00:38:20 And they suck when you have to write them

00:38:22 because you have to figure it out.

00:38:24 And there are always those ones that break

00:38:27 when you don’t want them to break and they’re annoying

00:38:29 and it feels like you spent all this time.

00:38:30 But looking back, I think that like longterm optimal,

00:38:34 even with a team of four,

00:38:36 it allowed us to move very, very quickly

00:38:38 because anyone could touch any part of the product

00:38:41 and know that they weren’t gonna bring down the site,

00:38:44 or at least in general.

00:38:45 At which point do you know product market fit?

00:38:48 How many users would you say?

00:38:50 Is it all it takes is like 10 people?

00:38:52 Or is it a thousand?

00:38:53 Is it 50,000?

00:38:55 I don’t think it is generally

00:38:58 a question of absolute numbers.

00:38:59 I think it’s a question of cohorts

00:39:01 and I think it’s a question of trends.

00:39:03 So, you know, it depends how big your business is trying

00:39:08 to be, right?

00:39:09 But if I were signing up a thousand people a week

00:39:12 and they all retain,

00:39:14 like the retention curves for those cohorts looked good,

00:39:16 healthy, and even like,

00:39:19 as you started getting more people on the service,

00:39:21 maybe those earlier cohorts started curving up again

00:39:24 because now there are network effects

00:39:26 and their friends are on the service

00:39:27 or totally depends what type of business you’re in,

00:39:29 but I’m talking purely social, right?

00:39:34 I don’t think it’s an absolute number.

00:39:36 I think it is,

00:39:37 I guess you could call it a marginal number.

00:39:39 So I spent a lot of time when I work with startups

00:39:42 asking them like, okay,

00:39:44 have you looked at that cohort versus this cohort,

00:39:47 whether it’s your clients

00:39:48 or whether it’s people signing up for the service?

00:39:52 But a lot of people think you just have to hit some mark,

00:39:55 like 10,000 people or 50,000 people,

00:39:57 but really seven ish billion people in the world.

00:40:01 Most people forever will not know about your product.

00:40:05 There are always more people out there to sign up.

00:40:07 It’s just a question of how you turn on the spigot, so.

00:40:11 At that stage, early stage yourself,

00:40:14 but also by way of advice,

00:40:16 should you worry about money at all?

00:40:18 How this thing’s gonna make money?

00:40:20 Or do you just try to find product market fit

00:40:24 and get a lot of users to enjoy using your thing?

00:40:28 I think it totally depends.

00:40:30 And that’s an unsatisfying answer.

00:40:32 I was talking with a friend today who,

00:40:36 he was one of our earlier investors and he was saying,

00:40:39 hey, like, have you been doing any angel investing lately?

00:40:41 I said, not really.

00:40:42 I’m just like focused on what I wanna do next.

00:40:44 And he said, the number of financings have just gone bonkers.

00:40:49 Like people are throwing money everywhere right now.

00:40:56 And I think the question is,

00:41:00 do you have an inkling of how you’re gonna make money?

00:41:04 Or are you really just like waving your hands?

00:41:07 I would not like to be an entrepreneur in the position of,

00:41:11 well, I have no idea how this will eventually make money.

00:41:14 That’s not fun.

00:41:16 If you are in an area,

00:41:18 like let’s say you wanted to start a social network, right?

00:41:22 Not saying this is a good idea, but if you did,

00:41:25 there are only a handful of ways they’ve made money

00:41:27 and really only one way they’ve made money in the past

00:41:29 and that’s ads.

00:41:31 So, if you have a service that’s amenable to that

00:41:37 and then I wouldn’t worry too much about that

00:41:39 because if you get to the scale,

00:41:40 you can hire some smart people and figure that out.

00:41:44 I do think that is really healthy for a lot of startups

00:41:48 these days, especially the ones doing

00:41:50 like enterprise software, slacks of the world, et cetera,

00:41:54 to be worried about money from the beginning,

00:41:56 but mostly as a way of winning over clients

00:42:00 and having stickiness.

00:42:05 Of course you need to be worried about money,

00:42:06 but I’m gonna also say this again,

00:42:08 which is it’s like longterm profitability.

00:42:12 If you have a roadmap to that, then that’s great.

00:42:15 But if you’re just like, I don’t know, maybe never,

00:42:18 like we’re working on this metaverse thing,

00:42:19 I think maybe someday, I don’t know.

00:42:22 Like that seems harder to me.

00:42:24 So you have to be as big as Facebook

00:42:26 to like finance that bet, right?

00:42:29 Do you think it’s possible, you said,

00:42:31 you’re not saying it’s necessarily a good idea

00:42:33 to launch a social network.

00:42:35 Do you think it’s possible today,

00:42:38 maybe you can put yourself in those shoes,

00:42:41 to launch a social network that achieves the scale

00:42:45 of a Facebook or a Twitter or an Instagram,

00:42:49 and maybe even greater scale?

00:42:51 Absolutely.

00:42:53 How do you do it?

00:42:55 Asking for a friend.

00:42:56 Yeah, if I knew, I’d probably be doing it right now

00:42:59 and not sitting here.

00:43:00 So, I mean, there’s a lot of ways to ask this question.

00:43:03 One is create a totally new product market fit,

00:43:07 create a new market, create something like Instagram did,

00:43:10 which is like create something kind of new,

00:43:13 or literally out compete Facebook at its own thing,

00:43:17 or I’ll compete Twitter at its own thing.

00:43:19 The only way to compete now,

00:43:21 if you wanna build a large social network

00:43:23 is to look for the cracks, look for the openings.

00:43:28 No one competed, I mean,

00:43:31 no one competed with the core business of Google.

00:43:33 No one competed with the core business of Microsoft.

00:43:36 You don’t go at the big guys

00:43:39 doing exactly what they’re doing.

00:43:41 Instagram didn’t win, quote unquote,

00:43:43 because it tried to be a visual Twitter.

00:43:46 Like we spotted things that either Twitter

00:43:49 wasn’t going to do or refuse to do,

00:43:52 images and feed for the longest time, right?

00:43:55 Or that Facebook wasn’t doing or not paying attention to

00:43:58 because they were mostly desktop at the time

00:44:00 and we were purely mobile, purely visual.

00:44:05 Often there are opportunities sitting there.

00:44:07 You have to figure out like,

00:44:12 I think like there’s a strategy book,

00:44:13 I can’t remember the name,

00:44:14 but talk about moats and just like the best place to play

00:44:19 is where your competitor literally can’t pivot

00:44:22 because structurally they’re set up not to be there.

00:44:26 And that’s where you win.

00:44:28 And what’s fascinating is like,

00:44:30 do you know how many people were like,

00:44:31 images, Facebook does that, Twitter does that.

00:44:35 I mean, how wrong were they, really wrong?

00:44:37 And these are some of the smartest people

00:44:38 in Silicon Valley, right?

00:44:40 But now Instagram exists for a while.

00:44:42 How is it that Snapchat could then exist?

00:44:45 It makes no sense.

00:44:47 Like plenty of people would say,

00:44:48 well, there’s Facebook, no images.

00:44:50 Okay, okay, Instagram, I’ll give you that one.

00:44:52 But wait, now another image based social network

00:44:55 is gonna get really big.

00:44:57 And then TikTok comes along.

00:44:59 Like the prior, so you asked me, is it possible?

00:45:03 The only reason I’m answering yes

00:45:05 is because my prior is that it’s happened once every,

00:45:09 I don’t know, three, four or five years consistently.

00:45:13 And I can’t imagine there’s anything structurally

00:45:15 that would change that.

00:45:17 So that’s why I answer that way.

00:45:18 Not because I know how, I just,

00:45:21 when you see a pattern, you see a pattern

00:45:22 and there’s no reason to believe that’s gonna stop.

00:45:25 And it’s subtle too, because like you said,

00:45:27 Snapchat and TikTok,

00:45:29 they’re all doing the same space of things,

00:45:32 but there’s something fundamentally different

00:45:34 about like a three second video and a five second video

00:45:38 and a 15 second video and a one minute video

00:45:41 and a one hour video, like fundamentally different.

00:45:43 Fundamentally different.

00:45:45 I mean, I think one of the reasons Snapchat exists

00:45:48 is because Instagram was so focused on posting great,

00:45:51 beautiful manicured versions of yourself throughout time.

00:45:57 And there was this enormous demand of like,

00:45:58 hey, I really like this behavior.

00:46:00 I love using Instagram, but man,

00:46:03 I just like wish I could share something going on in my day.

00:46:07 Do I really have to put it on my profile?

00:46:10 Do I really have to make it last forever?

00:46:11 Do I really, and that opened up a door,

00:46:14 it created a market, right?

00:46:16 And then what’s fascinating is Instagram had an explore page

00:46:20 for the longest time and it was image driven, right?

00:46:23 But there’s absolutely a behavior where you open up Instagram

00:46:26 and you sit on the explore page all day.

00:46:28 That is effectively TikTok,

00:46:29 but obviously focused on videos.

00:46:32 And it’s not like you could just put the explore page

00:46:35 in TikTok form and it works.

00:46:37 It had to be video, it had to have music.

00:46:39 These are the hard parts about product development

00:46:42 that are very hard to predict,

00:46:44 but they’re all versions of the same thing

00:46:47 with varying, like if you line them up

00:46:50 in a bunch of dimensions, they’re just like kind of on,

00:46:55 they’re different values of the same dimensions,

00:46:56 which is like, I guess, easy to say in retrospect.

00:46:59 But like, if I were an entrepreneur going after that area,

00:47:02 I’d ask myself like, where’s the opening?

00:47:05 What needs to exist because TikTok exists now?

00:47:08 So I wonder how much things that don’t yet exist

00:47:13 and can exist is in the space of algorithms,

00:47:15 in the space of recommender systems.

00:47:18 So in the space of how the feed is generated.

00:47:21 So we kind of talk about the actual elements

00:47:24 of the content, that’s what we’ve been talking,

00:47:27 the difference between photos,

00:47:29 between short videos, longer videos.

00:47:32 I wonder how much disruption is possible

00:47:35 in the way the algorithms work.

00:47:37 Because a lot of the criticism towards social media

00:47:39 is in the way the algorithms work currently.

00:47:41 And it feels like, first of all,

00:47:44 talking about product market fit,

00:47:47 there’s certainly a hunger for social media algorithms

00:47:54 that do something different.

00:47:56 I don’t think anyone, everyone said complaining,

00:47:59 this is hurting me and this is hurting society,

00:48:04 but I keep doing it because I’m addicted to it.

00:48:07 And they say, we want something different,

00:48:09 but we don’t know what.

00:48:11 It feels like just different.

00:48:15 It feels like there’s a hunger for that,

00:48:17 but that’s in the space of algorithms.

00:48:19 I wonder if it’s possible to disrupt in that space.

00:48:22 Absolutely, I have this thesis that the worst part

00:48:27 about social networks is that they’re, is the people.

00:48:33 It’s a line that sounds funny, right?

00:48:36 Because like, that’s why you call it a social network.

00:48:39 But what does social networks actually do for you?

00:48:42 Like just think, like imagine you were an alien

00:48:45 and you landed and someone says,

00:48:47 hey, there’s this site, it’s a social network.

00:48:49 We’re not gonna tell you what it is,

00:48:50 but just what does it do?

00:48:51 And you have to explain it to them.

00:48:53 It does two things.

00:48:54 One is that people you know and have social ties with

00:49:00 distribute updates through whether it’s photos or videos

00:49:05 about their lives so that you don’t have to physically

00:49:07 be with them, but you can keep in touch with them.

00:49:09 That’s one, that’s like a big part of Instagram.

00:49:12 That’s a big part of Snap.

00:49:14 It is not part of TikTok at all.

00:49:16 So there’s another big part, which is there’s all this

00:49:19 content out in the world that’s entertaining,

00:49:22 whether you wanna watch it or you wanna read it.

00:49:26 And matchmaking between content that exists in the world

00:49:30 and people that want that content

00:49:33 turns out to be like a really big business, right?

00:49:35 Search and discovery, which you?

00:49:37 Search and discovery, but my point is it could be video,

00:49:39 it could be text, it could be websites, it could be,

00:49:42 I mean, think back to like dig, right?

00:49:46 Or stumble upon or, right?

00:49:49 Nice, yeah.

00:49:50 But like, what did those do?

00:49:51 Like they basically distributed interesting content

00:49:54 to you, right?

00:49:57 I think the most interesting part or the future

00:50:00 of social networks is going to be making them less social

00:50:03 because I think people are part of the root cause

00:50:06 of the problem.

00:50:07 So for instance, often in recommender systems,

00:50:10 we talk about two stages.

00:50:11 There’s a candidate generation step, which is just like

00:50:14 of our vast trove of stuff that you might wanna see,

00:50:18 what small subset should we pick for you, okay?

00:50:24 Typically that is grabbed from things

00:50:25 your friends have shared, right?

00:50:28 Then there’s a ranking step which says, okay,

00:50:30 now given these 100, 200 things depends on the network,

00:50:33 right?

00:50:34 Let’s like be really good about ranking them

00:50:36 and generally rank the things up higher

00:50:38 that get the most engagement, right?

00:50:40 So what’s the problem with that?

00:50:42 Step one is we’ve limited everything you could possibly see

00:50:46 to things that your friends have chosen to share

00:50:49 or maybe not friends, but influencers.

00:50:52 What things do people generally want to share?

00:50:54 They wanna share things that are gonna get likes,

00:50:56 that are gonna show up broadly.

00:50:58 So they tend to be more emotionally driven.

00:51:00 They tend to be more risque or whatever.

00:51:03 So why do we have this problem?

00:51:05 It’s because we show people things people have decided

00:51:08 to share and those things self select to being the things

00:51:11 that are most divisive.

00:51:14 So how do you fix that?

00:51:15 Well, what if you just imagine for a second

00:51:20 that why do you have to grab things

00:51:21 from things your friends have shared?

00:51:23 Why not just like grab things?

00:51:25 That’s really fascinating to me.

00:51:27 And that’s something I’ve been thinking a lot about.

00:51:29 And just like, why is it that when you log onto Twitter,

00:51:34 you’re just sitting there looking at things from accounts

00:51:38 that you’ve followed for whatever reason?

00:51:42 And TikTok I think has done a wonderful job here,

00:51:44 which is like, you can literally be anyone.

00:51:47 And if you produce something fascinating, it’ll go viral.

00:51:51 But like, you don’t have to be someone that anyone knows.

00:51:54 You don’t have to have built up a giant following.

00:51:57 You don’t have to have paid for followers.

00:52:00 You don’t have to try to maintain those followers.

00:52:01 You literally just have to produce something interesting.

00:52:04 That is I think the future of social networking.

00:52:07 That’s the direction things will head.

00:52:10 And I think what you’ll find is it’s far less

00:52:12 about people manipulating distribution

00:52:15 and far more about what is like, is this content good?

00:52:19 And good is obviously a vague definition

00:52:22 that we could spend hours on.

00:52:23 But different networks I think will decide

00:52:27 different value functions to decide what is good

00:52:29 and what isn’t good.

00:52:30 And I think that’s a fascinating direction.

00:52:32 So that’s almost like creating an internet.

00:52:33 I mean, that’s what Google did for web pages

00:52:36 that did page rank search.

00:52:39 So it’s discovery, you don’t follow anybody on Google

00:52:42 when you use a search engine.

00:52:44 You just discover web pages.

00:52:46 And so what TikTok does is saying,

00:52:49 let’s start from scratch.

00:52:51 Let’s like start a new internet

00:52:54 and have people discover stuff on that new internet

00:52:56 within a particular kind of pool of people.

00:52:59 But what’s so fascinating about this

00:53:01 is like the field of information retrieval.

00:53:05 Like I always talked about as I was studying this stuff,

00:53:08 it was used the word query and document.

00:53:10 So I was like, why are they saying query and documents?

00:53:12 Like they’re literally imagine,

00:53:14 like if you just stop thinking about query

00:53:17 as like literally a search query

00:53:18 and a query could be a person.

00:53:20 I mean, a lot of the way,

00:53:22 I’m not gonna claim to know how Instagram or Facebook

00:53:24 machine learning works today,

00:53:26 but if you want to find a match for a query,

00:53:30 the query is actually the attributes of the person,

00:53:33 their age, their gender, where they’re from,

00:53:36 maybe some kind of summarization of their interests.

00:53:39 And that’s a query, right?

00:53:41 And that matches against documents.

00:53:43 And by the way, documents don’t have to be texts.

00:53:45 They can be videos, however long.

00:53:48 I don’t know what the limit is on TikTok these days.

00:53:50 They keep changing it.

00:53:51 My point is just, you’ve got a query,

00:53:53 which is someone in search of something

00:53:55 that they want to match and you’ve got the document

00:53:58 and it doesn’t have to be text.

00:53:59 It could be anything.

00:54:00 And how do you match make?

00:54:02 And that’s one of these like,

00:54:03 I mean, I’ve spent a lot of time thinking about this

00:54:06 and I don’t claim to have mastered it at all,

00:54:08 but I think it’s so fascinating about where that will go

00:54:11 with new social networks.

00:54:13 See, what I’m also fascinated by is metrics

00:54:16 that are different than engagement.

00:54:18 So the other thing from an alien perspective,

00:54:20 what social networks are doing is they,

00:54:25 they in the short term,

00:54:26 bring out different aspects of each human being.

