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EPISODE LINKS:
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OUTLINE:
0:00 - Introduction
0:22 - Origin of Instagram
42:29 - New social networks
1:01:34 - Selling Instagram to Facebook
1:24:36 - Features
1:28:42 - Facebook
2:05:24 - Whistleblower
2:16:43 - Machine learning
2:26:41 - Advice for startups
2:32:33 - Money
2:38:33 - Love
2:40:50 - Meaning of life

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The following is a conversation with Kevin Systrom, co-founder and longtime CEO of Instagram, including for 6 years after Facebook's acquisition of Instagram.

To support it, please check out our sponsors in the description.

And now, here's my conversation with Kevin Systrom.

At the risk of asking the Rolling Stones to play Satisfaction, let me ask you about the origin story of Instagram.

So maybe some context, you, like we were talking about offline, grew up in Massachusetts, learned computer programming there, liked to play Doom II, worked at a vinyl record store, then you went to Stanford, turned down Mr.

Mark Zuckerberg in Facebook, went to Florence to study photography.

Those are just some random, beautiful, impossibly brief glimpses into a life.

So let me ask again, can you take me through the origin story of

All right, So we have a fair amount of time, so I'll go into some detail.

But basically what I'll say is Instagram started out of a company actually called Bourbon and it was spelled B-U-R-B-N.

And a couple of things were happening at the time.

So if we zoom back to 2010, not a lot of people remember what was happening in the dot-com world then, but check-in apps were all the rage.

I'm gonna tell the world that I'm at this place.

What's the idea behind this kind of app, by the way?

You know what, I'm gonna answer that, but through what Instagram became and why I believe Instagram replaced them.

So the whole idea was to share with the world what you were doing specifically with your friends, right?

But there were all the rage and Foursquare was getting all the press.

And I remember sitting around saying, hey, I wanna build something but I don't know what I wanna build.

What if I built a better version of Foursquare?

And I asked myself, well, why don't I like Foursquare?

And basically I sat down and I said, I think that if you have a few extra features, it might be enough.

1 of which happened to be posting a photo of where you were.

My co-founder joined, we were going to attack Foursquare and the likes and try to build something interesting.

So 1 day we were sitting down and we asked ourselves, okay, it's come to Jesus moment, are we gonna do this startup?

And if we're going to, we can't do what we're currently doing, we have to switch it up.

So we sat down and we wrote out 3 things that we thought people uniquely loved about our product that weren't in other products.

So sharing a photo of what you were doing, where you were at the moment was not something products let you do, really.

Facebook was like, post an album of your vacation from 2 weeks ago, right?

Twitter allowed you to post a photo, but their feed was primarily text and they didn't show the photo in line, or at least I don't think they did at the time.

So even though it seems totally stupid and obvious to us now, at the moment, then posting a photo of what you were doing at the moment was like not a thing.

So we decided to go after that because we noticed that people who used our service, the 1 thing they happened to like the most was posting a photo.

And yes, like we went through and we added filters, and there's a bunch of stories around that.

But the origin of this was that we were trying to be a check-in app, realized that no 1 wanted another check-in app.

It became a photo sharing app, but 1 that was much more about what you're doing and where you are.

And that's why when I say I think we've replaced check-in apps, it became a check-in via a photo rather than saying your location and then optionally adding a photo.

When you were thinking about what people like, from where did you get a sense that this is what people like?

You said, we sat down, we wrote some stuff down on paper.

Where is that intuition that seems fundamental to the success of an app like Instagram?

Where does that idea, where does that list of 3 things come from exactly?

Only after having studied machine learning now for a couple of years, I like, I have a...

I've started to make connections, like, we can go into this later, but obviously the connections between machine learning and the human brain, I think, are stretched sometimes.

At the same time, being able to back prop and being able to look at the world, try something, figure out how you're wrong, how wrong you are, and then nudge your company in the right direction based on how wrong you are.

