iMessage, World War 2, and a Mathematical Theory of Communication
Maybe computers were never made for us in the first place.
When you send a text to a friend, what do you mean? And I don’t mean what words are you literally trying to say - I mean what emotion are you trying to send?
I’ve been thinking about this concept a lot recently, specifically because this weird culture war of messaging, this blue bubble versus green bubble, or iOS versus Android problem, has been a REALLY hot topic this year.
It’s a conversation that’s existed forever, basically since iMessage came out in 2011. But recently, there are more articles being written. More twitter threads that are trending. People talk about this problem every year, but in 2022 the discourse seems to be a lot louder than usual.
Recently, there was one particular article that made me think that I really have to look at this deeper. “Why Apple’s iMessage is Winning: Teens Dread the Green Text Bubble”.
There was a lot of the usual stuff in there. How Android users get left out of group conversations. How not having FaceTime and MeMoji support is getting more and more frustrating. But there was one particular statistic in that article that really drove home how important the color of your text bubble has become to a lot of people, at least in the U.S.
If you are between the ages of 18 and 24 in the United States, there is now about a 75% chance you own an iPhone, up from just around 45% six years ago. A 30% change in operating system market share in probably the most valuable age bracket for these companies. That is massive.
So I started paying closer attention. And I started to notice that every time I switch from an iPhone to an Android phone, I get messages such as “See you never I guess”, “Ew green bubble?” or “Wait did you get an Android”. And every time I switch back, I get messages like “Omg blue text bubble!” or “IT’S BLUEEEE”.
For the longest time, I found this to be so damn weird. It just feels like such an immediate, visceral reaction to the color of a message. Blue is not inherently a more valuable color than green. It’s.. just a color.. Right?
So, my original intention with this project was to understand why. What exactly is the underlying catalyst that makes people feel such an instinctual reaction to those green bubbles? What is it that makes you experience such a primal sense of frustration when you get a message that isn’t the color you were expecting?
If you look at the surface, there are some fairly obvious answers. Mainly, that iMessage is a rich communication service. It’s internet based, which means it can support features like reactions, and typing indicators, inline replies, better group messages and MeMoji. And it’s also only on the iPhone, so messaging an Android phone reverts your conversation back to SMS. A much older, less feature-rich messaging standard.
But I wanted to look a little bit deeper. What exactly is it about the human need for communication that forces us to feel physically uncomfortable when our rich features are taken away? That forces my iPhone friends to send me messages like that when I switch back to an Android phone? What is it that causes a single app to raise an operating system’s market share by 30% in 5 years? And maybe most importantly, what is it about our human condition that makes this whole thing so damn frustrating for everyone involved?
I’ve been looking into this for a couple of months now. I’ve scoured old research papers and poured over documentaries. And what started as a look into the color of a single bubble took me down a rabbit hole that includes a world war, a new mathematical theory of communication, and why I think that maybe, just maybe, computers were never even made for us in the first place.
In the early 1940’s, the world was in the middle of a conflict. And even though it might not have looked like it, this was a war of information. A war of encryption and secret messages. World War 2 was fought with soldiers, but it was really a battle of military intelligence. It was a war game driven by which side had superior intel.
In those early periods, the U.S. government was going college to college, recruiting the best minds in, what at the time, was considered electrical engineering. The government needed people who knew how to keep information secret, alongside people who knew how to break secret information wide open. And it was in that early period of the war that the U.S. government happened upon a man named Claude Shannon.
From the get-go it was pretty clear Shannon was gonna be a good candidate. He had Bachelor's degrees in electrical engineering and math. A master’s degree and a PHD from MIT. He was already a pretty big name in the field. And the people that know about him tend to regard him as the father of the information age. The reason that we have computers and smartphones and the internet. Up there with Einstein and Newton in terms of importance. I’m still kind of lost on why he isn’t more widely known. But he definitely should be.
When the government recruited him, Shannon had just published his Master’s thesis, A Symbolic Analysis of Relay and Switching Circuits, which proved that boolean algebra, the use of a 1, meaning true, or a 0, meaning false, could be used to simplify effectively all telecommunications relays, and also use those relays to solve boolean algebra problems. Shannon effectively invented the modern digital circuit - the foundations for the hardware that runs most of the modern world. And he did this as a 22 year-old man.