00:54:30 So first, let me say that an algorithm or a social network

00:54:38 for each individual can bring out the best of that person

00:54:41 or the worst of that person,

00:54:43 or there’s a bunch of different parts to us,

00:54:45 parts we’re proud of that we are,

00:54:48 parts we’re not so proud of.

00:54:50 When we look at the big picture of our lives,

00:54:53 when we look back 30 days from now,

00:54:55 am I proud that I said those things or not?

00:54:57 Am I proud that I felt those things?

00:54:59 Am I proud that I experienced or read those things

00:55:02 or thought about those things?

00:55:04 Just in that kind of self reflected kind of way.

00:55:08 And so coupled with that,

00:55:10 I wonder if it’s possible to have different metrics

00:55:12 that are not just about engagement,

00:55:14 but are about long term happiness,

00:55:18 growth of a human being,

00:55:20 where they look back and say,

00:55:22 I am a better human being

00:55:23 for having spent 100 hours on that app.

00:55:26 And that feels like it’s actually strongly correlated

00:55:30 with engagement in the long term.

00:55:33 In the short term, it may not be,

00:55:34 but in long term, it’s like the same kind of thing

00:55:37 where you really fall in love with the product.

00:55:40 You fall in love with an iPhone,

00:55:41 you fall in love with a car.

00:55:43 That’s what makes you fall in love

00:55:45 is like really being proud

00:55:49 and just in a self reflected way,

00:55:51 understanding that you’re a better human being

00:55:53 for having used the thing.

00:55:55 And that’s what great relationships are made from.

00:55:58 It’s not just like you’re hot

00:56:01 and we like being together or something like that.

00:56:04 It’s more like I’m a better human being

00:56:06 because I’m with you.

00:56:07 And that feels like a metric

00:56:09 that could be optimized for by the algorithms.

00:56:13 But anytime I kind of talk about this with anybody,

00:56:17 they seem to say, yeah, okay,

00:56:18 that’s going to get out competed immediately

00:56:21 by the engagement if it’s ad driven especially.

00:56:24 I just don’t think so.

00:56:26 I don’t, I mean, a lot of it’s just implementation.

00:56:30 I’ll say a couple of things.

00:56:31 One is to pull back the curtain on daily meetings

00:56:36 inside of these large social media companies.

00:56:40 A lot of what management,

00:56:42 or at least the people that are tweaking these algorithms

00:56:45 spend their time on are trade offs.

00:56:47 And there’s these things called value functions,

00:56:50 which are like, okay,

00:56:51 we can predict the probability that you’ll click

00:56:54 on this thing or the probability that you’ll share it,

00:56:58 or the probability that you will leave a comment on it

00:57:01 or the probability you’ll dwell on it.

00:57:04 Individual actions, right?

00:57:07 And you’ve got this neural network

00:57:09 that basically has a bunch of heads at the end

00:57:11 and all of them are between zero and one and great.

00:57:14 They all have values, right?

00:57:15 Or they all have probabilities.

00:57:19 And then in these meetings, what they will do is say,

00:57:21 well, how much do we value a comment versus a click

00:57:26 versus a share versus a,

00:57:29 and then maybe even some downstream thing, right?

00:57:31 That has nothing to do with the item there,

00:57:34 but like driving follows or something.

00:57:37 And what typically happens is they will say,

00:57:40 well, what are our goals for this quarter at the company?

00:57:42 Oh, we wanna drive sharing up, okay.

00:57:44 Well, let’s turn down these metrics

00:57:46 and turn up these metrics.

00:57:48 And they blend them right into a single scalar

00:57:52 which they’re trying to optimize.

00:57:55 That is really hard because invariably

00:57:58 you think you’re solving for, I don’t know,

00:57:59 something called meaningful interactions, right?

00:58:02 This was the big Facebook pivot.

00:58:04 And I don’t actually have any internal knowledge.

00:58:06 Like I wasn’t in those meetings,

00:58:09 but at least from what we’ve seen over the last month or so,

00:58:12 it seems by actually trying to optimize

00:58:16 for meaningful interactions,

00:58:18 it had all these side effects of optimizing

00:58:20 for these other things.

00:58:22 And I don’t claim to fully understand them,

00:58:24 but what I will say is that trade offs abound.

00:58:28 And as much as you’d like to solve for one thing,

00:58:31 if you have a network of over a billion people,

00:58:34 you’re gonna have unintended consequences either way.

00:58:36 And it gets really hard.

00:58:38 So what you’re describing is effectively a value model

00:58:41 that says like, can we capture,

00:58:43 this is the thing that I spent a lot of time thinking about,

00:58:45 like, can you capture utility

00:58:49 in a way that like actually measures someone’s happiness

00:58:53 that isn’t just a, what do they call it?

00:58:56 A surrogate problem where you say, well,

00:58:59 kind of think like the more you use the product,

00:59:01 the happier you are.

00:59:03 That was always the argument at Facebook, by the way.

00:59:04 It was like, well, people use it more,

00:59:07 so they must be more happy.

00:59:09 Turns out there are like a lot of things you use more

00:59:11 that make you less happy in the world.

00:59:12 Not talking about Facebook,

00:59:14 just let’s think about whether it’s gambling or whatever,

00:59:17 like that you can do more of,

00:59:19 but doesn’t necessarily make you happier.

00:59:20 So the idea that time equals happiness,

00:59:22 obviously you can’t map utility and time together easily.

00:59:27 There are a lot of edge cases.

00:59:28 So when you look around the world and you say,

00:59:30 well, what are all the ways we can model utility?

00:59:32 That is like one of the,

00:59:34 please, if you know someone smart doing this,

00:59:36 introduce me because I’m fascinated by it.

00:59:38 And it seems really tough.

00:59:40 But the idea that reinforcement learning,

00:59:42 like everyone interesting I know in machine learning,

00:59:46 like I was really interested in recommender systems

00:59:48 and supervised learning.

00:59:49 And the more I dug into it, I was like,

00:59:52 oh, literally everyone smart

00:59:54 is working on reinforcement learning.

00:59:56 Like literally everyone.

00:59:57 You just made people at OpenAI and DeepMind very happy, yes.

01:00:00 But I mean, but what’s interesting is like,

01:00:02 it’s one thing to train a game and like,

01:00:06 I mean that paper where they just took Atari

01:00:09 and they used a ConvNet to basically just like

01:00:12 train simple actions, mind blowing, right?

01:00:15 Absolutely mind blowing, but it’s a game, great.

01:00:18 So now what if you’re constructing a feed for a person,

01:00:23 right?

01:00:24 Like how can you construct that feed in such a way

01:00:29 that optimizes for a diversity of experience,

01:00:32 a longterm happiness, right?

01:00:36 But that reward function,

01:00:38 it turns out in reinforcement learning again,

01:00:41 as I’ve learned, like reward design is really hard.

01:00:45 And I don’t know, like how do you design a scalar reward

01:00:49 for someone’s happiness over time?

01:00:51 I mean, do you have to measure dopamine levels?

01:00:53 Like, do you have to?

01:00:54 Well, you have to have a lot more signals

01:00:58 from the human being.

01:00:59 Currently it feels like there’s not enough signals

01:01:01 coming from the human being users of this algorithm.

01:01:06 So for reinforcement learning to work well,

01:01:08 you need to have a lot more data.

01:01:10 Needs to have a lot of data.

01:01:11 And that actually is a challenge for anyone

01:01:13 who wants to start something,

01:01:14 which is you don’t have a lot of data.

01:01:16 So how do you compete?

01:01:17 But I do think back to your original point,

01:01:20 rethinking the algorithm, rethinking reward functions,

01:01:23 rethinking utility, that’s fascinating.

01:01:28 That’s cool.

01:01:28 And I think that’s an open opportunity

01:01:31 for a company that figures it out.

01:01:34 I have to ask about April, 2012,

01:01:37 when Instagram, along with its massive employee base

01:01:43 of 13 people was sold to Facebook for $1 billion.

01:01:48 What was the process like on a business level,

01:01:50 engineering level, human level?

01:01:52 What was that process of selling to Facebook like?

01:01:54 What did it feel like?

01:01:56 So I want to provide some context,

01:01:58 which is I worked in corporate development at Google,

01:02:00 which not a lot of people know,

01:02:02 but corporate development is effectively the group

01:02:04 that buys companies, right?

01:02:06 You sit there and you acquire companies.

01:02:08 And I had sat through so many of these meetings

01:02:10 with entrepreneurs.

01:02:12 We actually, fun fact, we never acquired a single company

01:02:14 when I worked in corporate development.

01:02:15 So I can’t claim that I had like a lot of experience,

01:02:21 but I had enough experience to understand,

01:02:23 okay, like what prices are people getting

01:02:25 and what’s the process?

01:02:27 And as we started to grow,

01:02:32 we were trying to keep this thing running

01:02:33 and we were exhausted and we were 13 people.

01:02:36 And I mean, we were trying to think back,

01:02:38 it was probably 27, 37 now,

01:02:43 so young on a relative basis, right?

01:02:48 And we’re trying to keep the thing running.

01:02:50 And then we go out to raise money

01:02:53 and we’re kind of like the hot startup at the time.

01:02:57 And I remember going into a specific VC and saying,

01:03:00 our terms we’re looking for are,

01:03:02 we’re looking for a $500 million valuation.

01:03:05 And I’ve never seen so many jaws drop all in unison, right?

01:03:10 And I was like, thanked and walked out the door

01:03:12 very kindly after.

01:03:14 And then I got a call the next day

01:03:16 from someone who was connected to them.

01:03:18 And they said, we just wanna let you know

01:03:21 that like it was pretty offensive

01:03:22 that you asked for a $500 million valuation.

01:03:25 And I can’t tell if that was like just negotiating or what,

01:03:30 but it’s true, like no one offered us more, right?

01:03:32 So we were…

01:03:33 So can you clarify the number again?

01:03:35 You said how many million?

01:03:37 500.

01:03:37 500 million.

01:03:38 500 million, yeah, half a billion.

01:03:42 So in my mind, I’m anchored like, okay,

01:03:44 well, literally no one’s biting at 500 million.

01:03:46 And eventually we would get Sequoia and Greylock

01:03:50 and others together at 500 million, basically, post.

01:03:54 It was 450 pre, I think we raised $50 million.

01:03:56 But just like no one was used to seeing

01:03:59 a $500 million companies then.

01:04:01 Like, I don’t know if it was because we were just coming

01:04:03 out of the hangover of 2008

01:04:06 and things were still on recovery mode.

01:04:10 But then along comes Facebook.

01:04:13 And after some negotiation, we’ve two X to the number

01:04:18 from a half a billion to a billion.

01:04:21 Yeah, it seems pretty good.

01:04:22 And I think Mark and I really saw eye to eye

01:04:27 that this thing could be big.

01:04:29 We thought we could…

01:04:30 Their resources would help us scale it.

01:04:32 And in a lot of ways it de risks.

01:04:34 I mean, it de risks a lot of the employees lives

01:04:37 for the rest of their lives,

01:04:38 including me, including Mike, right?

01:04:41 I think I might’ve had like 10 grand

01:04:42 in my bank account at the time, right?

01:04:44 Like we’re working hard, we had nothing.

01:04:48 So on a relative basis, it seems very high.

01:04:51 And then I think the last company to exit

01:04:53 for anywhere close to a billion was YouTube

01:04:55 that I could think of.

01:04:57 And thus began the giant long bull run of 2012

01:05:02 to all the way to where we are now,

01:05:04 where I saw some stat yesterday

01:05:06 about like how many unicorns exist and it’s absurd.

01:05:11 But then again, never underestimate technology

01:05:13 and like the value it can provide.

01:05:15 And man, costs have dropped and man scale has increased.

01:05:19 And you can make businesses make a lot of money now.

01:05:23 But on a fundamental level, I don’t know,

01:05:26 like how do you describe the decision

01:05:29 to sell a company with 13 people for a billion dollars?

01:05:32 So first of all, like how did it take a lot of guts

01:05:36 to sit at a table and say 500 million

01:05:38 or 1 billion with Mark Zuckerberg?

01:05:41 It seems like a very large number with 13.

01:05:44 Like, especially…

01:05:45 It doesn’t seem, it is.

01:05:47 It is.

01:05:48 They’re all large numbers.

01:05:49 Especially like you said before the unicorn parade.

01:05:55 I like that, I’m gonna use that.

01:05:56 The unicorn parade?

01:05:57 Yeah.

01:05:59 You were at the head of the unicorn parade.

01:06:01 It’s the, yeah, it’s a massive unicorn parade.

01:06:04 Okay, so no, I mean, we knew we were worth

01:06:10 quote unquote a lot, but we didn’t,

01:06:12 I mean, there was no market for Instagram.

01:06:14 I mean, it’s not, you couldn’t mark to market this thing

01:06:17 in the public markets.

01:06:18 You didn’t quite understand what it would be worth

01:06:20 or was worth at the time.

01:06:23 So in a market, an illiquid market

01:06:24 where you have one buyer and one seller

01:06:26 and you’re going back and forth,

01:06:27 and well, I guess there were like VC firms

01:06:31 who were willing to invest at a certain valuation.

01:06:34 So I don’t know, you just go with your gut.

01:06:38 And at the end of the day, I would say

01:06:41 the hardest part of it was not realizing,

01:06:50 like when we sold, it was tough

01:06:52 because like literally everywhere I go,

01:06:54 restaurants, whatever, like for a good six months after,

01:06:59 there was a lot of attention on the deal,

01:07:01 a lot of attention on the product,

01:07:03 a lot of attention, it was kind of miserable, right?

01:07:06 And you’re like, wait, like I made a lot of money,

01:07:08 but like, why is this not great?

01:07:10 And it’s because it turns out,

01:07:14 I don’t know, like I don’t really keep in touch with Mark,

01:07:16 but I’ve got to assume his job right now

01:07:17 is not exactly the most happy job in the world.

01:07:19 It’s really tough when you’re on top

01:07:21 and it’s really tough when you’re in the limelight.

01:07:24 So the decision itself was like, oh, cool, this is great.

01:07:27 How lucky are we, right?

01:07:29 So, okay, there’s a million question I want to ask.

01:07:30 Yeah, go, go, go.

01:07:32 First of all, why is it hard to be on top?

01:07:37 Why did you not feel good?

01:07:39 Like, can you dig into that?

01:07:40 It always, I’ve heard like Olympic athletes say

01:07:45 after they win gold, they get depressed.

01:07:49 Is it something like that where it feels like

01:07:53 it was kind of like a thing you were working towards?

01:07:56 Yeah, sure.

01:07:57 Some loose definition of success.

01:07:58 And this sure as heck feels like

01:08:01 at least according to other startups,

01:08:03 this is what success looks like.

01:08:04 And now why don’t I feel any better?

01:08:08 I’m still human, I still have all the same problems.

01:08:10 Is that the nature?

01:08:11 Or is it just like negative attention of some kind?

01:08:14 I think it’s all of the above.

01:08:16 But to be clear, there was a lot of happiness

01:08:18 in terms of like, oh my God, this is great.

01:08:20 Like we won the Super Bowl of startups, right?

01:08:25 Anyone who can get to a liquidity event

01:08:28 of anything meaningful feels like,

01:08:31 wow, this is what we started out to do.

01:08:32 Of course we want to create great things that people love,

01:08:35 but like we won in a big way.

01:08:38 But yeah, there’s this big like,

01:08:39 oh, if we won, what’s next?

01:08:43 So they call it the we have arrived syndrome,

01:08:49 which I need to go back and look where I can quote that from.

01:08:52 But I remember reading about it at the time.

01:08:54 I was like, oh yeah, that’s that.

01:08:56 And I remember we had a product manager leave very early on

01:08:59 when we got to Facebook.

01:09:00 And he said to me,

01:09:01 I just don’t believe I can learn anything

01:09:03 at this company anymore.

01:09:05 It’s like, it’s hit its apex.

01:09:08 We sold it great.

01:09:09 I just don’t have anything else to learn.

01:09:12 So from 2012 all the way to the day I left in 2018,

01:09:15 like the amount I learned and the humility

01:09:18 with which I realized, oh, we thought we won.

01:09:22 Billion dollars is cool,

01:09:23 but like there are a hundred billion dollar companies.

01:09:26 And by the way, on top of that, we had no revenue.

01:09:29 We had, I mean, we had a cool product,

01:09:31 but we didn’t scale it yet.

01:09:32 And there’s so much to learn.

01:09:34 And then competitors and how fun was it to fight Snapchat?

01:09:38 Oh my God.

01:09:39 Like it was, it’s like Yankees Red Sox.

01:09:42 It’s great.

01:09:42 Like that’s what you live for.

01:09:46 You know, you win some, you lose some,

01:09:47 but the amount you can learn through that process,

01:09:52 what I’ve realized in life is that there is no,

01:09:56 and there’s always someone who has more,

01:09:58 there’s always more challenge, just at different scales.

01:10:02 And it sounds like a little Buddhist,

01:10:04 but everything is super challenging,

01:10:09 whether you’re like a small business

01:10:11 or an enormous business.

01:10:13 I say like choose the game you like to play, right?

01:10:17 You’ve got to imagine that

01:10:18 if you’re an amazing basketball player,

01:10:19 you enjoy to some extent practicing basketball.

01:10:22 It’s gotta be something you love.

01:10:24 It’s gonna suck.

01:10:24 It’s gonna be hard.

01:10:26 You’re gonna have injuries, right?

01:10:27 But you gotta love it.

01:10:28 And the same thing with Instagram,

01:10:30 which is we might’ve sold, but it was like, great.

01:10:35 There’s one Super Bowl title.