We didn't know we were doing it at the time, but that's basically what we were doing, right?

We put it out to call it a hundred people and you would look at their data.

We think they're going to resonate with X, but it turns out they resonate with Y.

And it turns out if you do that enough quickly enough, you can get to a solution that has product market fit.

Most companies fail because they sit there and they don't either their learning rates too slow.

They sit there and they're just adamant that they're right, even though the data is telling them they're not right.

Or they're learning rates too high and they wildly chase different ideas, and they never actually set out on 1 where they don't groove.

And I think when we sat down and we wrote up those 3 ideas, what we were saying is, what are the 3 possible, whether they're local or global maxima in our world, right?

That users are telling us they like, because they're using the product that way.

It was clear people liked the photos because that was the thing they were doing.

And we just said, okay, like, what if we just cut out most of the other stuff and focus on that thing?

And then it happened to be a multi-billion dollar business.

Well, nobody ever writes about neural networks that miserably failed.

So this particular neural network succeeded.

When you said the way people are using the app, is that the loss function for this neural network or is it also self-report?

Like, do you ever ask people what they like or do you have to track exactly what they're doing, not what they're saying?

And it was for relatives and I like to cook a lot.

And I worked really hard on picking the specific dishes and I was really proud because I had planned it out using a Gantt chart and like it was ready on time and everything was hot.

Like I don't know if you're a big Thanksgiving guy, but like the worst thing about Thanksgiving is when the turkey is cold and some things are hot and something.

Fairly expensive, like Gantt chart thing that I think maybe 10 people have purchased in the world.

But I'm 1 of them and I use it for recipe planning, only around big holidays.

Do people do this kind of- Over engineering.

It's not over, it's just engineering, it's planning.

Thanksgiving is a complicated set of events with some uncertainty with a lot of things going on.

You should be able, you should be planning in this way.

And I've spent some time with some chefs who like know their stuff.

And they always just take out a piece of paper and just work backwards in rough order.

Like it's never perfect, but rough order.

Why not just work backwards from the end goal, right?

And so I probably over specified it a bit using a Gantt chart But the fact that you can do it, it's what professional kitchens roughly do.

They just don't call it a Gantt chart, or at least I don't think they do.

Anyway, I was telling a story about Thanksgiving.

I'm sitting down, we have the meal, and then I got to know Ray Dalio fairly well over maybe the last year of Instagram.

And 1 thing that he kept saying was like, feedback is really hard to get honestly from people.

And I sat down at after dinner, I said, guys, I want feedback.

And what's funny is like, literally, everyone just said everything was great.

And I like personally knew I had screwed up a handful of things.

And can you imagine now not something as high stakes as Thanksgiving dinner?

It's not that high stakes, but you're trying to build a product and everyone knows you left your job for it and you're trying to build it out and you're trying to make something wonderful and it's yours, right?

Now try asking for feedback and know that you're giving this to your friends and your family.

People have trouble giving hard feedback.

People have trouble saying, I don't like this or this isn't great, or this is how it's failed me.

In fact, you usually have 2 classes of people, people who just won't say bad things, you can literally say to them, Please tell me what you hate most about this and they won't do it.

And then the other class of people are just negative, period, about everything.

And it's hard to parse out what is true and what isn't.

So my rule of thumb with this is you should always ask people, but at the end of the day, it's amazing what data will tell you.

And that's why with whatever project I work on, even now, collecting data from the beginning on usage patterns, So engagement, how many days of the week do they use it?

How many, I don't know, if we were to go back to Instagram, how many impressions per day, right?

And don't be like overly scientific about it, right?

Because Maybe you have 50 beta users or something.

But what's fascinating is that data doesn't lie.

People are very defensive about their time.

I'm just, you know, but I don't know you're posting on Instagram the whole time.

So I don't know at the end of the day, like at Facebook, there was, you know, before time spent became kind of this loaded term there.