But the government had initially recruited him to help develop anti-aircraft missile systems and fire control systems. Even if it was clear this guy was gonna have a huge impact on the future, the government really needed things to be done in the immediate present. But Shannon was clearly most interested in pushing those information ideas even further. So while he was doing the grunt work the government needed at the time, he was quietly working on the next thesis.
With that first paper, Shannon had effectively created the logic board. A series of 1’s and 0’s that, when put together, could create much more complex systems and solve multiple different types of problems.
But building the logic board only solves half the problem. Shannon’s paper only outlined the ways a circuit itself could be simplified, but what about the information that ran through it? Designing a channel for information transfer but not thinking about the efficiency of the information traveling through that channel is like designing the most efficient highway but not thinking about the efficiency of the cars. Without optimizing the channel and the information moving through it, you’re still left with an inefficient system.
So in 1948, Shannon published a new paper he’d been quietly working on throughout the course of the war. A paper that would change the way we communicate. A paper that would lay the foundation for the entire information age. A Mathematical Theory of Communication.
That first paper Shannon wrote? Groundbreaking. Fundamental. Absolutely set the stage for the future of computing. But this paper? A Mathematical Theory of Communication was so important, it became the entire foundation for the science of information theory. If A Symbolic Analysis of Relays and Switching Circuits theorized the components inside your computer, this paper theorized the code that runs on top.
Now there’s a lot of stuff packed into Shannon’s 45-page paper, but at the heart of it, there are a few core concepts that every other idea is built on top of. Fundamentals that help define how we send information over practically any channel. And possibly, the reason this blue bubble versus green bubble conversation even exists. Let’s break it down.
First, let me present you with this flow chart.
This schematic outlines how information is transferred across any system. It works with speech, with light, with sound. Anything that transfers information from one place to another. As Shannon puts it in his paper, this flowchart is a function of time and “other variables”. That’s what this is meant to be. The fundamental building blocks of how information moves over a channel over a period of time.
I know this all might seem complicated. But believe me, I am bad at math. And as it turns out, this system is actually really simple to understand. I mean, it is supposed to be the building blocks of information. So let’s try it out. This article is about texting (and we’ll get back to that, I promise!), so I’ll use a text message I’m sending to you as an example.
First, you’ve got the information source. The beginning of the information system. In this case, that information source is myself. I have something that I am trying to communicate to you. An idea I am trying to send. But in order to do that, I’m going to need a transmitter for that information.
When we talk about texting, we’re generally talking about words on a screen. Things that you can read on your phone that communicate an idea. So in this case, the transmitter is my fingers, and the phone itself. The inputs going into my device are going through my fingers, and into the device. And that device can then transmit that information through a channel.
In this case, the channel is the wireless network. My text has to be sent through waves in the air, to a cell tower, then routed to you. But the problem with almost any channel is, well, it’s not perfect.
In any channel, there is potential for entropy and noise. Randomness and interference that is totally out of your control. If you’ve ever got a text that had some part of the message backwards, or missing, or messed up, that would be this. There is a chance that the channel you are sending the information through just won’t get to the other end exactly as you sent it.
Now, we’ve got the receiver. That’s the thing that catches the information being sent over a channel. In this case, it’s your phone! That device is receiving the information from the other end and displaying it on its screen.
And finally, you have the destination. In this case, that’s you! At the end of this chain, you are finally receiving the information I have put out into the world. And the system is complete.
It might seem like this flow chart is pretty specific to telecommunications, but it works for effectively any transfer of information. From speech, to smoke signals, you name it. Here, we can try something even more basic than texting.
Say you’re having a conversation with a friend. You’ve got the information source, which is yourself. The transmitter - your vocal cords. The channel - the vibrations of the air. You’ve got the noise - in this case, possibly very literally. Maybe there’s a truck driving by. Or you’ve got a cold and can’t articulate correctly. Then you have the receiver, the other person’s ears. And the destination - your friend. Boom. Channel complete.
This system, along with some other elements of that paper, make up Information Theory - the very foundation of how we move information across space. From the earliest methods of shouting, and foot couriers. To smoke signals to homing pigeons to Morse code and the telephone. Every single one of these communication systems can be reduced to the fundamentals introduced in this paper.