01:10:37 Can we win five?

01:10:38 What else can we do?

01:10:40 Now I imagine you didn’t ask this, but okay, so I left.

01:10:43 There’s a little bit of like, what do you do next, right?

01:10:46 Like, how do you top that thing?

01:10:49 It’s the wrong question.

01:10:50 The question is like, when you wake up every day,

01:10:53 what is the hardest, most interesting thing

01:10:54 you can go work on?

01:10:56 Because like at the end of the day,

01:10:58 we all turn into dirt, it doesn’t matter, right?

01:11:00 But what does matter is like,

01:11:02 can we really enjoy this life?

01:11:05 Not in a hedonistic way, because that’s those,

01:11:08 it’s like the reinforcement learning,

01:11:09 learning like short term versus long term objectives.

01:11:13 Can you wake up every day and truly enjoy what you’re doing

01:11:19 knowing that it’s gonna be painful?

01:11:22 Knowing that like, no matter what you choose,

01:11:24 it’s gonna be painful.

01:11:25 Whether you sit on a beach

01:11:26 or whether you manage a thousand people or 10,000,

01:11:29 it’s gonna be painful.

01:11:31 So choose something that’s fun to have pain.

01:11:35 But yes, there was a lot of, we have arrived

01:11:38 and it’s a maturation process you just have to go through.

01:11:44 So no matter how much success there is,

01:11:47 how much money you make,

01:11:48 you have to wake up the next day and choose the hard life,

01:11:51 whatever that means next, that’s fun.

01:11:53 The fun slash hard life, hard life that’s fun.

01:11:56 I guess what I’m trying to say is slightly different,

01:12:00 which is just that no one realizes

01:12:02 everything’s gonna be hard.

01:12:05 Even chilling out is hard.

01:12:07 And then you just start worrying about stupid stuff.

01:12:09 Like, I don’t know, like did so and so

01:12:12 forget to paint the house today

01:12:14 or like did the gardener come or whatever?

01:12:16 Like, or, oh, I’m so angry

01:12:18 and my shipment of wine didn’t show up

01:12:20 and I’m sitting here on the beach without my wine.

01:12:22 I don’t know, I’m making shit up now, but like.

01:12:24 It turns out that even chilling, AKA meditation,

01:12:27 is hard work.

01:12:27 Yeah, and at least meditation is like productive chilling

01:12:31 where you’re like actually training yourself

01:12:32 to calm down and be, but backing up for a moment,

01:12:36 everything’s hard.

01:12:37 You might as well be like playing the game you love to play.

01:12:41 I just like playing and winning and I’m on the,

01:12:47 I’m still on the, I think the first half of life,

01:12:49 knock on wood, and I’ve got a lot of years

01:12:53 and what am I gonna do, sit around?

01:12:54 And the other way of looking at this, by the way,

01:12:59 imagine you made one movie and it was great.

01:13:02 Would you just like stop making movies?

01:13:05 No, generally you’re like, wow,

01:13:06 I really like making movies, let’s make another one.

01:13:09 A lot of times, by the way,

01:13:10 the second one or the third one, not that great,

01:13:11 but the fourth one, awesome.

01:13:13 And no one forgets the second,

01:13:15 or everyone forgets the second and the third one.

01:13:17 So there’s just this constant process of like,

01:13:20 can I produce and is that fun?

01:13:23 Is that exciting?

01:13:24 What else can I learn?

01:13:25 So this machine learning stuff for me

01:13:26 has been this awesome new chapter of being like,

01:13:31 man, that’s something I didn’t understand at all.

01:13:33 And now I feel like I’m one 10th of the way there.

01:13:37 And that feels like a big mountain to climb.

01:13:40 So I distracted us from the original question.

01:13:42 No, and we’ll return to the machine learning

01:13:44 cause I’d love to explore your interest there.

01:13:46 But I mean, speaking of sort of challenges and hard things,

01:13:50 is there a possible world

01:13:52 where sitting in a room with Mark Zuckerberg

01:13:56 with a $1 billion deal, you turn it down?

01:14:00 Yeah, of course.

01:14:01 What does that world look like?

01:14:03 Why would you turn it down?

01:14:04 Why did you take it?

01:14:05 What was the calculation that you were making?

01:14:08 Thus enters the world of counterfactuals

01:14:11 and not really knowing.

01:14:13 And if only we could run that experiment.

01:14:15 Well, the universe exists,

01:14:16 it’s just running in parallel to our own.

01:14:18 Yeah, it’s so fascinating, right?

01:14:23 I mean, we’re talking a lot about money,

01:14:24 but the real question was,

01:14:28 I’m not sure you’ll believe me when I say this,

01:14:30 but could we strap our little company

01:14:33 onto the side of a rocket ship

01:14:36 and like get out to a lot of people really, really quickly

01:14:39 with the support, with the talent of a place like Facebook?

01:14:45 I mean, people often ask me what I would do differently

01:14:48 at Instagram today.

01:14:49 And I say, well, I’d probably hire more carefully

01:14:51 because we showed up just like before I knew it,

01:14:53 we had like a hundred people on the team

01:14:56 and 200, then 300.

01:14:57 I don’t know where all these people were coming from.

01:14:59 I never had to recruit them.

01:15:00 I never had to screen them.

01:15:02 They were just like internal transfers, right?

01:15:05 So it’s like relying on the Facebook hiring machine,

01:15:07 which is quite sort of, I mean, it’s an elaborate machine.

01:15:11 It’s great, by the way.

01:15:12 They have really talented people there.

01:15:15 But my point is the choice was like, take this thing,

01:15:21 put it on the side of a rocket ship

01:15:22 that you know is growing very quickly.

01:15:25 Like I had seen what had happened

01:15:27 when Ev sold Blogger to Google and then Google went public.

01:15:30 Remember we sold before Facebook went public.

01:15:34 There was a moment at which the stock price was $17,

01:15:37 by the way, Facebook stock price was $17.

01:15:40 I remember thinking, what the fuck did I just do, right?

01:15:43 Now at 320 ish, I don’t know where we are today.

01:15:48 But like, okay, like the best thing by the way

01:15:52 is like when the stock is down, everyone calls you a dope.

01:15:56 And then when it’s up, they also call you a dope,

01:15:58 but just for a different reason, right?

01:16:01 Like you can’t win.

01:16:02 Less than in there somewhere.

01:16:03 Yeah.

01:16:04 So, but you know, the choice is to strap yourself

01:16:06 to a rocket ship or to build your own.

01:16:08 You know, Mr. Elon built his own

01:16:11 literally with a rocket ship.

01:16:13 That’s a difficult choice because there’s a world.

01:16:18 Actually, I would say something different,

01:16:19 which is Elon and others decided to sell PayPal

01:16:23 for not that much.

01:16:25 I mean, how much was it about a billion dollars?

01:16:26 I can’t remember.

01:16:27 Something like that, yeah.

01:16:28 I mean, it was early and,

01:16:30 but it’s worth a lot more now.

01:16:31 To then build a new rocket ship.

01:16:33 So this is the cool part, right?

01:16:34 If you are an entrepreneur

01:16:37 and you own a controlling stake in the company,

01:16:40 not only is it really hard to do something else

01:16:42 with your life because all of the, you know,

01:16:45 value is tied up in you as a personality

01:16:48 attached to this company, right?

01:16:50 But if you sell it and you’re getting yourself enough capital

01:16:53 and you like have enough energy,

01:16:55 you can do another thing or 10 other things

01:16:58 or in Elon’s case, like a bunch of other things.

01:17:00 I don’t know, like I lost count at this point.

01:17:03 And it might’ve seemed silly at the time.

01:17:05 And sure, like if you look back,

01:17:07 man, PayPal is worth a lot now, right?

01:17:10 But I don’t know.

01:17:11 Like, do you think Elon like cares about like,

01:17:13 are we gonna buy Pinterest or not?

01:17:15 Like, I just, he is,

01:17:17 he created a massive capital

01:17:20 that allowed him to do what he wants to do.

01:17:23 And that’s awesome.

01:17:24 That’s more freeing than anything

01:17:25 because when you are an entrepreneur attached to a company,

01:17:28 you gotta stay at that company for a really long time.

01:17:30 It’s really hard to remove yourself.

01:17:32 But I’m not sure how much he loved

01:17:34 PayPal versus SpaceX and Tesla.

01:17:37 I have a sense that you love Instagram.

01:17:41 Yeah, I loved enough to like work

01:17:43 for six years beyond the deal.

01:17:44 Which is rare, which is very rare.

01:17:46 You chose.

01:17:47 But can I tell you why?

01:17:48 Sure.

01:17:49 It’s, please.

01:17:51 There are not a lot of companies that you can be part of

01:17:55 where the Pope’s like,

01:17:57 I would like to sign up for your product.

01:17:59 Like I’m not a religious person at all.

01:18:01 I’m really not.

01:18:02 Yeah.

01:18:03 You go to the Vatican and you’re like walking

01:18:05 among giant columns and you’re hearing the music

01:18:07 and everything and like the Pope walks in

01:18:10 and he wants to press the signup button on your product.

01:18:12 Like it’s a moment in life, okay?

01:18:15 No matter what your persuasion, okay?

01:18:19 The number of doors and experiences that that opened up

01:18:22 was, it was incredible.

01:18:23 I mean, the people I got to meet, the places I got to go,

01:18:27 I assume maybe like a payments company

01:18:30 is slightly different, right?

01:18:32 But that’s why, like it was so fun.

01:18:34 And plus I truly believed we were building

01:18:37 such a great product and I loved, loved the game.

01:18:40 It wasn’t about money.

01:18:41 It was about the game.

01:18:43 Do you think you had the guts to say no?

01:18:46 Is that, so here’s, I often think about this,

01:18:49 like how hard is it for an entrepreneur to say no?

01:18:52 Because the peer pressure.

01:18:54 So every, like basically the sea of entrepreneurs

01:18:57 in Silicon Valley are gonna tell you,

01:18:59 I mean, this is their dream.

01:19:01 The thing you were sitting before is a dream.

01:19:05 To walk away from that is really,

01:19:07 it seems like nearly impossible.

01:19:11 Because Instagram could in 10 years be,

01:19:16 you could talk about Google.

01:19:17 You could be making self driving cars

01:19:20 and building rockets that go to Mars

01:19:21 and compete with SpaceX.

01:19:23 Totally.

01:19:24 And so that’s an interesting decision to say,

01:19:28 am I willing to risk it?

01:19:31 And the reason I also say it’s an interesting decision

01:19:34 because it feels like per our previous discussion,

01:19:37 if you’re launching a social network company,

01:19:42 there’s going to be that meeting, whatever that number is.

01:19:45 If you’re successful, if you’re on this rocket ship

01:19:48 of success, there’s going to be a meeting

01:19:50 with one of the social media, social network companies

01:19:53 that wanna buy you, whether it’s Facebook or Twitter,

01:19:58 but it could also very well be Google

01:20:00 who seems to have like a graveyard

01:20:04 of failed social networks.

01:20:06 And it’s, I mean, I don’t know.

01:20:08 I think about that, how difficult it is

01:20:11 for an entrepreneur to make that decision.

01:20:13 How many have successfully made that decision?

01:20:15 I guess, this is a big question.

01:20:18 It’s sad to me, to be honest,

01:20:20 that too many make that decision,

01:20:21 perhaps for the wrong reason.

01:20:23 Sorry, when you say make the decision,

01:20:25 you mean to the affirmative.

01:20:26 To the affirmative, yeah.

01:20:27 Got it, yeah.

01:20:28 There are also companies that don’t sell, right?

01:20:31 And take the PATH and say, we’re gonna be independent.

01:20:34 And then you’ve never heard of them again.

01:20:37 Like I remember PATH, right?

01:20:40 Was one of our competitors early on.

01:20:43 There was a big moment when they had,

01:20:45 I can’t remember what it was,

01:20:46 like $110 million offer from Google or something.

01:20:50 It might’ve been larger, I don’t know.

01:20:54 And I remember there was like this big TechCrunch article

01:20:56 that was like, they turned it down after talking deeply

01:20:59 about their values and everything.

01:21:01 And I don’t know the inner workings of Foursquare,

01:21:05 but I’m certain there were many conversations over time

01:21:09 where there were companies that wanted Foursquare as well.

01:21:11 Recently, I mean, what other companies?

01:21:15 There’s Clubhouse, right?

01:21:16 Like, I don’t know.

01:21:17 Maybe people were really interested in them too.

01:21:20 Like there are plenty of moments where people say no

01:21:25 and we just forget that those things happen.

01:21:28 We only focus on the ones where like they said yes

01:21:33 and like, wow, like what if they had stayed independent?

01:21:36 So I don’t know.

01:21:37 I used to think a lot about this and now I just don’t

01:21:39 because I’m like, whatever.

01:21:42 Things have gone pretty well.

01:21:44 I’m ready for the next game.

01:21:45 I mean, think about an athlete where, I don’t know,

01:21:50 maybe they do something wrong in the World Series

01:21:53 or whatever.

01:21:54 If you let it haunt you for the rest of your career,

01:21:57 like why not just be like, I don’t know, it was a game.

01:22:00 Next game, next shot, right?

01:22:02 And if you just move to that world,

01:22:05 like at least I have a next shot, right?

01:22:07 No, that’s beautiful, but I mean, just insights

01:22:10 and it’s funny you brought up Clubhouse.

01:22:12 It is very true.

01:22:14 It seems like Clubhouse is on the downward path

01:22:19 and it’s very possible to see a billion plus dollar deal

01:22:23 at some stage, maybe like a year ago or half a year ago

01:22:27 from Facebook, from Google.

01:22:28 I think Facebook was flirting with that idea too.

01:22:30 And I think a lot of companies probably were.

01:22:34 I wish it was more public.

01:22:37 You know what?

01:22:37 There’s not like a bad public story

01:22:41 about them making the decision to walk away.

01:22:43 We just don’t hear about it.

01:22:45 And then we get to see the results of that success

01:22:47 or the failure, more often failure.

01:22:49 So a couple of things, one is,

01:22:52 I would not assume Clubhouse is down for the count at all.

01:22:54 They’re young, they have plenty of money,

01:22:56 they’re run by really smart people.

01:22:59 I’d give them like a very fighting chance to figure it out.

01:23:02 There are a lot of times when people call Twitter

01:23:04 down for the count and they figure it out

01:23:05 and they seem to be doing well, right?

01:23:08 So just backing up like,

01:23:09 and not knowing anything about their internals,

01:23:11 like there’s a strong chance they will figure it out

01:23:15 and that people are just down

01:23:16 because they like being down about companies.

01:23:18 They like assuming that they’re gonna fail.

01:23:20 So who knows, right?

01:23:21 But let’s take the ones in the past

01:23:22 where like we know how it played out.

01:23:24 There are plenty of examples

01:23:25 where people have turned down big offers

01:23:28 and then you’ve just never heard from them again,

01:23:30 but we never focus on the companies

01:23:32 because you just forget that those were big.

01:23:34 But inside your psyche,

01:23:38 I think it’s easy for someone with enough money

01:23:42 to say money doesn’t matter, which I think is like,

01:23:45 it’s bullshit.

01:23:45 Of course, money matters to people, but at the moment,

01:23:50 you just can’t even grasp like the number of zeros

01:23:53 that you’re talking about.

01:23:53 It just doesn’t make sense, right?

01:23:56 So to think rationally in that moment

01:23:58 is not something many people are equipped to do,

01:24:01 especially not people

01:24:03 where I think we had founded the company a year earlier,

01:24:05 maybe two years earlier, like a year and a half,

01:24:07 we were 13 people, but I will say,

01:24:11 I still don’t know if it was the right decision

01:24:14 because I don’t have that counterfactual.

01:24:16 I don’t know that other world.

01:24:18 I’m just thankful that by and large,

01:24:21 most people love Instagram, still do.

01:24:23 By and large, people are very happy

01:24:25 with like the time we had there

01:24:28 and I’m proud of what we built.

01:24:29 So like, I’m cool.

01:24:31 Like now it’s next shot, right?

01:24:35 Well, if we could just linger on this Yankees versus Red Sox,

01:24:40 the fun of it, the competition over,

01:24:42 I would say over the space of features.

01:24:45 So there are a bunch of features,

01:24:49 like there’s photos, there’s one minute videos on Instagram,

01:24:54 there’s IGTV, there’s stories, there’s reels, there’s live.

01:24:58 So that sounds like it’s like a long list

01:25:01 of too much stuff, but it’s not

01:25:04 because it feels like they’re close together,

01:25:07 but they’re somehow, like what we’re saying,

01:25:09 fundamentally distinct,

01:25:10 like each of the things I mentioned.

01:25:13 Maybe can you describe the philosophies,

01:25:15 the design philosophies behind some of these,

01:25:17 how you were thinking about it

01:25:19 during the historic war between Snapchat and Instagram

01:25:24 or just in general,

01:25:25 like this space of features that was discovered.

01:25:30 There’s this great book by Clay Christensen

01:25:34 called, Competing Against Luck.

01:25:36 It’s like a terrible title,

01:25:39 but within it, there’s effectively an expression

01:25:43 of this thing called jobs to be done theory.

01:25:46 And it’s unclear if like he came up with it

01:25:48 or some of his colleagues,

01:25:50 but there are a bunch of places you can find

01:25:52 with people claiming to have come up

01:25:53 with this jobs to be done theory.

01:25:55 But the idea is if you like zoom out

01:25:59 and you look at your product,

01:26:00 you ask yourself, why are people hiring your product?