The idea that people people's currency in their lives is time, and they only have a certain amount of time to give things, whether it's friends or family or apps or TV shows or whatever.

There's no way of inventing more of it, at least not that I know of.

If they don't use it, it's because it's not great.

So the moral of the story is you can ask all you want but you just have to look at the data and data doesn't lie, right?

I mean, there's metrics, there's data can obscure the key insight if you're not careful.

There's so many metrics you can put at this and they will give you totally different insights, especially when you're trying to create something that doesn't obviously exist yet.

So, you know, measuring maybe why you left the app or measuring special moments of happiness that will make sure you return to the app, or moments of happiness that are long lasting versus like dopamine short-term, all of those things.

But I think, I suppose in the beginning, you can just get away with just asking the question, which features are used a lot?

And I mean, maybe you can tell me what Instagram looked in the beginning, but how hard was it to make pictures the first class citizen?

Like, at whatever point Instagram became this feed of photos, that's quite brilliant.

Plus, also don't know when this happened, but they're all shaped the same.

Like imagine trying to fly a plane by staring at, I don't know, a single metric like airspeed.

You don't know if you're going up or down.

I mean, it correlates with up or down, but you don't actually know.

Like it turns out you have to synthesize a bunch of metrics to know where to go.

Like if your airspeed is 0, unless it's not working, right?

If it's 0, you're probably gonna fall out of the sky.

So generally you look around and you have the scan going.

And you're just asking yourself, is this working or is this not working?

But people have trouble explaining how they actually feel.

So just, it's about synthesizing both of them.

We were talking about revolutionary moment where the feed became square photos basically.

It was clear to me that the biggest, so I believe the biggest companies are founded when enormous technical shifts happen.

And the biggest technical shift that happened right before Instagram was founded was the advent of a phone that didn't suck.

Like in retrospect, we're like, oh my God, the first iPhone almost had, like, it wasn't that good, but Compared to everything else at the time, it was amazing.

And by the way, the first phone that had an incredible camera that could like do as well as the point and shoot you might carry around was the iPhone 4.

And that was right when Instagram launched.

And we looked around and we said, what will change because everyone has a camera in their pocket?

And it was so clear to me that the world of social networks before, It was based in the desktop and sitting there and having a link you could share, right?

The question is, what would you share if you were out and about in the world?

If not only did you have a camera that fit in your pocket, but by the way, that camera had a network attached to it that allowed you to share instantly.

And a bunch of people saw it at the same time.

The thing we did, I think, was not only, well, we focused on 2 things.

So we wrote down those things, we circled photos, and we said, I think we should invest in this.

But then we said, what sucks about photos?

Now, my phone takes pretty great photos, right?

Back then, they were blurry, not so great, compressed, right?

2, it was really slow, like really slow to upload a photo and I'll tell a fun story about that and explain to you why they're all the same size and square as well.

And if you wanted to share a photo on different networks, you had to go to each of the individual apps and select all of them and upload individually.

And so we were like, all right, those are the pain points.

So 1, instead of because they weren't beautiful, we were like, why don't we lean into the fact that they're not beautiful?

And I remember studying in Florence, my photography teacher gave me this Holga camera, and I'm not sure everyone knows what a Holga camera is, but They're these old school plastic cameras.

I think they're produced in China at the time.

And there, I want to say the original ones were like from the seventies or the eighties or something, they're supposed to be like $3 cameras for the, every person, They took nice medium format films, large, large negatives, but they kind of blurred the light and they kind of like light leaked into the side.

And there was this whole resurgence where people looked at that and said, oh my God, this is a style, right?

And I remember using that in Florence and just saying, well, why don't we just like lean into the fact that these photos suck and make them suck more, but in an artistic way.

And it turns out that had product market fit.

They were willing to share their not so great photos if they looked not so great on purpose, okay?

That's where the filters come into the picture.

So computational modification of photos to make them look extra crappy to where it becomes art.