And the cool thing about reduction, breaking something as basic as communication down to its fundamentals, is that we can branch out from there. If we can trace something back to its roots, we can control how they grow and optimize that system. We can make it as efficient as possible. So how do we build the most efficient car to travel on the most efficient freeway?
Now we’ve gotta remember that especially back in the 30’s and 40’s, information was expensive. It wasn’t like today where we can write paragraphs and paragraphs of text without any issues. Back then, every bit mattered. And especially during a period of war, the speed at which an idea could be transmitted, passed through a channel, and received was literally life and death. So ideas needed to be short, and they needed to be exact.
“Hey! I thought you should probably know that there’s an issue. We’re thinking that there is a missile headed in your direction. John was telling me that it’s most probably coming from the west.”. Can you imagine if that’s what was sent over Morse code? The time it would take to encode that information, send it over the channel, and decode the information might be a pretty big problem.
Instead it would probably be a lot more efficient to send something like “Missile. West.”. I mean think about it. Even something like “SOS” is a method to create a shortened form of a message that means something else. There is so much information encoded into 3 simple letters. And that was much easier to send over a telegraph in the 1940’s.
So if information is expensive and time-sensitive, how do you optimize the information traveling over that channel? Well, there are two primary ways.
First is the density of that information you’re sending. Think about SOS again. 3 letters, tons of information. And Shannon even defined mathematical models to give weight to different letters and words in various languages to create the most informationally dense messages possible. Different letters take more time to encode and decode over morse code. Some words encode more information than others. So if you can optimize the information density, you can optimize the channel capacity.
Shannon defines channel capacity as “the highest rate of information that can travel through a given channel with arbitrarily low error rates”. Because you know, that information needs to travel quickly, but it also needs to do that while accounting for that channel noise that could potentially disrupt the message. You don’t want a message getting scrambled or screwed up by an enemy. You’ve gotta account for that.
These ideas around optimization eventually percolated out to the rest of computing in the future and were fundamental for building so many of the systems we use today. So much of computer science is based around how to code something in the cleanest, most efficient ways possible, especially back when CPU’s weren’t nearly as fast as they are now. You might not realize it, but so many of the systems we operate inside of in the modern era are based around efficiency and optimization.
You might remember texting on your flip phone where you only had a certain number of texts per month and a certain number of characters per text. You probably tried really hard to optimize what you were trying to say to fit in those messages. I mean I’m constantly changing around my words and using shorthand in my Tweets to make things fit in that 280 character limit. When the channel you’re sending information through is limited, you have to work really hard to transfer ideas as efficiently as possible.
The original Super Mario Bros fit the entire game in just 32 kilobytes of storage. There’s an old story about Pokemon Gold and Silver originally being much smaller games, but Satoru Iwata came in and compressed the game so efficiently that Gamefreak was able to add Kanto into the game alongside Johto. Especially in the early days of computing, efficiency was everything.
But here’s the big thing. And possibly the reason that texting sucks so much as a form of communication. As you may have noticed, humans are not efficient beings. At all. We say things multiple times. We write novels with thousands of pages. We produce movies that are three, four hours long. We tell long, winding stories. And it’s rare that any of these things are concise. They don’t need to be. They’re not commands that are meant to be executed. They’re emotions that are meant to be shared.
If I text you “Hey! Do you wanna grab a coffee at the cafe later? Around 6?”, there is some literal information coded in there. Namely, the location and the time. But what am I really saying to you? The coffee is clearly not the important thing here.
Words, just like the channels they travel over, are very specific, literal, boolean concepts. They are things that were created by humans to communicate an idea.
Maybe we had a falling out and I’m reaching out to try and mend that wound. Maybe I have some exciting information to tell you about that requires an in-person visit. Words, just like the channels they travel over, are very specific, literal, boolean concepts. They are things that were created by humans to communicate an idea. But the feelings that are communicated alongside these words is something that is entirely dictated by the context of a relationship. It’s a source of entropy and noise in the channel. I could send the exact same text to 10 different people, and the emotion that is communicated would come out entirely differently.
But is emotion mathematical? Shannon’s theories defined how we send information over a channel. But does it define how we communicate? We are not concise, and efficient, as you have to be in war. We are long-winded and artistic and complex. And it’s this idea that made me think that maybe.. Maybe Shannon’s paper was mis-titled.