01:26:04 Like imagine every product in your life

01:26:07 is effectively an employee, you know,

01:26:10 you’re CEO of your life

01:26:11 and you hire products to be employees effectively.

01:26:13 They all have roles and jobs, right?

01:26:16 Why are you hiring a product?

01:26:18 Why do you want that product

01:26:19 to perform something in your life?

01:26:21 And like, what are the hidden reasons

01:26:22 why you’re in love with this product?

01:26:26 Instagram was about sharing your life

01:26:28 with others visually, period, right?

01:26:31 Why? Because you feel connected with them.

01:26:34 You get to show off.

01:26:35 You get to feel good and cared about, right?

01:26:38 With likes and it turns out that that will,

01:26:43 I think forever define Instagram

01:26:46 and any product that serves that job

01:26:49 is going to do very well, okay?

01:26:52 Stories let’s take as an example

01:26:55 is very much serving that job.

01:26:58 In fact, it serves it better than the original product

01:27:00 because when you’re large and have an enormous audience,

01:27:04 you’re worried about people seeing your stuff

01:27:08 or you’re worried about being permanent

01:27:09 so that a college admissions person

01:27:11 is going to see your photo of you doing something.

01:27:13 And so it turns out that that is a more efficient way

01:27:16 of performing that job than the original product was.

01:27:19 The original product still has its value,

01:27:22 but at scale, these two things together

01:27:24 work really, really well.

01:27:26 Now, I will claim that other parts of the product

01:27:29 over time didn’t perform that job as well.

01:27:32 I think IGTV probably didn’t, right?

01:27:35 Shopping is like completely unrelated

01:27:38 to what I just described, but it might work.

01:27:40 I don’t know, right?

01:27:43 Products I think, products that succeed

01:27:46 are products that all share this parent node

01:27:49 of like this job to be done that is in common.

01:27:52 And then they’re just like different ways of doing it, right?

01:27:55 Apple, I think does a great job with this, right?

01:27:57 It’s like managing your digital life

01:27:59 and all the products just work together.

01:28:01 They sync, they’re like, it’s beautiful, right?

01:28:06 Even if they require like silly specific cords to work,

01:28:10 but they’re all part of a system.

01:28:12 It’s when you leave that system

01:28:14 and you start doing something weird

01:28:15 that people start scratching their head

01:28:17 and I think you are less successful.

01:28:19 So I think one of the challenges

01:28:20 Facebook has had throughout its life

01:28:22 is that it has never fully, I think,

01:28:24 appreciated the job to be done of the main product.

01:28:28 And what it’s done is said,

01:28:29 ooh, there’s a shiny object over there.

01:28:31 That startup’s getting some traction.

01:28:32 Let’s go copy that thing.

01:28:34 And then they’re confused why it doesn’t work.

01:28:36 Like why doesn’t it work?

01:28:37 It’s because the people who show up for this

01:28:40 don’t want that, it’s different.

01:28:42 What’s the purpose of Facebook?

01:28:44 So I remember I was a very early Facebook user.

01:28:47 The reason I was personally excited about Facebook

01:28:50 is because you can, first of all, use your real name.

01:28:55 Like I can exist in this world.

01:28:58 It can be like formally exist.

01:29:00 I like anonymity for certain things, Reddit and so on,

01:29:04 but I want it to also exist not anonymously

01:29:09 so that I can connect with other friends of mine

01:29:12 nonanonymously and there’s a reliable way to know

01:29:16 that I’m real and they’re real and that we’re connecting.

01:29:19 And it’s kind of like, I liked it for the reasons

01:29:24 that people like LinkedIn, I guess.

01:29:27 But like without the form, like not everybody is dressed up

01:29:30 and being super polite, like more like with friends.

01:29:34 But then it became something much bigger than that,

01:29:37 I suppose, there’s a feed.

01:29:39 It became this, I mean, it became a place

01:29:44 to discover content, to share content

01:29:49 that’s not just about connecting directly with friends.

01:29:53 I mean, it became something else.

01:29:54 I don’t even know what it is really.

01:29:56 So you said Instagram is a place

01:29:58 where you visually share your life.

01:30:02 What is Facebook?

01:30:03 Well, let’s go back to the founding of Facebook

01:30:06 and why it worked really well initially at Harvard

01:30:09 and then Dartmouth and Stanford and I can’t remember,

01:30:12 probably MIT, there were like a handful of schools

01:30:14 in that first tranche, right?

01:30:17 It worked because there are communities

01:30:19 that exist in the world that want to transact.

01:30:23 And when I say transact, I don’t mean commercially,

01:30:25 I just mean they want to share, they want to coordinate,

01:30:28 they want to communicate, they want a space for themselves.

01:30:32 And Facebook at its best, I think is that.

01:30:36 And if actually you look at the most popular products

01:30:39 that Facebook has built over time,

01:30:42 if you look at things like groups, marketplace,

01:30:45 groups is enormous.

01:30:47 And groups is effectively like everyone can found

01:30:50 their own little Stanford or Dartmouth or MIT, right?

01:30:53 And find each other and share and communicate

01:30:57 about something that matters deeply to them.

01:31:00 That is the core of what Facebook was built around.

01:31:03 And I think today is where it stands most strongly.

01:31:09 Yeah, it’s brilliant.

01:31:09 The groups, I wish groups were done better.

01:31:13 It feels like it’s not a first class citizen.

01:31:16 I know I may be saying something without much knowledge,

01:31:19 but it feels like it’s kind of bolted on

01:31:24 while being used a lot.

01:31:26 It feels like there needs to be a little bit more structure

01:31:28 in terms of discovery, in terms of like.

01:31:32 I mean, look at Reddit.

01:31:33 Like Reddit is basically groups of public and open

01:31:36 and a little bit crazy, right?

01:31:38 In a good way.

01:31:40 But there’s clear product market fit

01:31:42 for that specific use case.

01:31:44 And it doesn’t have to be a college, it can be anything.

01:31:47 It can be a small group, a big group,

01:31:48 it can be group messaging.

01:31:50 Facebook shines, I think, when it leans into that.

01:31:53 I think when there are other companies

01:31:56 that just seem exciting and now all of a sudden

01:32:00 the product shifts in some fundamental way

01:32:02 to go try to compete with that other thing,

01:32:05 that’s when I think consumers get confused.

01:32:09 Even if you can be successful,

01:32:10 like even if you can compete with that other company,

01:32:13 even if you can figure out how to bolt it on,

01:32:16 eventually you come back and you look at the app

01:32:17 and you’re like, I just don’t know why I opened this app.

01:32:20 Like why, like too many things going on.

01:32:23 And that was always a worry.

01:32:24 I mean, you listed all the things at Instagram

01:32:26 and it almost gave me a heart attack,

01:32:27 like way too many things.

01:32:30 But I don’t know, entrepreneurs get bored.

01:32:31 They want to add things.

01:32:32 They want to like, right?

01:32:35 I don’t have a good answer for it,

01:32:37 except for that I think being true to your original use case

01:32:41 and not even original use case,

01:32:43 but sorry, actually not use case, original job.

01:32:46 There are many use cases under that job.

01:32:49 Being true to that and like being really good at it

01:32:52 over time and morphing as needs change,

01:32:57 I think that’s how to make a company last forever.

01:32:59 And I mean, honestly, like my main thesis

01:33:03 about why Facebook is in the position it is today

01:33:07 is if they have had a series of product launches

01:33:12 that delighted people over time,

01:33:16 I think they’d be in a totally different world.

01:33:17 So just like imagine for a moment,

01:33:20 and by the way, Apple’s entering this,

01:33:21 but like Apple for so long,

01:33:23 just like product after product,

01:33:25 you couldn’t wait for it.

01:33:26 You stood in line for it.

01:33:27 You talked about it.

01:33:28 You got excited.

01:33:29 Amazon makes your life so easy.

01:33:31 It’s like, wow, I needed this thing

01:33:34 and it showed up at my door two days later.

01:33:36 And like both of these companies, by the way,

01:33:39 Amazon, Apple have issues, right?

01:33:41 There are labor issues,

01:33:43 whether it’s here in the US or in China,

01:33:45 there are environmental issues.

01:33:48 But like when’s the last time

01:33:49 you heard like a large chorus being like,

01:33:52 these companies better pay

01:33:54 for what they’re doing on these things, right?

01:33:56 I think Facebook’s main issue today is like,

01:33:59 you need to produce a hit.

01:34:01 If you don’t produce hits,

01:34:03 it’s really hard to keep consumers on your side.

01:34:06 Then people just start picking on you

01:34:08 for a variety of reasons, whether it’s right or wrong.

01:34:11 I’m not even going to place a judgment

01:34:12 right here and right now.

01:34:13 I’m just going to say that it is way better

01:34:17 to be in a world where you are producing hits

01:34:19 and consumers love what you’re doing

01:34:21 because then they’re on your side.

01:34:23 And I think that’s, it’s the past 10 years

01:34:26 for Facebook has been fairly hard on this dimension.

01:34:29 So, and by hits, it doesn’t necessarily mean financial hits.

01:34:33 It feels like to me, what you’re saying

01:34:34 is something that brings joy.

01:34:37 A product that brings joy

01:34:38 to some fraction of the population.

01:34:40 Yeah, I mean, TikTok isn’t just literally an algorithm.

01:34:45 In some ways, TikTok’s content and algorithm

01:34:49 have more sway now over the American psyche

01:34:53 than Facebook’s algorithm, right?

01:34:54 It’s visual, it’s video.

01:34:57 By the way, it’s not defined by who you follow.

01:34:59 It’s defined by some magical thing that,

01:35:01 by the way, if someone wanted to tweak

01:35:02 to show you a certain type of content for some reason,

01:35:05 they could, right, but people love it.

01:35:10 So as a CEO, let me ask you a question

01:35:13 because leadership matters.

01:35:17 This is a complicated question.

01:35:19 Why is Mark Zuckerberg distrusted, disliked

01:35:23 and sometimes even hated by many people in public?

01:35:28 Right, that is a complicated question.

01:35:30 Well, the premise, I’m not sure I agree with the premise.

01:35:34 And I can expand that to include

01:35:38 even a more mysterious question for me, Bill Gates.

01:35:42 Hmm.

01:35:44 What is the Bill Gates version of the question?

01:35:47 Do you think people hate Bill Gates?

01:35:49 No, distrust.

01:35:51 Ah.

01:35:52 So takeaway one, it’s a checklist.

01:35:56 There is, I think Mark Zuckerberg’s distrust

01:36:00 is the primary one, but there’s also like a dislike,

01:36:04 maybe hate is too strong a word,

01:36:05 but it’s just if you look at like the articles

01:36:09 that are being written and so on, there’s a dislike.

01:36:13 And it makes, it’s confusing to me

01:36:15 because it’s like the public picks certain individuals

01:36:18 and they attach certain kinds of emotions

01:36:21 to those individuals.

01:36:22 Yeah, so someone once just recently said,

01:36:27 there’s a strong case that founder led companies

01:36:30 have this problem and that a lot of Mark’s issues

01:36:33 come today come from the fact that he is a visible founder

01:36:38 with this story that people have watched in both a movie

01:36:42 and they followed along and he’s this boy wonder kid

01:36:45 who became one of the world’s richest people

01:36:47 and he’s no longer Mark the person,

01:36:50 he’s Mark this image of a person

01:36:53 with enormous wealth and power.

01:36:55 And in today’s world, we have issues

01:36:59 with enormous wealth and power for a variety of reasons.

01:37:02 One of which is we’ve been stuck inside

01:37:05 for a year and a half, two years.

01:37:07 One of which is a lot of people were really unhappy

01:37:10 about not the last election, but the last, last election.

01:37:13 And where do you take out that anger?

01:37:15 Who do you blame but the people in charge?

01:37:18 That’s one example or one reason why I think

01:37:22 a lot of people express anger or resentment

01:37:26 or unhappiness with Mark.

01:37:29 At the same time, I don’t know,

01:37:31 I pointed out to that person, I was like, well,

01:37:34 I don’t know, I think a lot of people really like Elon.

01:37:38 Elon arguably, he kept his factory open here

01:37:41 throughout COVID protocols, which arguably

01:37:44 a lot of people would be against.

01:37:47 While saying a bunch of crazy offensive things

01:37:50 on the internet, they still.

01:37:52 And basically gives the middle finger to the SEC

01:37:56 on Twitter and I don’t know, I’m like,

01:37:59 well, there’s a founder and like people kind of like him.

01:38:02 So I do think that the founder and slash CEO

01:38:09 of a company that’s a social network company

01:38:11 is like an extra level of difficulty.

01:38:13 If life is a video game,

01:38:15 you just chose the harder video game.

01:38:17 So, I mean, that’s why it’s interesting to ask you

01:38:20 because you were the founder and CEO of a social network.

01:38:24 I challenge it because.

01:38:25 Exactly, but you’re one of the rare examples.

01:38:30 Even Jack Dorsey’s disliked, not to the degree,

01:38:34 but it just seems harder

01:38:35 when you’re running a social media company.

01:38:38 It’s interesting.

01:38:39 I never thought of Jack as just like,

01:38:41 I think generally he’s well respected.

01:38:45 Yeah, I think so.

01:38:45 I think you’re right, but he’s not loved.

01:38:50 Yeah.

01:38:51 And I feel like you, I mean, to me,

01:38:53 Twitter is an incredible thing.

01:38:54 Yeah, again, can I just come back to this point,

01:38:57 which seems over simplistic,

01:38:59 but I really do think how a product makes someone feel,

01:39:04 they ascribe that feeling to the founder.

01:39:07 Yep.

01:39:09 So make people feel good.

01:39:11 So think about it.

01:39:13 Let’s just go with this thesis for a second.

01:39:14 Sure, I like it though.

01:39:17 Amazon’s pretty utilitarian, right?

01:39:19 It delivers brown boxes to your front door.

01:39:21 Sure, you can have Alexa

01:39:23 and you can have all these things, right?

01:39:25 But in general, it delivers stuff quickly to you

01:39:28 at a reasonable price, right?

01:39:31 I think Jeff Bezos is wonderfully wealthy,

01:39:34 thoughtful, smart guy, right?

01:39:36 But people kind of feel that way about him.

01:39:38 They’re like, wow, this is really big.

01:39:40 We’re impressed that this is really big.

01:39:42 But he’s doing the same space stuff Elon’s doing,

01:39:45 but they don’t necessarily ascribe

01:39:47 the same sense of wonder, right?

01:39:50 Now let’s take Elon.

01:39:51 And again, this is pet theory.

01:39:52 I don’t have much proof other than my own intuition.

01:39:56 He is literally about living the future.

01:39:59 Mars, it’s about wonder.

01:40:01 It’s about going back to that feeling as a kid

01:40:04 when you looked up to the stars and asked,

01:40:05 is there life out there?

01:40:08 People get behind that because it’s a sense of hope

01:40:11 and excitement and innovation.

01:40:13 And like, you can say whatever you want,

01:40:15 but we ascribe that emotion to that person.

01:40:18 Now, let’s say you’re on a social network

01:40:21 and people make you kind of angry

01:40:23 because they disagree with you

01:40:24 or they say something ridiculous

01:40:25 or they’re living a FOMO type life where you’re like,

01:40:28 wow, I wish I was doing that thing.

01:40:31 I think Instagram, if I were to think back,

01:40:33 by and large, when I was there,

01:40:35 was not about FOMO, was not about this influencer economy,

01:40:39 although it certainly became that way closer to the end.

01:40:43 It was about the sense of wonder and happiness

01:40:45 and beautiful things in the world.

01:40:47 And I don’t know, I mean, like,

01:40:49 I don’t want to have a blind spot,

01:40:50 but I don’t think anyone had a strong opinion

01:40:52 about me one way or the other.

01:40:53 For the longest time, the way people explained to me,

01:40:55 I mean, if you want to go for toxicity,

01:40:57 you go to Facebook or Twitter.

01:40:59 If you want to go to feel good about life,

01:41:01 you go to Instagram to enjoy, celebrate life.

01:41:04 And my experience been talking to people

01:41:05 is they gave me the benefit of the doubt because of that.

01:41:08 But if your experience of the product

01:41:10 is kind of makes you angry, it’s where you argue.

01:41:13 I mean, a big part of Jack might be

01:41:15 that he wasn’t actually the CEO for a very long time

01:41:17 and only became recently.

01:41:19 So I’m not sure how much of the connection got made.

01:41:23 But in general, I mean, if you hate,

01:41:28 I’m just thinking about other companies

01:41:29 that aren’t tech companies.

01:41:30 If you hate like what a company is doing

01:41:32 or it makes you not feel happy.

01:41:36 I don’t know, like people are really angry

01:41:37 about Comcast or whatever.

01:41:39 Are they even called Comcast anymore?

01:41:40 It’s like Xfinity or something, right?

01:41:42 They had to rebrand.

01:41:43 They became meta, right?

01:41:44 And it’s like, but my point is if it makes you angry.

01:41:48 That’s beautiful, yeah.

01:41:50 But the thing is, this is me saying this.

01:41:54 I think your thesis is very strong and correct,

01:41:58 has elements of correctness,

01:42:00 but I still personally put some blame on individuals.

01:42:05 Of course.

01:42:06 I think you said, Elon, looking up,

01:42:10 there’s something about Childlike Wander to him,

01:42:13 like to his personality, his character,

01:42:16 something about, I think more so than others

01:42:19 where people can trust them.