And I mean, add light leaks, add like an overlay filter, make them more contrasty than they should be.

The first filter we ever produced was called X-Pro2.

And I designed it while I was in this small little bed and breakfast room in Todos Santos, Mexico.

I was trying to take a break from the bourbon days.

And I remember saying to my co-founder, I just need like a week to reset.

And that was on that trip, worked on the first filter because I said, you know, I think I can do this.

And I literally iterated 1 by 1 over the RGB values in the array that was the photo and just slightly shifted.

Basically, there was a function of R, function of G, function of B that just shifted them slightly.

And it turns out that actually made your photo look pretty cool.

It just mapped from 1 color space to another color space.

I mean, if you applied a filter, I think it used to take 2 or 3 seconds to render.

Only eventually would I figure out how to do it on the GPU.

And I'm not even sure it was a GPU, but it was using OpenGL.

But anyway, I would eventually figure that out.

By the way, anyone who's watching or listening, it's amazing what you can get away with in a startup as long as the product outcome is right for the user.

Like, you can be slow, you can be terrible, you can be, as long as you have product market fit, people will put up with a lot.

And then the question is just about compressing, making it more performant over time so that they get that product market fit instantly.

So fascinating because there's some things where those 3 seconds would make or break the app, but some things you're saying not.

You know, it's the problem Spotify solved, making streaming work.

And delays in listening to music is a huge negative, even slight delays.

But here you're saying, I mean, how do you know when those 3 seconds are okay?

Or are you just gonna have to try it out?

Because to me, my intuition would be those 3 seconds would kill the app.

I wasn't, I just knew how to do what I knew how to do.

And I decided, okay, like, why don't I just iterate over the values and change them?

And what's interesting is that Compared to the alternatives, no 1 else used OpenGL.

So everyone else was doing it the dumb way.

And in fact, they were doing it at a high resolution.

Now comes in the small resolution that we'll talk

By choosing 512 pixels by 512 pixels, which I believe it was at the time, we iterated over a lot fewer pixels than our competitors who were trying to do these enormous output-like images.

So instead of taking 20 seconds, I mean, 3 seconds feels pretty good, right?

So on a relative basis, we were winning like a lot.

Answer number 2 is, we actually focused on latency in the right places.

So we did this really wonderful thing when you uploaded.

So the way it would work is, you know, you'd take your phone, you'd take the photo and then you'd go to the, you'd go to the edit screen where you would caption it and on that caption screen, you'd start typing and you'd think, okay, like what's a clever caption?

And I said to Mike, hey, when I worked on the Gmail team, you know what they did?

When you typed in your username or your email address, even before you've entered in your password, like the probability, Once you enter in your username, you're going to actually sign in is extremely high.

So why not just start loading your account in the background?

Not like sending it down to the desktop, that would be a security issue, but like loaded into memory on the server, like get it ready and prepare it.

I always thought that was so fascinating and unintuitive.

I was like, Mike, why don't we just do that?

But like, we'll just upload the photo and like assume you're going to upload the photo.

So what ended up happening was people would caption their Photo they press done or upload and you'd see this little progress bar.

Okay we were no faster than anyone else at the time, but by choosing 512 by

and doing it in the background, it almost guaranteed that it was done by the time you captioned.

And everyone, when they used it, was like, how the hell is this thing so fast?

It wasn't like, these things are just like, it's a shell game.

And I think that, so you were willing to prop with a slow filter if it meant you could share it immediately.

And of course we added sharing options which let you distribute it really quickly.

So latency matters, but relative to what?

And then there's some like tricks you get around to just hiding the latency.

Like I don't know if Spotify starts downloading the next song eagerly.

There are a bunch of ideas here that are not rocket science that really help.

And all of that was stuff you were explicitly having a discussion about, like those designs and argument, you were having like arguments, discussions.

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Kevin Systrom: Instagram | Lex Fridman Podcast #243