A Mathematical Theory of Communication. Communication involves information. It is a core part of it. But human communication is not just information. It is information plus emotion. So with this in mind, Shannon’s paper probably should have been titled A Mathematical Theory of Information.
If you really want to define how we communicate, you have to consider emotion alongside information. You need to add this entropic, variable constant to Shannon’s theorems. A constant that is different for literally every relationship. A constant that turns information theory into communication theory. A real Mathematical theory of Communication.
And so, this brings me back to the original point of this video. What is it about the green bubble that makes iPhone users feel so uncomfortable? Why are my friends distressed when I switch to an Android phone, and overjoyed when I switch back? After going down this rabbit hole for the last couple of months, I think this is the answer.
Even if it wasn’t incredibly obvious in the 1930’s and 40’s, Information Theory, and the two papers that defined it, became one of the most important bodies of work to come out of the 20th century. These papers laid the foundation of all computing, and maybe more importantly, for how we share information. The shared existence of humanity now takes place over digital channels just as much as it takes place in person.
But these theories are fundamentally binary. They are ideas based around boolean algebra. They simplify more complex ideas into 1’s and 0’s. Truths and falsehoods. Yes, or no. And while they defined the most efficient way to send information over a channel, they didn’t define how we communicate. So everything that percolated from these concepts, from email to texting to Twitter, is also based around the efficiency of information across a channel. Not our interpersonal relationships.
In most communication, the emotional element becomes more apparent and more established when we add in our other emotional communication channels. Think about the inflection of your voice. Your body language. Your facial expressions. We have so many ways to communicate that emotional element in human-to-human interactions.
The channel capacity of information in a text message might be high, but what about the channel capacity for emotion? What about the element of communication that actually encapsulates what we’re trying to say? In a text, the words are only half the story. It’s the context, the emotional element that really communicates what we mean.
But iMessage, symbolized by that blue bubble, is a rich communication service. That means it’s internet based, so it can do things you just can’t do over SMS. iMessage users have access to things like reactions. Typing indicators. MeMoji, whispers and shouts in a message. Rich group conversations. High quality images and videos. These are things that amplify the emotional efficiency over that channel. While you’re still not getting all the benefits of face to face interactions, all these features have a much higher level of emotional efficiency than SMS. They are better methods of communication.
Reactions say so much more than the words they sit on top of. A heart reaction can say I hear you. It can say I love you. Or it can simply be a way to say you saw a message. Reactions encapsulate the emotional element of communication attached to the raw information they attach to.
Typing indicators can help indicate presence. They can give the sense that you’re right there with the person you’re talking to. That you’re having a consistent conversation, and not just a series of delayed relays.
Inline replies let you have multiple conversations at once. Gif support helps encapsulate an emotion in a universally understood reference. And even something like MeMoji can help add a personalized facial expression to increase the emotional efficiency of your message.
These rich communication features act as an extension of your vocabulary. They aren’t literal like text. They are features that help fill in for the emotional channels we have access to when we communicate face to face. Would a MeMoji ever be used during a war? Would a gif? They are inherently informationally inefficient. But they are emotionally efficient. And they help us communicate.
But the problem is Apple’s iMessage is one of the only messaging protocols that is not platform agnostic. It only works on Apple devices. So if you are messaging another iPhone, you’re using iMessage. But as soon as you message an Android phone, the protocol switches back to SMS - a much older, less feature-rich standard.
When an iMessage user sees that green bubble, it’s a signal that their emotional channel capacity is about to be reduced significantly.
So if you’re an iPhone user and you’re used to having this expanded vocabulary, this ability to attach emotion and context to a traditionally literal system, it’s going to be jarring when your rich communication services are suddenly reverted back to SMS - an old, archaic system which, besides emojis, only had the capacity to encode literal information.
So when an iMessage user sees that green bubble, it’s a signal that their emotional channel capacity is about to be reduced significantly. You can’t use inline replies. You can’t use reactions. Or use MeMoji. You don’t see typing indicators. Group chats lose rich features if even one Android user is in it. And even though you can technically react to an Android user’s message on an iPhone, the Android user doesn’t have the ability to react back.