01:42:21 And there’s, I don’t know,

01:42:23 Sondra Prachai is an example of somebody who’s like,

01:42:26 there’s some kind, it’s hard to put into words,

01:42:29 but there’s something about the human being

01:42:32 where he’s trustworthy.

01:42:34 He’s human in a way that connects to us.

01:42:38 And the same with Sajid Nadella.

01:42:40 I mean, some of these folks, something about us

01:42:46 is drawn to them, even when they’re flawed.

01:42:48 Even like, so your thesis really holds up for Steve Jobs

01:42:53 because I think people didn’t like Steve Jobs,

01:42:55 but he delivered products

01:42:57 and then they fell in love every time.

01:43:01 I guess you could say that the CEO,

01:43:03 the leader is also a product.

01:43:05 And if they keep delivering a product that people like

01:43:08 by being in public and saying things that people like,

01:43:11 that’s also a way to make people happy.

01:43:14 But from a social network perspective,

01:43:16 it makes me wonder how difficult it is

01:43:19 to explain to people why certain things happen,

01:43:22 like to explain machine learning,

01:43:24 to explain why certain,

01:43:30 the woke mob effect happens

01:43:32 or the certain kinds of like bullying happens,

01:43:36 which is like, it’s human nature combined with algorithm.

01:43:40 And it’s very difficult to control for

01:43:42 how the spread of quote unquote misinformation happens.

01:43:45 It’s very difficult to control for that.

01:43:47 And so you try to decelerate certain parts

01:43:50 and you create more problems than you solve.

01:43:53 And anything that looks at all like censorship

01:43:55 can create huge amounts of problems as a slippery slope.

01:43:58 And then you have to inject humans

01:44:01 to oversee the machine learning algorithms.

01:44:03 And anytime you inject humans into the system,

01:44:05 it’s gonna create a huge number of problems.

01:44:07 And I feel like it’s up to the leader

01:44:08 to communicate that effectively, to be transparent.

01:44:12 First of all, design products

01:44:13 that don’t have those problems.

01:44:15 And second of all, when they have those problems,

01:44:17 to be able to communicate with them.

01:44:19 I guess that’s all going to,

01:44:21 when you run a social network company, your job is hard.

01:44:25 Yeah, I will say the one element that you haven’t named

01:44:28 that I think you’re getting at is just bedside manner,

01:44:31 which Steve Jobs, I never worked for him.

01:44:35 I never met him in person.

01:44:38 Had an uncanny ability in public to have bedside manner.

01:44:43 I mean, some of the best clips of Steve Jobs

01:44:46 from like, I would say maybe the 80s

01:44:49 when he’s on the stage and getting questions

01:44:51 from the audience about life.

01:44:54 And he’ll take this question that is like,

01:44:56 how are you gonna compete with blah?

01:44:58 And it’s super boring.

01:44:59 And I don’t even know the name of the company.

01:45:01 And his answer is as if you just asked your grandfather,

01:45:05 the meaning of life.

01:45:07 And you sit there and you’re just like, what?

01:45:10 And there’s that bedside manner.

01:45:13 And if you lack that, or if that’s just not intuitive to you,

01:45:16 I think that it can be a lot harder

01:45:20 to gain the trust of people.

01:45:21 And then add on top of that, missteps of companies.

01:45:27 I don’t know if you have any friends from the past

01:45:29 where maybe they crossed you once,

01:45:31 or maybe you get back together and you’re friends again,

01:45:34 but you just never really forget that thing.

01:45:36 It’s human nature not to forget.

01:45:38 I’m Russian, you crossed me once.

01:45:41 We solved the problem.

01:45:43 So my point is, humans don’t forget.

01:45:48 And if there are times in the past

01:45:50 where they feel like they don’t trust the company

01:45:52 or the company hasn’t had their back,

01:45:55 that is really hard to earn back,

01:45:57 especially if you don’t have that bedside manner.

01:46:00 And again, I’m not attributing this specifically to Marc

01:46:02 because I think a lot of the companies have this issue

01:46:06 where one, you have to be trustworthy as a company

01:46:10 and live by it and live by those actions.

01:46:12 And then two, I think you need to be able

01:46:14 to be really relatable in a way that’s very difficult

01:46:18 if you’re worth what these people are.

01:46:20 It’s really hard.

01:46:22 Yeah.

01:46:23 Jack does a pretty good job of this by being a monk.

01:46:27 But I also, Jack issues attention.

01:46:29 He’s not out there almost on purpose.

01:46:32 He’s just working hard, doing square, right?

01:46:35 I literally shared a desk like this with him at Odeo.

01:46:38 I mean, this normal guy who likes painting,

01:46:41 I remember he would leave early on Wednesdays or something

01:46:45 to go to a painting class.

01:46:47 And he’s creative, he’s thoughtful.

01:46:49 I mean, money makes people more creative and more thoughtful,

01:46:53 extreme versions of themselves, right?

01:46:55 And this was a long, long time ago.

01:46:58 You mentioned that he asked you

01:47:00 to do some kind of JavaScript thing.

01:47:02 We were working on some JavaScript together.

01:47:05 That’s hilarious, like pre Twitter, early Twitter days,

01:47:08 you and Jack Dorsey are in a room together

01:47:11 talking about JavaScript,

01:47:12 solving some kind of menial problem.

01:47:14 Terrible problems, yeah.

01:47:15 I mean, not terrible, just like boring widget problem.

01:47:18 I think it was the Odeo widget

01:47:19 we were working on at the time.

01:47:21 I’m surprised anyone paid me to be in the room as an intern

01:47:24 because I didn’t really provide any value.

01:47:26 I’m very thankful to anyone who included me back in the day.

01:47:30 It was very helpful.

01:47:32 So thank you for listening.

01:47:33 I mean, is there Odeo that’s a precursor to Twitter?

01:47:38 First of all, did you have any anticipation

01:47:40 that this Jack Dorsey guy could be also

01:47:43 a head of a major social network?

01:47:46 And second, did you learn anything from the guy that,

01:47:49 like, do you think it’s a coincidence

01:47:52 that you two were in the room together?

01:47:55 And it’s the coincidence meaning like,

01:47:58 why does the world play its game in a certain way

01:48:01 where these two founders of social networks?

01:48:04 I don’t know.

01:48:04 It’s so weird, right?

01:48:05 Like, I mean, it’s also weird that Mark showed up

01:48:11 in our fraternity my sophomore year

01:48:13 and we got to know each other then,

01:48:16 like long before Instagram.

01:48:18 It’s a small world,

01:48:20 but let me tell a fun story about Jack.

01:48:25 We’re at Odeo and I don’t know,

01:48:27 I think Ev was feeling like people

01:48:28 weren’t working hard enough or something.

01:48:31 Nice.

01:48:32 And I can’t remember exactly what he,

01:48:35 he created this thing where every Friday,

01:48:39 I don’t know if it was every Friday,

01:48:40 I only remember this happening once,

01:48:43 but he had us like a statuette, it’s like of Mary.

01:48:47 And in the bottom, it’s hollow, right?

01:48:50 And I remember on a Friday,

01:48:54 Ev decided he was going to let everyone vote for

01:48:57 who had worked the hardest that week.

01:48:59 We all voted, closed ballot, right?

01:49:01 We all put it in a bucket and he tallied the votes.

01:49:04 And then whoever got the most votes, as I recall,

01:49:07 got the statuette.

01:49:09 And in the statuette was a thousand bucks,

01:49:12 or I recall there was a thousand bucks in it.

01:49:14 It might’ve been a hundred bucks,

01:49:15 but let’s call it a thousand.

01:49:16 It’s more exciting that way.

01:49:17 It felt like a thousand, yeah.

01:49:18 It did to me for sure.

01:49:19 I actually got two votes.

01:49:20 I was very happy.

01:49:21 We were a small company, but as the intern,

01:49:23 I got at least two votes.

01:49:24 So everybody knew how many votes they got individually?

01:49:26 Yeah, yeah.

01:49:27 And I think it was one of these self accountability things.

01:49:29 Anyway, I remember Jack just getting

01:49:31 like the vast majority of votes from everyone.

01:49:35 And I remember just thinking like,

01:49:37 like I couldn’t imagine he would become what he’d become

01:49:39 and do what he would do,

01:49:41 but I had a profound respect that the new guy

01:49:45 who I really liked worked that hard.

01:49:48 And you could see his dedication even then

01:49:51 and that people respected him.

01:49:53 That’s the one story that I remember of him

01:49:55 like working with him specifically from that summer.

01:49:58 Can I take a small tangent on that?

01:49:59 Of course.

01:50:00 There’s kind of a pushback in Silicon Valley

01:50:02 a little bit against hard work.

01:50:04 Can you speak to the sort of the thing you admire

01:50:08 to see the new guy working so hard, that thing?

01:50:11 What is the value of that thing in a company?

01:50:13 See, this is like, just to be very frank,

01:50:16 it drives me nuts.

01:50:17 Like I saw this really funny video on TikTok.

01:50:21 Was it on TikTok?

01:50:22 It was like, I’m taking a break from my mental health

01:50:24 to work on my career.

01:50:26 I thought that was funny.

01:50:27 Um, so I was like, oh, it is kind of phrased that way,

01:50:31 the opposite often, right?

01:50:32 Yeah.

01:50:33 Okay, so a couple of things.

01:50:35 Uh, I, uh, I have worked so hard

01:50:42 to do the things that I did.

01:50:43 Like Mike and I lost years off of our lives,

01:50:47 staying up late, figuring things out,

01:50:50 the stress that comes with the job.

01:50:51 I have a lot more gray hair now than I did back then.

01:50:54 It requires an enormous amount of work

01:50:57 and most people aren’t successful, right?

01:50:59 But even the ones that do don’t skate by.

01:51:04 I am okay if people choose not to work hard

01:51:07 because I don’t actually think there’s anything

01:51:09 in this world that says you have to work hard.

01:51:13 But I do think that great things require a lot of hard work.

01:51:16 So there’s no way you can expect to change the world

01:51:18 without working really hard.

01:51:19 By the way, even changing the world,

01:51:22 you know, the folks that I respect the most

01:51:24 have nudged the world in like a slight direction,

01:51:27 slight, very, very slight.

01:51:30 Like even if Elon accomplishes all the things

01:51:33 he wants to accomplish,

01:51:34 we will have nudged the world in a slight direction,

01:51:38 but it requires enormous amount.

01:51:40 There was an interview with him where he was just like,

01:51:43 he was interviewed, I think, at the Tesla factory

01:51:45 and he was like, work is really hard.

01:51:47 This is actually unhealthy.

01:51:49 And I can’t recall the exact,

01:51:51 but he was like visibly shaken

01:51:52 about how hard he had been working.

01:51:54 And he was like, this is bad.

01:51:55 And unfortunately, I think to have great outcomes,

01:51:57 you actually do need to work

01:51:58 at like three standard deviations above the mean,

01:52:01 but there’s nothing saying that people have to go for that.

01:52:03 See, the thing is, but what I would argue,

01:52:06 this is my personal opinion,

01:52:08 is nobody has to do anything, first of all.

01:52:10 They certainly don’t have to work hard.

01:52:12 But I think hard work in a company should be admired.

01:52:17 And you should not feel like,

01:52:22 you shouldn’t feel good about yourself for not working hard.

01:52:26 Like, so for example, I don’t have to work out.

01:52:30 I don’t have to run.

01:52:31 I hate running, but like, I certainly don’t feel good

01:52:35 if I don’t run because I know for my health,

01:52:37 like there’s certain values,

01:52:39 I guess is what I’m trying to get at.

01:52:40 There’s certain values that you have in life.

01:52:42 It feels like there’s certain values

01:52:43 that companies should have in hard work.

01:52:45 Certain values that companies should have in hard work

01:52:47 is one of the things I think that should be admired.

01:52:51 I often ask this kind of silly question,

01:52:54 just to get a sense of people,

01:52:56 like if I’m hiring and so on.

01:52:58 I just ask if they think it’s better

01:53:00 to work hard or work smart.

01:53:03 It was helpful for me to get a sense of people from that.

01:53:07 Because you think like the right.

01:53:08 The answer’s both.

01:53:09 What’s that?

01:53:10 The answer’s both.

01:53:11 The answer’s both.

01:53:12 I usually try not to give them that,

01:53:13 but sometimes I’ll say both if that’s an option.

01:53:16 But a lot of people kind of,

01:53:19 a surprising number will say work smart.

01:53:22 And there are usually people

01:53:23 who don’t know how to work smart.

01:53:26 And they’re literally just lazy.

01:53:29 Not just, there’s two effects behind that.

01:53:32 One is laziness and the other is ego.

01:53:37 When you’re younger and you say it’s better to work smart,

01:53:40 it means you think you know what it means

01:53:44 to work smart at this early stage.

01:53:46 To me, people that say work hard or both,

01:53:49 they have the humility to understand like,

01:53:52 I’m going to have to work my ass off

01:53:54 because I’m too dumb to know how to work smart.

01:53:56 And people who are self critical in this way,

01:53:59 in some small amount, you have to have some confidence.

01:54:01 But if you have humility,

01:54:03 that means you’re going to actually eventually figure out

01:54:06 what it means to work smart.

01:54:07 And then to actually be successful, you should do both.

01:54:11 So I have a very particular take on this,

01:54:14 which is that no one’s forcing you to do anything.

01:54:19 All choices have consequences.

01:54:22 So if you major in, I don’t know, theoretical literature,

01:54:27 I don’t even know if that’s a major.

01:54:28 I’m just making something up.

01:54:30 That’s supposed to regular literature, applied literature.

01:54:33 Yeah, think about like theoretical Spanish lit

01:54:38 from the 14th century.

01:54:39 Like just make up your esoteric thing.

01:54:41 And then the number of people I went to Stanford with

01:54:44 who get out in the world and they’re like,

01:54:45 wait, what, I can’t find a job?

01:54:47 Like no one wants a theoretical,

01:54:49 like there are plenty of counter examples

01:54:51 of people who have majored in esoteric things

01:54:53 and gone on to be very successful.

01:54:54 So I just want to be clear, it’s not about the major.

01:54:56 But every choice you make, whether it’s to have kids,

01:55:00 like I love my children.

01:55:02 It’s so awesome to have two kids.

01:55:04 And it is so hard to work really hard and also have kids.

01:55:08 It’s really hard.

01:55:09 And there’s a reason why certain very successful people

01:55:12 like don’t have, or not successful,

01:55:14 but people who run very, very large companies or startups

01:55:17 have chosen not to have kids for a while

01:55:19 or chosen not to like prioritize them.

01:55:21 Everything’s a choice.

01:55:22 And like I choose to prioritize my children

01:55:24 because like I want to do that, right?

01:55:27 So everything’s a choice.

01:55:29 Now, once you’ve made that choice,

01:55:33 I think it’s important that the contract is clear,

01:55:36 which is to say,

01:55:37 let’s imagine you were joining a new startup.

01:55:40 It’s important that that startup communicate

01:55:43 that like the expectation is like,

01:55:44 we’re all working really, really hard right now.

01:55:46 You don’t have to join the startup.

01:55:48 But like, if you do just know like you’re,

01:55:51 it’s almost as if you join, I don’t know,

01:55:53 pick your like sports team.

01:55:57 Like let’s go back to the Yankees for a second.

01:56:00 You want to join the Yankees,

01:56:01 but you don’t really want to work that hard.

01:56:03 You don’t really want to do batting practice

01:56:05 or pitching practice or whatever for your position, right?

01:56:09 That to me is wacko.

01:56:11 And that’s actually the world that it feels like we live in

01:56:13 in tech sometimes,

01:56:15 where people both want to work for the Yankees

01:56:17 because it pays a lot,

01:56:18 but like don’t actually want to work that hard.

01:56:21 That I don’t fully understand.

01:56:23 Because if you sign up for some of these things,

01:56:26 just sign up for it.

01:56:26 But it’s okay if you don’t want to sign up for it.

01:56:29 There’s so many wonderful careers in this world

01:56:31 that don’t require 80 hours a week.

01:56:33 But when I read about companies going to like

01:56:35 four day work weeks and stuff,

01:56:36 I just like, I chuckle because I can’t get enough done

01:56:39 with a seven day week.

01:56:41 I don’t know how.

01:56:42 And people will say, oh, you’re just not working smart.

01:56:44 And it’s like, no, I work pretty smart,

01:56:46 I think in general.

01:56:47 Like I wouldn’t have gotten to this point

01:56:49 if I hadn’t like some amount of working smart.

01:56:52 And there is balance though.

01:56:53 So I used to be like a pretty big cyclist.

01:56:55 I don’t do it much anymore just because of kids

01:56:58 and like prioritizing other things, right?

01:57:01 But one of the most important things to learn

01:57:03 as a cyclist is to take a rest day.

01:57:06 But to me and to cyclists,

01:57:07 like resting is a function of optimizing for the long run.

01:57:12 It’s not like a thing that you do for its own merits.

01:57:16 It’s actually like, if you don’t rest,

01:57:17 your muscles don’t recover.

01:57:18 And then you’re just not as,

01:57:19 like you’re not training as efficiently.

01:57:21 You should probably, the successful people I’ve known

01:57:24 in terms of athletes, they hate rest days,

01:57:27 but they know they have to do it for the long term.

01:57:29 They think their opposition is getting stronger and stronger

01:57:32 and that’s the feeling,

01:57:34 but you know it’s the right thing

01:57:36 and usually you need a coach to help you.

01:57:38 Yeah, totally.

01:57:39 So, I mean, I use this thing called training peaks

01:57:41 and it’s interesting

01:57:42 because it actually mathematically shows

01:57:44 like where you are on the curve and all this stuff,

01:57:46 but you have to have that rest,

01:57:49 but it’s a function of going harder for longer.

01:57:52 Again, it’s this reinforcement learning,

01:57:53 like planning the aggregate and the long,

01:57:56 but a lot of people will hide behind laziness

01:57:58 by saying that they’re trying to optimize for the long run

01:58:00 and they’re not, they’re just not working very hard.

01:58:03 But again, you don’t have to sign up for it.

01:58:05 It’s totally cool.

01:58:05 Like, I don’t think less of people

01:58:07 for like not working super hard.

01:58:08 It’s just like, don’t sign up for things

01:58:10 that require working super hard.

01:58:11 And some of that requires for the leadership

01:58:13 to have the guts, the boldness to communicate effectively

01:58:17 at the very beginning.

01:58:17 I mean, sometimes I think most of the problems arise

01:58:20 in the fact that the leadership is kind of hesitant

01:58:24 to communicate the socially difficult truth

01:58:30 of what it takes to be at this company.

01:58:33 So they kind of say, hey, come with us.

01:58:36 There’s, we have snacks, you know, but.

01:58:39 Unlimited vacation and.

01:58:40 Yeah.

01:58:41 You know, Ray at Bridgewater is always fascinating

01:58:44 because, you know, people,

01:58:45 it’s been called like a cult on the outside

01:58:47 or cultish and,

01:58:49 but what’s fascinating is like,

01:58:51 they just don’t give on their principles.

01:58:52 They’re like, listen, this is what it’s like to work here.

01:58:55 We record every meeting.

01:58:57 We’re like brutally honest

01:58:59 and that’s not going to feel right to everyone.

01:59:00 And if it doesn’t feel right to you, totally cool.

01:59:03 Just go work somewhere else.

01:59:04 But if you work here, you are signing up for this.

01:59:08 And that’s, that’s been fascinating to me

01:59:10 because it’s honesty upfront.

01:59:12 It’s a system in which you operate.

01:59:15 And if it’s not for you,

01:59:16 like no one’s forcing you to work there, right?

01:59:19 I actually did.

01:59:20 So I did a conversation with him

01:59:22 and kind of got stuck in a funny moment,

01:59:25 which is at the end I asked him, you know,

01:59:28 to give me honest feedback of how I did on the interview.

01:59:31 And I was.

01:59:32 Did he?

01:59:33 I don’t think so.

01:59:34 He was super nice.

01:59:36 He asked me, he’s like, well,

01:59:38 tell me, did you accomplish

01:59:40 what you were hoping to accomplish?

01:59:42 I was like, that’s not, that’s not.

01:59:45 I’m asking you as an objective observer

01:59:47 of two people talking, how do we do today?

01:59:52 And then he’s like, well,

01:59:53 he gave me this politician answer.

01:59:55 Well, I feel like we’ve accomplished

01:59:57 successful communication of like ideas,

02:00:00 which is I’d love to spread some of the ideas

02:00:03 in that, like in principles and so on.

02:00:06 I was like.

02:00:07 Back to my original point,

02:00:08 it’s really hard to get.

02:00:10 Even for Radalia.

02:00:11 It’s really hard to give feedback.

02:00:13 And one of the other things I learned from him

02:00:15 and just people in that world is like,

02:00:19 man, humans really like to pretend

02:00:22 like they’ve come to,

02:00:24 that they’ve come to some kind of meeting of the minds.

02:00:27 Like if there’s conflict, if you and I have conflict,

02:00:30 it’s always better to meet face to face, right?

02:00:32 We’re on the phone.

02:00:33 Slack is not great, right?

02:00:35 Email is not great, but face to face.

02:00:37 What’s crazy is you and I get together

02:00:38 and we actively try to,

02:00:40 even if we’re not actually solving the conflict,

02:00:43 we actively try to paper over the conflict.

02:00:45 Oh yeah, it didn’t really bother me that much.

02:00:48 Oh yeah, I’m sure you didn’t mean it.

02:00:50 But like, no, in our minds we’re still there.

02:00:54 So this is one of the things that as a leader,

02:00:56 you always have to be digging,

02:00:58 especially as you ascend.

02:00:59 Like straight to the conflict.

02:01:00 Yeah, but as you ascend,

02:01:02 no one wants to tell you you’re crazy.

02:01:03 No one wants to tell you your idea is bad.

02:01:06 And you can, you’re like, oh,

02:01:08 oh, I’m going to be a leader.

02:01:09 And the idea is, well, I’m just going to ask people.

02:01:12 No one tells you.

02:01:13 So like you have to look for the markers,

02:01:16 knowing that literally just people

02:01:18 aren’t going to tell you along the way and be paranoid.

02:01:21 I mean, you asked about selling the company.

02:01:24 I think one of the biggest differences between me

02:01:25 and a lot of other entrepreneurs is like,

02:01:28 I wasn’t completely confident we could do it.

02:01:30 Like we could be alone and actually be great.

02:01:35 And if any entrepreneur is honest with you,

02:01:37 they also feel that way.

02:01:39 But a lot of people are like,

02:01:40 well, I have to be cocky and just say,

02:01:42 I can do this on my own.

02:01:43 We’re going to be fine.

02:01:44 We’re going to crush everyone.

02:01:46 Some people do say that and then it’s not right.

02:01:49 And they, and they fail.

02:01:50 But being honest in that moment with yourself,

02:01:55 with those close to you.

02:01:57 And also you talked about the personality of leaders

02:02:00 and who resonates and who doesn’t.

02:02:04 It’s rare that I see leaders be vulnerable, rare.

02:02:09 And one thing I tried to do at Instagram,

02:02:12 at least internally was like, say when I screwed up

02:02:16 and like point out how I was wrong about things

02:02:19 and point out where my judgment was off.

02:02:22 Everyone thinks they have to bat a thousand, right?

02:02:25 Like that’s crazy.

02:02:27 The best quant hedge funds in the world,

02:02:29 bat 50.001%.

02:02:31 They just take a lot of bets, right?

02:02:34 Renaissance, they might, they might bat 51%, right?

02:02:37 But holy hell, like the question isn’t,

02:02:41 are you right every single time

02:02:43 and you have to seem invincible.

02:02:46 The question is how many at bats do you get?

02:02:48 And on average, are you better on average, right?

02:02:53 With enough bets and enough at bats

02:02:55 that your aggregate can be very high.

02:02:58 I mean, Steve Jobs was wrong at a lot of stuff.

02:03:01 The Newton too early, right?

02:03:03 Next, not quite right.

02:03:05 There was even a time where he said like,

02:03:08 no one will ever wanna watch a video on the iPod.

02:03:13 Totally wrong.

02:03:14 But who cares if you come around

02:03:16 and realize your mistake and fix it.

02:03:18 It becomes just like you said, harder and harder

02:03:20 when your ego grows and the number of people around you

02:03:23 that say positive things towards you grows.

02:03:26 I actually think it’s really valuable that,

02:03:29 like let’s imagine a counterfactual

02:03:31 where Instagram became worth like $300 billion

02:03:34 or something crazy, right?

02:03:37 I kind of like that my life is relatively normal now.

02:03:40 When I say relatively, you get what I mean.

02:03:41 I’m not making a claim that I live a normal life,

02:03:44 but like I certainly don’t live in a world

02:03:46 where there are like 15 Sherpas following me,

02:03:49 like fetching me water, like that’s not how it works.

02:03:52 I actually like that I have a sense of humility of like,

02:03:56 I may not found another thing that’s nearly as big

02:03:59 so I have to work twice as hard

02:04:01 or I have to like learn twice as much.

02:04:04 I have to, we haven’t talked about machine learning yet,

02:04:07 but my favorite thing is all these like famous,

02:04:11 you know, tech guys who have worked in the industry,

02:04:14 pontificating about the future of machine learning

02:04:17 and how it’s gonna kill us all, right?

02:04:19 And like, I’m pretty sure they’ve never tried

02:04:22 to build anything with machine learning themselves.

02:04:24 Yes, so there’s a nice line between people

02:04:27 that actually build stuff with machine,

02:04:29 like actually program something

02:04:31 or at least understand some of those fundamentals

02:04:33 and the people that are just saying philosophical stuff

02:04:36 or journalists and so on.

02:04:38 It’s an interesting line to walk

02:04:40 because the people who program are often not philosophers.

02:04:44 Or don’t have the attention,

02:04:45 they can’t write an op ed for the Wall Street Journal,

02:04:48 like it doesn’t work.

02:04:49 So like, it’s nice to be both a little bit,

02:04:51 like to have elements of both.

02:04:52 My point is the fact that I have to learn stuff from scratch

02:04:56 or that I choose to are like.

02:04:58 It’s humbling.

02:04:59 Yeah, I mean, again, I have a lot of advantages.

02:05:02 I like, but my point is it’s awesome

02:05:06 to be back in a game where you have to fight.

02:05:11 That is, that’s fun.

02:05:13 So being humble, being vulnerable,

02:05:16 it’s an important aspect of a leader

02:05:18 and I hope it serves me well,

02:05:19 but like, I can’t fast forward 10 years to now.

02:05:22 I’ve just, that’s my game plan.

02:05:24 Before I forget, I have to ask you one last thing

02:05:26 on Instagram.

02:05:28 What do you think about the whistleblower,

02:05:30 Frances Haugen, recently coming out

02:05:33 and saying that Facebook is aware of Instagram’s

02:05:36 harmful effect on teenage girls

02:05:39 as per their own internal research studies on the matter?

02:05:43 What do you think about this baby of yours,

02:05:46 Instagram being under fire now,

02:05:48 as we’ve been talking about under the leadership of Facebook?

02:05:54 You know, I often question, where does the blame lie?

02:05:59 Is the blame at the people that originated the network me?

02:06:06 Is the blame at like the decision to combine the network

02:06:10 with another network with a certain set of values?

02:06:15 Is the blame at how it gets run after I left?

02:06:20 Like, is it the driver or is it the car, right?

02:06:25 Is it that someone enabled these devices in the first place?

02:06:29 If you go to an extreme, right?

02:06:31 Or is it the users themselves, just human nature?

02:06:35 Is it just the way of human nature?

02:06:37 Sure, and like the idea that we’re gonna find

02:06:39 a mutually exclusive answer here is crazy.

02:06:42 There’s not one place that’s a combination

02:06:43 of a lot of these things.

02:06:45 And then the question is like, is it true at all, right?

02:06:48 Like I’m not actually saying that’s not true

02:06:50 or that it’s true, but there’s always more nuance here.

02:06:55 Do I believe that social media has an effect

02:06:59 on young people?

02:07:00 Well, it’s got it, they use it a lot.

02:07:02 And I bet you there are a lot of positive effects

02:07:04 and I bet you there are negative effects,

02:07:05 just like any technology.

02:07:07 And where I’ve come to in my thinking on this

02:07:10 is that I think any technology has negative side effects.

02:07:13 The question is, as a leader, what do you do about them?

02:07:16 And are you actively working on them

02:07:18 or do you just like not really believe in them?

02:07:20 If you’re a leader that sits there and says,

02:07:22 well, we’re gonna put an enormous amount

02:07:24 of resources against this.

02:07:26 We’re gonna acknowledge when there are true criticisms,

02:07:29 we’re gonna be vulnerable and that we’re not perfect

02:07:32 and we’re gonna go fix them

02:07:33 and we’re gonna be held accountable along the way.

02:07:37 I think that people generally really respect that.

02:07:41 But I think that where Facebook I think has had issues

02:07:44 in the past is where they say things like,

02:07:46 can’t remember what Mark said about misinformation

02:07:49 during the election.

02:07:50 There was that like famous quote where he was like,

02:07:52 it’s pretty crazy to think that Facebook had anything

02:07:54 to do with this election.

02:07:55 Like that was something like that quote.

02:07:57 And I don’t remember what stage he was on and yeah.

02:08:00 But ooh, that did not age well, right?

02:08:02 Like you have to be willing to say,

02:08:05 well, maybe there’s something there and wow,

02:08:09 like I wanna go look into it

02:08:11 and truly believe it in your gut.

02:08:12 But if people look at you and how you act

02:08:14 and what you say and don’t believe you truly feel that way.

02:08:18 It’s not just the words you say, but how you say them

02:08:20 and that people believe they actually feel the pain

02:08:23 of having caused any suffering in the world.

02:08:25 So to me, it’s much more about your actions

02:08:29 and your posture post event

02:08:31 than it is about debugging the why.

02:08:33 Cause I don’t know, is it like, I don’t know this research.

02:08:36 It was written well after I left, right?

02:08:38 Like, is it the algorithm?

02:08:41 Is it the explore page?

02:08:42 Is it the people you might know unit connecting you

02:08:45 to ideas that are dangerous?

02:08:47 Like I really don’t know.

02:08:51 So we’d have to have a much deeper,

02:08:53 I think dive to understand where the blame lies.

02:08:56 What’s very unpleasant to me to consider,

02:08:58 now, I don’t know if this is true,

02:09:00 but to consider the very fact that there might be

02:09:03 some complicated games being played here.

02:09:07 For example, as somebody, I really love psychology

02:09:10 and I love it enough to know that the field

02:09:13 is pretty broken in the following way.

02:09:15 It’s very difficult to study human beings well at scale

02:09:19 because the questions you ask affect the results.

02:09:22 You can basically get any results you want.

02:09:25 And so you have an internal Facebook study

02:09:27 that asks some question of which we don’t know

02:09:29 the full details and there’s some kind of analysis,

02:09:32 but that’s just the one little tiny slice

02:09:34 into some much bigger picture.

02:09:37 And so you can have thousands of employees at Facebook.

02:09:40 One of them comes out and picks whatever narrative,

02:09:44 knowing that they become famous,

02:09:46 coupled with the other really uncomfortable thing

02:09:49 I see in the world, which is journalists seem to understand

02:09:53 they get a lot of clickbait attention

02:09:55 from saying something negative about social networks.

02:09:58 Certain companies, like they even get some clickbait stuff

02:10:03 about Tesla or about, especially when it’s like,

02:10:07 when there’s a public famous CEO type of person,

02:10:11 if they get a lot of views on the negative, not the positive,

02:10:14 the positive, they’ll get, I mean,

02:10:16 it actually goes to the thing you were saying before,

02:10:18 if there’s a hot, sexy new product,

02:10:20 that’s great to look forward to, they get positive on that,

02:10:23 but absent a product, it’s nice to have like the CEO

02:10:28 messing up in some kind of way.

02:10:30 And so couple that with the whistleblower

02:10:33 and with this whole dynamic of journalism and so on,

02:10:38 you know, with Social Dilemma being really popular,

02:10:41 documentary, it’s like, all right,

02:10:44 my concern is there’s deep flaws in human nature here

02:10:48 in terms of things we need to deal with,

02:10:51 like the nature of hate, of bullying,

02:10:54 all those kinds of things.

02:10:56 And then there’s people who are trying to use that

02:11:00 potentially to become famous and make money

02:11:02 off of blaming others for causing more of the problem

02:11:06 as opposed to helping solve the problem.

02:11:08 So I don’t know what to think.

02:11:10 I’m not saying this is like, I’m just uncomfortable

02:11:12 with, I guess, not knowing what to think about any of this

02:11:16 because a bunch of folks I know that work at Facebook

02:11:19 on the machine learning side, so Yann LeCun,

02:11:21 I mean, they’re quite upset about what’s happening

02:11:25 because there’s a lot of really brilliant,

02:11:26 good people inside Facebook that are trying to do good.

02:11:30 And so like all of this press, Yann is one of them,

02:11:33 and he has an amazing team

02:11:34 with the machine learning researchers.

02:11:35 Like he’s really upset with the fact

02:11:38 that people don’t seem to understand

02:11:40 that the portrayal does not represent

02:11:43 the full nature of efforts that’s going on at Facebook.

02:11:46 So I don’t know what to think about that.

02:11:48 Well, you just, I think, very helpfully explained

02:11:52 the nuance of the situation

02:11:54 and why it’s so hard to understand.

02:11:56 But a couple of things.

02:11:57 One is I think I have been surprised

02:12:02 at the scale with which some product manager

02:12:11 can do an enormous amount of harm

02:12:14 to a very, very large company

02:12:17 by releasing a trove of documents.

02:12:19 Like I think I read a couple of them when they got published

02:12:21 and I haven’t even spent any time going deep.

02:12:24 Part of it’s like, I don’t really feel like reliving

02:12:26 a previous life, but wow.

02:12:30 Like talk about challenging the idea of open culture

02:12:34 and like what that does to Facebook internally.

02:12:37 If Facebook was built, like I remember like my office,

02:12:43 we had this like no visitors rule around my office

02:12:45 because we always had like confidential stuff up

02:12:47 on the walls and everyone was super angry

02:12:50 because they’re like, that goes against our culture

02:12:52 of transparency and like marks in the fish cube

02:12:54 or whatever they call it, the aquarium,

02:12:56 I think they called it, where like literally anyone could see

02:12:59 what he was doing at any point and I don’t know.

02:13:03 I mean, other companies like Apple have been quiet

02:13:06 slash locked down, Snapchat’s the same way for a reason.

02:13:10 And I don’t know what this does to transparency

02:13:14 on the inside of startups that value that.

02:13:16 I think that it’s a seminal moment and you can say,

02:13:20 well, you should have nothing to hide, right?

02:13:22 But to your point, you can pick out documents

02:13:24 that show anything, right?

02:13:27 But I don’t know.

02:13:28 What happens to transparency inside of startups

02:13:31 and the culture that startups or companies

02:13:35 in the future will grow, like the startup of the future

02:13:37 that becomes the next Facebook will be locked down

02:13:40 and what does that do, right?

02:13:42 So that’s part one.

02:13:44 Part two, like I don’t think that you could design

02:13:49 a more like a well orchestrated handful of events

02:13:54 from the like 16 minutes to releasing the documents

02:13:59 in the way that they were released at the right time.

02:14:02 That takes a lot of planning and partnership.

02:14:05 And it seems like she has a partner at some firm, right?

02:14:09 That probably helped a lot with this, but man,

02:14:12 at a personal level, if you’re her,

02:14:15 you’d have to really believe in what you are doing,

02:14:19 really believe in it because you are personally

02:14:22 putting your ass on the line, right?

02:14:25 Like you’ve got a very large company

02:14:29 that doesn’t like enemies, right?

02:14:33 It takes a lot of guts and I don’t love

02:14:37 these conspiracy theories about like,

02:14:39 oh, she’s being financed from some person or people.

02:14:42 Like I don’t love them because that’s

02:14:43 like the easy thing to say.

02:14:45 I think the Occam’s razor here is like someone thought

02:14:49 they were doing something wrong

02:14:51 and was like very, very courageous.

02:14:54 And I don’t know if courageous is the word,

02:14:57 but like, so without getting into like,

02:15:00 is she a martyr?

02:15:01 Is she courageous?

02:15:02 Is she right?

02:15:03 Like, let’s put that aside for a second.

02:15:05 Then there are the documents themselves.

02:15:07 They say what they say.

02:15:09 To your point, a lot of the things that like people

02:15:12 have been worried about already in the documents

02:15:15 or they’re already been said externally.

02:15:17 And I don’t know, I’m just like, I’m thankful

02:15:22 that I am focused on new things with my life.

02:15:25 Well, let me just say, I just think it’s a really

02:15:27 hard problem that probably Facebook and Twitter

02:15:30 are trying to solve.

02:15:32 I’m actually just fascinated by how hard this problem is.

02:15:35 There are fundamental issues at Facebook in tone

02:15:38 and in an approach of how product gets built

02:15:41 and the objective functions.

02:15:43 And since people, organizations are not people.

02:15:48 So yawn and fair, right?

02:15:50 Like there are a lot of really great people

02:15:51 who like literally just want to push

02:15:53 reinforcement learning forward.

02:15:55 They literally just want to teach a robot

02:15:56 to touch, feel, lift, right?

02:15:59 Like they’re not thinking about political misinformation,

02:16:02 right?

02:16:03 But there’s a strong connection between what funds

02:16:07 that research and an enormously profitable machine

02:16:10 that has trade offs.

02:16:13 And one cannot separate the two.

02:16:18 You are not completely separate from the system.

02:16:21 So I agree, it can feel really frustrating

02:16:24 to feel if you’re internal there,

02:16:27 that you’re working on something completely unrelated

02:16:29 and you feel like your group’s good.

02:16:31 I can understand that.

02:16:32 But there’s some responsibility still.

02:16:34 You have to acknowledge, it’s like the Ray Dalio thing.

02:16:36 You have to look in the mirror and see if there’s problems

02:16:38 and you have to fix those problems.

02:16:40 Yeah.

02:16:43 You mentioned machine learning reinforcement quite a bit.

02:16:46 I mean, to me, social networks is one of the exciting places,

02:16:50 recommender systems where machine learning is applied.

02:16:52 Where else in the world, in the space of possibilities

02:16:56 over the next five, 10, 20 years,

02:16:58 do you think we’re going to see impact of machine learning

02:17:02 when you try, on the philosophical level,

02:17:05 on a technical level, what do you think?

02:17:07 Or within social networks themselves?

02:17:11 Well, I think the obvious answers are climate change.

02:17:17 Think about how much fuel

02:17:19 or just waste there is in energy consumption today

02:17:24 because we don’t plan accordingly,

02:17:27 because we take the least efficient route or…

02:17:30 The logistics and stuff, the supply chain,

02:17:32 all that kind of stuff.

02:17:33 Yeah, I mean, listen, if we’re gonna fight climate change,

02:17:36 like one really way, one awesome way to do it

02:17:39 is figure out how to optimize how we operate as a species

02:17:43 and minimize the amount of energy we consume

02:17:47 to maximize whatever economic impact we wanna have.

02:17:51 Because right now those two are very much tied together.

02:17:53 And I don’t believe that that has to be the case.

02:17:56 There’s this really interesting, you’ve read it.

02:18:00 For people who are listening,

02:18:00 there’s this really interesting paper

02:18:02 on reinforcement learning

02:18:04 and energy consumption inside buildings.

02:18:06 It’s like one of the seminal ones, right?

02:18:08 But imagine that at massive scale.

02:18:10 That’s super interesting.

02:18:11 I mean, they’ve done resource planning for servers

02:18:15 for peak load using reinforcement learning.

02:18:17 I don’t know if that was at Google or somewhere else,

02:18:19 but like, okay, great, you do it for servers,

02:18:21 but what if you could do it for just capacity

02:18:24 and general energy capacity for cities

02:18:26 and planning for traffic?

02:18:27 And of course there’s all the self driving cars

02:18:30 and I don’t know, like I’m not gonna pontificate

02:18:34 on like crazy ideas using reinforcement learning

02:18:39 or machine learning.

02:18:40 It’s just so clear to me

02:18:41 that humans don’t think quickly enough.

02:18:43 So it’s interesting to think about machine learning

02:18:46 helping a little bit at scale.

02:18:49 So a little bit to a large number of people

02:18:52 that has a huge impact.

02:18:53 So if you optimize, say Google Maps, something like that,

02:18:57 trajectory planning or what a map quest first.

02:19:01 Getting here, I looked and it was like,

02:19:02 here’s the most energy efficient route.

02:19:04 And I was like, I’m gonna be late.

02:19:05 I need to take the fastest route.

02:19:07 As opposed to unrolling the map.

02:19:09 Yeah, yeah.

02:19:10 Like, and then that’s going to be very inefficient

02:19:12 no matter what.

02:19:13 I was definitely the other day,

02:19:14 like part of the Epsilon of Epsilon Greedy

02:19:17 with Waze where like I was sent on like a weird route

02:19:21 that I could tell they’re like,

02:19:22 we just need to collect data at this road.

02:19:24 Like we just, Kevin’s.

02:19:26 You were the ant they sent out for exploration.

02:19:28 Kevin’s definitely gonna be the guinea pig.

02:19:30 And great, now we have.

02:19:32 Did you at least feel pride?

02:19:34 Oh, going through it, I was like, oh, this is fun.

02:19:36 Like now they get data about this weird shortcut.

02:19:38 And actually I hit all the green lights networked.

02:19:40 I’m like, this is a problem.

02:19:41 Bad data.

02:19:42 Bad data, they’re just gonna imagine.

02:19:44 I could see you slowing down and stopping at a green light

02:19:47 just to give them the right kind of data.

02:19:50 But to answer your question,

02:19:51 like I feel like that was fairly unsatisfying

02:19:53 and it’s easy to say climate change.

02:19:55 But what I would say is at Instagram,

02:19:58 everything we applied machine learning to

02:20:02 got better for users and it got better for the company.

02:20:04 I saw the power.

02:20:06 I didn’t fully understand it as an executive.

02:20:08 And I think that’s actually one of the issues

02:20:10 that, and when I say understand,

02:20:12 I mean the mathematics of it.

02:20:14 Like I understand what it does.

02:20:15 I understand that it helps.

02:20:17 But there are a lot of executives now

02:20:20 that talk about it in the way

02:20:22 that they talk about the internet

02:20:23 or they talked about the internet like 10 years ago.

02:20:25 They’re like, we’re gonna build mobile.

02:20:27 And you’re like, what does that mean?

02:20:27 They’re like, we’re just gonna do mobile.

02:20:29 And you’re like, okay.

02:20:31 So my sense is the next generation of leaders

02:20:33 will have grown up having had classes

02:20:37 in reinforcement learning, supervised learning, whatever.

02:20:40 And they will be able to thoughtfully apply it

02:20:42 to their companies and the places that it is needed most.

02:20:46 And that’s really cool.

02:20:47 Cause I mean, talk about efficiency gains.

02:20:53 That’s what excites me the most about it.

02:20:54 Yeah, so there’s, it’s interesting just to get a fundamental

02:20:58 first principles understanding

02:20:59 of certain concepts of machine learning.

02:21:01 So supervised learning from an executive perspective,

02:21:04 supervised learning, you have to have a lot of humans

02:21:07 label a lot of data.

02:21:08 So the question there is, okay,

02:21:09 can we gather a large amount of data

02:21:12 that can be labeled well?

02:21:14 And that’s the question Tesla asked,

02:21:16 like can we create a data engine

02:21:17 that keeps sending an imperfect machine learning system

02:21:22 out there, whenever it fails, it gives us data back.

02:21:25 We label it by human and we send it back and forth

02:21:27 to this way.

02:21:28 Then there’s Yann LeCun’s excited

02:21:30 about the self supervised learning

02:21:32 where you do much less human labeling

02:21:36 and there’s some kind of mechanism for the system

02:21:38 to learn it by itself on the human generated data.

02:21:42 And then there’s the reinforcement learning,

02:21:44 which is like basically allowing,

02:21:47 it’s applying the alpha zero technology

02:21:52 that allow through self play to learn how to solve

02:21:56 the game of Go and achieve incredible levels

02:21:59 at the game of chess.

02:22:02 Can you formulate the problem you’re trying to solve

02:22:05 in a way that’s amenable to reinforcement learning?

02:22:07 And can you get the right kind of signal at scale?

02:22:09 Cause you need a lot of signal.

02:22:11 And that’s kind of fascinating to see which part

02:22:15 of a social network can you convert

02:22:17 into reinforcement learning problem.

02:22:19 The fascinating thing about reinforcement learning,

02:22:22 I think, is that we now have learned

02:22:25 to apply neural networks to guess the Q function,

02:22:34 basically the values for any state in action.

02:22:37 And that is fascinating cause we used to just like,

02:22:40 I don’t know, have like a linear regression,

02:22:42 like hope it worked and that was the fanciest version of it.

02:22:45 But now you look at it, I’m like trying to learn this stuff

02:22:47 and I look at it and I’m like,

02:22:48 there are like 17 different acronyms of different ways

02:22:51 you can try to apply this.

02:22:52 No one quite agrees, like what’s the best.

02:22:56 Generally, if you’re trying to like build a neural network,

02:22:58 there are pretty well trodden ways of doing that.

02:23:02 You use Adam, you use ReLU,

02:23:04 like there’s just like general good ideas.

02:23:07 And in reinforcement learning,

02:23:08 I feel like the consensus is like, it totally depends.

02:23:13 And by the way, it’s really hard to get it to converge

02:23:16 and it’s noisy and it like,

02:23:18 so there are all these really interesting ideas

02:23:20 around building simulators.

02:23:23 You know, like for instance, in self driving, right?

02:23:25 Like you don’t want to like actually have someone

02:23:29 getting in an accident to learn that an accident is bad.

02:23:31 So you start simulating accidents,

02:23:33 simulating aggressive drivers,

02:23:35 just simulating crazy dogs that run into the street and,

02:23:39 wow, fascinating, right?

02:23:41 Like my mind starts racing and then the question is,

02:23:43 okay, forget about self driving cars.

02:23:45 Let’s talk about social networks.

02:23:49 How can you produce a better, more thoughtful experience

02:23:52 using these types of algorithms?

02:23:55 And honestly, in talking to some of the people

02:23:58 that work at Facebook and old Instagrammers,

02:24:01 most people are like, yeah, we tried a lot of things,

02:24:04 didn’t quite ever make it work.

02:24:05 I mean, for the longest time,

02:24:06 Facebook ads was effectively a logistic regression, okay?

02:24:10 I don’t know what it is now,

02:24:11 but like if you look at this paper

02:24:13 that they published back in the day,

02:24:14 it was literally just a logistic regression.

02:24:16 Made a lot of money.

02:24:18 So even at these like extremely large scales,

02:24:21 if we are not yet touching

02:24:23 what reinforcement learning can truly do,

02:24:25 imagine what the next 10 years looks like.

02:24:27 How cool is that?

02:24:28 It’s amazing.

02:24:29 So I really liked the use of reinforcement learning

02:24:32 as part of the simulation, for example,

02:24:34 like with self driving cars, it’s modeling pedestrians.

02:24:37 So the nice thing about reinforcement learning,

02:24:40 it can be used to learn agents within the world.

02:24:45 So they can learn to behave properly.

02:24:47 Like you can teach pedestrians to,

02:24:49 like you don’t hard code the way they behave,

02:24:51 they learn how to behave.

02:24:53 In that same way, I do have a hope,

02:24:55 was it Jack Dorsey talks about healthy conversations.

02:24:58 You talked about meaningful interactions, I believe.

02:25:03 Like simulating interactions.

02:25:06 So you can learn how to manage that, it’s fascinating.

02:25:09 So where most of your algorithm development happens

02:25:13 in virtual worlds, and then you can really learn

02:25:16 how to design the interface,

02:25:18 how you design a bunch of aspects of the experience

02:25:21 in terms of how you select what’s shown in the feed,

02:25:24 all those kinds of things.

02:25:26 It feels like if you can connect reinforcement learning

02:25:28 to that, that’s super exciting.

02:25:30 Yep, and I think if you have a company and leadership

02:25:35 that believe in doing the right things

02:25:36 and can apply this technology in the right way,

02:25:38 some really special stuff can happen.

02:25:41 It is mostly likely going to be a group of people

02:25:44 we’ve never heard about, start up from scratch, right?

02:25:49 And you asked if like new social networks could be built,

02:25:52 I’ve got to imagine they will be.

02:25:54 And whoever starts it, it might be some kids in a garage

02:25:58 that took these classes from these people, you, right?

02:26:02 And they’re building all of these things

02:26:04 with this tech at the core.

02:26:06 So I’m trying not to be someone who just like throws

02:26:08 around reinforcement learning as a buzzword.

02:26:11 I truly believe that it is the most cutting edge

02:26:16 in what can happen in social networks.

02:26:18 And I also believe it’s super hard.

02:26:20 Like it’s super hard to make it work.

02:26:22 It’s super hard to do it at scale.

02:26:24 It’s super hard to find people that truly understand it.

02:26:26 So I’m not gonna say that like,

02:26:30 I think it’ll be applied in social networks

02:26:31 before we have true self driving.

02:26:33 Yeah, we could argue about this for a long time,

02:26:36 but yes, I agree with you.

02:26:38 I think self driving is way harder than people realize.

02:26:40 Oh, absolutely.

02:26:41 Let me ask you in terms of that kid in the garage

02:26:44 or those couple of kids in the garage,

02:26:45 what advice would you give to them

02:26:47 if they wanna start a new social network or a business?

02:26:50 What advice would you give to somebody

02:26:52 with a big dream and a young startup?

02:26:56 To me, you have to choose to do something

02:26:59 that even if it fails, like it was so fun, right?

02:27:03 Like we never started Instagram knowing

02:27:06 it was going to be big.

02:27:07 We started Instagram because we loved photography.

02:27:10 We loved social networks.

02:27:12 I had seen what other social networks had done

02:27:14 and I thought, hmm, maybe we did a spin on this,

02:27:17 but like nowhere was our feet predestined.

02:27:20 Like it wasn’t like, it wasn’t written out anywhere

02:27:23 that everything was gonna go great.

02:27:25 And I often think the counterfactual,

02:27:27 like what if it had not gone well?

02:27:28 I would have been like, I don’t know, that was fun.

02:27:30 We raised some money, we learned some stuff

02:27:32 and does it position you well for the next experience?

02:27:37 That’s the advice that I would give

02:27:39 to anyone wanting to start something today,

02:27:41 which is like, does this meet with your ultimate goals?

02:27:45 Not wealth, not fame, none of that,

02:27:47 because all of that, by the way, is bullshit.

02:27:48 Like you can get super famous and super wealthy.

02:27:52 And I think generally those are not things that,

02:27:57 again, it’s easy to say with like a lot of money

02:27:59 that somehow like it’s not good to have a lot of money.

02:28:01 It’s just, I think that complicates life enormously

02:28:04 in a way that people don’t fully comprehend.

02:28:06 So I think it is way more interesting to shoot for,

02:28:09 can I make something that people love,

02:28:11 that provides value in the world,

02:28:13 that I love building, that I love working on, right?

02:28:17 That’s what I would do if I were starting from scratch.

02:28:21 And by the way, like in some ways

02:28:22 that I will do that personally,

02:28:25 which is like choose the thing

02:28:26 that you get up every morning and you’re like,

02:28:27 I love this, even when it’s painful.

02:28:33 Even when it’s painful.

02:28:34 What about a social network specifically?

02:28:36 If you were to imagine, put yourself in the mind of some.

02:28:40 I can’t compete against myself.

02:28:42 I can’t give out ideas.

02:28:43 Okay, I got you.

02:28:44 No, but it’s like high level.

02:28:45 You can focus on community.

02:28:47 Yeah.

02:28:50 I said that as a half joke.

02:28:54 In all honesty, I think these things are so hard to build

02:28:56 that like ideas are a dime a dozen, but.

02:28:59 You have talked about keeping it simple.

02:29:02 Can I tell you?

02:29:03 Which is a liberating idea.

02:29:04 My model is it’s three circles and they overlap.

02:29:08 One circle is what do I have experience at?

02:29:11 Slash, what am I good at?

02:29:12 I don’t like saying what am I good at

02:29:14 because it just like seems like,

02:29:16 what do I have experience in, right?

02:29:18 What can I bring to the table?

02:29:19 What am I excited about is the other circle.

02:29:22 What gets it?

02:29:22 What’s just super cool, right?

02:29:24 That I want to work on because even when this is hard,

02:29:29 I think it’s so cool.

02:29:30 I want to stick with it.

02:29:31 And the last circle is like, what does the world need?

02:29:34 And if that circle ain’t there,

02:29:36 it doesn’t matter what you work on.

02:29:37 Cause there are a lot of startups that exist

02:29:39 that just no one needs or very small markets need.

02:29:43 But if you want to be successful,

02:29:44 I think if you’re like, if you’re good at it,

02:29:47 you have, sorry, if you’re good at it,

02:29:49 you’re passionate about it and the world needs it.

02:29:51 I mean, this sounds simple,

02:29:53 not enough people sit down and just think

02:29:54 about those circles and think, do these things overlap?

02:29:58 And then can I get that middle section?

02:29:59 It’s small, but can I get that middle section?

02:30:02 I think a lot about that personally.

02:30:05 And then you have to be really honest about the circle

02:30:09 that you’re good at and really honest about the circle

02:30:14 that the world needs.

02:30:17 And as opposed to really honest about the passion,

02:30:20 like what do you actually love?

02:30:22 As opposed to like some kind of dream of making money,

02:30:24 all those kinds of stuff, like literally love doing.

02:30:26 I had a former engineer who decided to start a startup

02:30:29 and I was like, are you sure you want to start a company

02:30:32 versus like join something else?

02:30:34 Because being a coach of an NBA team and playing basketball

02:30:39 are two very, very different things.

02:30:42 And like not everyone fully understands the difference.

02:30:45 I think you can kind of do it both.

02:30:50 And I don’t know, jury’s out on that one

02:30:51 because like they’re in the middle of it now.

02:30:54 But it’s really important to figure out

02:30:57 what you’re good at, not be full of yourself,

02:30:59 like truly look at your track record.

02:31:03 What’s the saying like, it ain’t bragging if you can do it.

02:31:09 But too many people are delusional

02:31:12 and like think they’re better at things

02:31:14 than they actually are,

02:31:15 or think there’s a bigger market than there actually is.

02:31:18 When you confuse your passion for things with a big market,

02:31:21 that’s really scary, right?

02:31:23 Like just because you think it’s cool

02:31:25 doesn’t mean that it’s a big business opportunity.

02:31:27 So like, what evidence do you have?

02:31:28 Again, I’m a fairly like, I’m a strict rationalist on this.

02:31:32 And like sometimes people don’t like working with me

02:31:35 because I’m pretty pragmatic about things.

02:31:37 Like I’m not Elon, like I don’t sit

02:31:40 and make bold proclamations about visiting Mars.

02:31:44 Like that’s just not how I work.

02:31:46 I’m like, okay, I want to build this really cool thing

02:31:48 that’s fairly practical and I think we could do it.

02:31:50 And it’s in this way.

02:31:52 And what’s cool though is like, that’s just my sweet spot.

02:31:55 I’m not like, I just, I can’t with a straight face

02:31:58 talk about the metaphors.

02:31:59 I can’t, I just, it’s not me.

02:32:01 What do you think about the Facebook renaming itself to?

02:32:05 I didn’t mean that as a dig.

02:32:06 I just literally mean like, I’m fairly,

02:32:09 I like to live in the next five years.

02:32:11 And like, what things can I get out in a year

02:32:13 that people will use at scale?

02:32:15 And so it’s just, again, those circles I think are different

02:32:20 for different people, but it’s important to realize

02:32:22 that like market matters, you being good at it matters

02:32:26 and having passion for it matters.

02:32:27 Your question, sorry.

02:32:29 Well, on that last, on this topic in terms of funding,

02:32:33 is there, by way of advice,

02:32:39 was funding in your own journey helpful, unhelpful?

02:32:44 Like is there a right time to get funding, venture funding

02:32:48 or anything, borrow some money from your parents?

02:32:51 I don’t know.

02:32:51 Like is money getting in the way?

02:32:54 Does it help?

02:32:56 Is the timing important?

02:32:57 Is there some kind of wisdom you can give there

02:33:00 because you were exceptionally successful very quickly?

02:33:06 Funding helps as long as it’s from the right people.

02:33:09 That includes yourself.

02:33:10 And I’ll talk about myself funding myself in a second,

02:33:13 which is like, because I can fund myself

02:33:15 doing whatever projects I can do,

02:33:18 I don’t really have another person putting pressure on me

02:33:20 except for myself and that creates strange dynamics, right?

02:33:24 But let’s like talk about people getting funding

02:33:27 from a venture capitalist initially.

02:33:30 We raised money from Matt Kohler at Benchmark.

02:33:32 He’s brilliant, amazing guy, very thoughtful.

02:33:36 And he was very helpful early on.

02:33:39 But I have stories from entrepreneurs

02:33:41 where they raised money from the wrong person

02:33:42 or the wrong firm where incentives weren’t aligned.

02:33:46 They didn’t think in the same way

02:33:48 and bad things happened because of that.

02:33:51 The boardroom was always noisy.

02:33:53 There were fights, like we just never had that.

02:33:55 Matt was great.

02:33:57 I think like capital these days

02:33:59 is kind of a dime a dozen, right?

02:34:01 Like as long as you’re fundable,

02:34:03 like it seems like there’s money out there

02:34:05 is what I’m hearing.

02:34:08 It’s really important that you are aligned

02:34:10 and that you think of raising money

02:34:12 as hiring someone for your team rather than taking money

02:34:15 if capital is plentiful, right?

02:34:18 It provides a certain amount of pressure

02:34:20 to do the right thing that I think is healthy

02:34:23 for any startup.

02:34:24 And it keeps you real and honest

02:34:25 because they don’t wanna lose their money.

02:34:27 They’re paid to not lose their money.

02:34:29 The problem, maybe I could depersonalize it,

02:34:32 but like I remember having lunch with Elon.

02:34:35 It’s only happened once.

02:34:37 And I asked him, like I was trying to figure out

02:34:39 what I was doing after Instagram, right?

02:34:42 And I asked him something about like angel investing.

02:34:44 And he looked at me with a straight face.

02:34:46 He was like, why the F would I do that?

02:34:48 Like, why?

02:34:48 I was like, I don’t know.

02:34:50 Like you’re connected.

02:34:51 Like seems like he’s like, I only invest in myself.

02:34:54 I was like, Ooh, okay.

02:34:56 You know, like not the confidence.

02:34:59 I was just like, what a novel idea.

02:35:00 It’s like, yeah, if you have money,

02:35:03 like why not just put it against your bag

02:35:06 and like enable you’re visiting Mars or something, right?

02:35:11 Like that’s awesome, great.

02:35:12 But I had never really thought of it that way.

02:35:14 But also with that comes an interesting dynamic

02:35:17 where you don’t actually have people

02:35:22 who are gonna lose that money telling you,

02:35:23 hey, don’t do this or, hey, you need to face this reality.

02:35:27 So you need to create other versions of that truth teller.

02:35:32 And whatever I do next,

02:35:34 that’s gonna be one of the interesting challenges

02:35:36 is how do you create that truth telling situation?

02:35:40 And that’s part of why, by the way,

02:35:41 I think someone like Jack, when you start Square,

02:35:43 you have money, but you still, you bring on partners

02:35:46 because I think it creates

02:35:48 a truth telling type environment.

02:35:51 I’m still trying to figure this out.

02:35:52 Like it’s an interesting dynamic.

02:35:56 So you’re thinking of perhaps launching some kind of venture

02:35:59 where you’re investing in yourself?

02:36:01 I mean, I’m 37 going on 38 next month.

02:36:06 I have a long life to live.

02:36:07 I’m definitely not gonna sit on the beach, right?

02:36:10 So I’m gonna do something at some point

02:36:13 and I gotta imagine I will like help fund it, right?

02:36:18 So the other way of thinking about this

02:36:20 is you can park your money in the SMP,

02:36:21 and this is bad

02:36:22 because the SMP has done wonderfully well last year, right?

02:36:26 Or you can invest in yourself.

02:36:27 And if you’re not gonna invest in yourself,

02:36:30 you probably shouldn’t do a startup.

02:36:32 It’s kind of the way of thinking about it.

02:36:35 And you can invest in yourself in the way Elon does,

02:36:37 which is basically go all in on this investment.

02:36:41 Maybe that’s one way to achieve accountability

02:36:43 is like you’re kind of screwed if you fail.

02:36:46 Yeah, that’s, yeah.

02:36:49 I personally like that.

02:36:50 I like burning bridges behind me

02:36:52 so that I’m fucked if it fails.

02:36:57 It’s really important though.

02:37:00 One of the things I think Mark said to me early on

02:37:03 that sticks with me that I think is true.

02:37:06 We were talking about people

02:37:07 who had left like operating roles

02:37:10 and started doing venture or something.

02:37:11 He was like, a lot of people convince themselves

02:37:13 they work really hard.

02:37:14 Like they think they work really hard

02:37:15 and they put on the show

02:37:17 and in their minds they work really hard,

02:37:19 but they don’t work very hard.

02:37:22 There is something about lighting a fire underneath you

02:37:24 and burning bridges such that you can’t turn back.

02:37:28 That I think, we didn’t talk about this specifically,

02:37:31 but I think you’re right.

02:37:32 There is, you need to have that

02:37:34 because there’s the self delusion at a certain scale.

02:37:39 Oh, I have so many board calls.

02:37:40 Oh, like we have all these things to figure out.

02:37:43 It’s like, this is one of the hard parts

02:37:45 about it being an operator.

02:37:47 It’s like, there are so many people

02:37:50 that have made a lot of money not operating,

02:37:52 but operating is just one of the hardest things on earth.

02:37:55 It is just so effing hard.

02:37:58 It is stressful.

02:37:59 It is, you’re dealing with real humans,

02:38:01 not just like throwing capital in and hoping it grows.

02:38:04 I’m not undermining the VC mindset.

02:38:06 I think it’s a wonderful thing and needed

02:38:08 and so many wonderful VCs I’ve worked with.

02:38:11 But yeah, like when your ass is on the line

02:38:14 and it’s your money, it’s…

02:38:18 Talk to me in 10 years, we’ll see how it goes.

02:38:21 Yeah, but like you were saying, that is a source.

02:38:23 When you wake up in the morning

02:38:24 and you look forward to the day full of challenges,

02:38:28 that’s also where you can find happiness.

02:38:31 Let me ask you about love and friendship.

02:38:32 Sure.

02:38:33 What’s the role in this heck of a difficult journey

02:38:36 you have been on of love, of friendship?

02:38:41 What’s the role of love in the human condition?

02:38:45 Well, first things first,

02:38:47 the woman I married, my wife, Nicole,

02:38:49 there’s no way I could do what I do if we weren’t together.

02:38:53 She had the filter idea.

02:38:55 Yeah, yeah, exactly.

02:38:56 We didn’t go over that story.

02:38:59 Everything is a partnership, right?

02:39:01 And to achieve great things,

02:39:03 it’s not about like someone pulling their weight in places.

02:39:06 Like it’s not like someone’s supporting you

02:39:08 so that you could do this other thing.

02:39:11 It’s literally like,

02:39:14 Mike and I and our partnership as cofounders is fascinating

02:39:18 because I don’t think Instagram would have happened

02:39:20 without that partnership.

02:39:21 Like either him or me alone, no way.

02:39:25 We pushed and pulled each other in a way

02:39:28 that allowed us to build a better thing because of it.

02:39:32 Nicole, she pushed me to work on the filters early on.

02:39:35 And yes, that’s exciting.

02:39:36 It’s a fun story, right?

02:39:38 But the truth of it is being able to like level

02:39:41 with someone about how hard the process is

02:39:44 and have someone see you for who you are before Instagram

02:39:49 and know that there’s a constant you throughout all of this

02:39:53 and be able to call you when you’re drifting from that,

02:39:55 but also support you when you’re trying to stick with that.

02:39:58 That’s, I mean, that’s true friendship slash love,

02:40:02 whatever you want to call it.

02:40:04 But also it was for someone not to care.

02:40:07 I remember Nicole saying,

02:40:08 hey, like I know you’re going to do this Instagram thing.

02:40:10 You should, I guess it was bourbon at the time.

02:40:12 You should do it because, you know,

02:40:15 even if it doesn’t work,

02:40:16 we can move to like a smaller apartment and it’ll be fine.

02:40:20 Like we’ll make it work.

02:40:22 How beautiful is that, right?

02:40:24 That’s almost like a superpower

02:40:25 that gives you permission to fail.

02:40:27 And somehow that actually leads to success.

02:40:29 But also she’s like the least impressed

02:40:31 about Instagram of anyone.

02:40:33 She’s like, yeah, it’s great.

02:40:34 But like, I love you for you.

02:40:36 Like, I like that you’re like a decent cook.

02:40:38 That’s beautiful.

02:40:39 That’s beautiful with the Gantt chart and Thanksgiving,

02:40:42 which I still think is a brilliant effing idea.

02:40:44 Thank you.

02:40:46 Big, ridiculous question.

02:40:48 Have you, you’re old and wise at this stage.

02:40:53 So have you discovered meaning to this whole thing?

02:40:55 Why the hell are we descendants of apes here on earth?

02:40:59 What’s the meaning of it?

02:41:00 What’s the meaning of life?

02:41:01 I haven’t.

02:41:02 And I am, so the crazy,

02:41:06 so the best learning for me has been like,

02:41:09 no matter what level of success you achieve,

02:41:12 you’re still worried about similar things,

02:41:14 just maybe on a slightly different scale.

02:41:16 You’re still concerned about the same thing.

02:41:18 You’re still self conscious about the same things.

02:41:21 Just like, and actually that moment going through that

02:41:26 is what makes you believe there’s gotta be like

02:41:29 more machinery to life or purpose to life.

02:41:31 And that we’re all chasing these materialistic things,

02:41:35 but you start realizing like,

02:41:38 it’s almost like, you know, the Truman Show

02:41:39 when he gets to the edge and he like knocks against it.

02:41:42 He’s like, what?

02:41:43 Like there’s this awakening that happens

02:41:45 when you get to that edge that you realize,

02:41:47 oh, like sure, it’s great.

02:41:49 It’s great that we all chase money and fame and success.

02:41:53 But you hit the edge and I’m not even claiming

02:41:56 I hit an edge like Elon’s hit an edge.

02:41:58 Like there’s clearly larger scales.

02:42:00 But what’s cool is you learn that,

02:42:02 like it doesn’t actually matter

02:42:03 and that there are all these other things that truly matter.

02:42:07 That’s not a case for working less hard.

02:42:09 That’s not a case for taking it easy.

02:42:11 That’s not a case for the four day work week.

02:42:13 What that is a case for is designing your life

02:42:16 exactly the way you want to design it.

02:42:18 Cause I don’t know, I think we go around the earth,

02:42:22 you know, the sun a certain number of times

02:42:25 and then we die and then that’s it.

02:42:27 That’s me.

02:42:28 Are you afraid of that moment?

02:42:30 No, not at all.

02:42:31 In fact, or at least not yet.

02:42:36 Listen, I’m like a pilot, like I do crazy things

02:42:39 and I like, no, I like, if anything, I’m like,

02:42:43 oh, I got to choose mindfully and purposefully

02:42:49 the thing I am doing right now and not just fall into it

02:42:54 because you’re going to wake up one day and ask yourself

02:42:55 why the hell you spent the last 10 years doing X, Y or Z?

02:42:58 Yeah.

02:42:59 So I guess my like shorter answer to this is

02:43:03 doing things on purpose because you choose to do them.

02:43:08 So important in life and not just like floating

02:43:11 down the river of life, hitting branches along the way

02:43:14 cause you will hit branches, right?

02:43:17 But rather like literally plotting a course

02:43:19 and not having a 10 year plan,

02:43:21 but just choosing every day to opt in.

02:43:23 That I think has been more like,

02:43:28 I haven’t figured out the meaning of life

02:43:29 by any stretch of the imagination,

02:43:31 but it certainly isn’t money and it certainly isn’t fame

02:43:33 and it certainly isn’t travel.

02:43:35 And it’s like, and it’s way more of like opting

02:43:37 into the game you love playing.

02:43:40 Every day opting in.

02:43:41 Just opting in and like, don’t let it happen.

02:43:44 You opt in.

02:43:46 Kevin, it’s great to end on love and the meaning of life.

02:43:51 This was an amazing conversation.

02:43:53 It was a lot of fun, thank you.

02:43:54 You gave me like a light into some fascinating aspects

02:43:57 of this technical world.

02:44:00 And I can’t honestly wait to see what you do next.

02:44:04 Thank you so much.

02:44:05 Thanks for having me.

02:44:07 Thanks for listening to this conversation

02:44:09 with Kevin Systrom.

02:44:10 To support this podcast, please check out our sponsors

02:44:13 in the description.

02:44:14 And now let me leave you with some words

02:44:16 from Kevin Systrom himself.

02:44:19 Focusing on one thing and doing it really, really well

02:44:24 can get you very far.

02:44:25 Thank you for listening and hope to see you next time.