All of these things add up. These frustrations over not being fully heard. The inability to be emotionally efficient. It might not seem like a big deal when a message gets reduced back to SMS, but imagine how it would feel to have half of your vocabulary suddenly ripped away. Imagine you are able to use the full breath of language with some friends, but only half of that with others. And so, over time, these frustrations become inextricably linked with the color of your bubble. And we end up here. With 75% of young people in the U.S. owning an iPhone. Because it allows them to communicate how they really feel.
So. If you’re wondering why people care so much about the color of your bubble, this is probably why. But none of this is to say that I agree that it needs to be this way.
As you might remember at the top of this article, part of the reason this conversation has gotten so loud is because Google has been pushing Apple to support an open rich communication standard called RCS, which could easily add to their Messages app if they wanted to. This would allow Android and iOS users to share in their rich communication. To have reactions and typing indicators and inline replies. Rich group conversations. RCS wouldn’t include everything Apple has added to iMessage over the years, but it would be a huge step in ending the classism that has been born from the color of a single bubble.
Of course, it’s more complicated than that. There is a whole saga around RCS itself. There are valid reasons why Google wants Apple to support it and also some valid reasons why Apple doesn’t want to. Like pretty much everything in life, it’s very nuanced. And for my day job I helped make an explainer around that whole thing. So you can check out that video here if you’re interested.
But you know, funny enough, while I was in the process of writing this video, something very interesting happened.
If you’ve ever used an Android phone, you’re probably aware that when an iPhone user reacts to an SMS message, the Android phone would traditionally get a whole separate message describing that reaction. It might say “This person loved this message”, or “This person emphasized this message”. If you haven’t seen this before, it looks like this.
This is obviously frustrating, because you’ll often get left with whole pages of texts saying someone “reacted” to your message. But the thing is, the Android phone couldn’t just not notify the user that the iPhone user had sent that reaction. Because then there’s an emotional disconnect. The result is making the best of a terrible situation. It’s frustrating to get that whole separate message, but it’s even worse to not let the Android user know that emotion was sent at all.
So recently, Google tried to solve this problem manually to help increase the emotional efficiency of a message between an iPhone and an Android phone. If you’re using Google Messages, your phone will now manually stamp a reaction on your message if it receives one from an iPhone, with a little message indicating that it’s been “translated from iPhone”.
Cool! Seems great, right? I mean it’s frustrating that the Android user can’t send a reaction back, but at least that helps solve the frustration of getting pages on pages of typed out reactions. But sometimes when you try to solve one problem you end up with another. And uh, that’s definitely what happened here.
A couple of these translations are as accurate as they could be - direct conversions from the iMessage reactions to an emoji stamped on the Android’s text. But some of them are.. less so. The “HaHa” reaction translates to a laugh-crying emoji 😂. The question marks translate to a thinking emoji 🤔. As hard as Google may have tried, these emoji just don’t have the exact same emotional context as the reactions being sent from the iPhone, and that’s a huge problem. And maybe the worst offender of all is the “heart” ❤️ reaction being translated to heart eyes 😍 on Android.
I know this might not seem like a big deal, but it’s actually a HUGE problem. Imagine you’re using an iPhone and you ask your Android friend how they’re doing. They respond with “Alright! My dog died last week so we’re working through it. It’s rough but we’ll be ok.” The iPhone user reacts with a “heart” ❤️ reaction, but on Android it comes out as heart eyes 😍. How is that appropriate?
In this context, a “heart” ❤️ reaction could be warranted. It could indicate a feeling of being there for the person. That you care about them and want them to know it. But if the reaction comes out the other side with heart eyes 😍 , that’s completely inappropriate. There is a completely different emotion being received than was sent by the information source.
To be fair to Google, not all of Apple’s reactions are actual emoji that can be translated to. There is no “HaHa” emoji. But there is a heart emoji. There is a question mark emoji. They should be doing their best to translate as accurately as they can.
What’s crazy is that plenty of other rich communication services exist. Things like Telegram or Facebook Messenger or WhatsApp. And in fact, in pretty much any part of the world outside of the US, these other rich communication services are the default. WhatsApp alone has over 2 billion installs. That’s over a quarter of the planet. But for some reason, the United States is obsessed with using the default messaging app on our phones. And on the iPhone, that’s Messages, and by extension, iMessage. And the features it carries are incredibly important for emotional efficiency.
So, this is where we’re at. Until Apple adds support for RCS, which I don’t see them doing any time soon, we’re going to be left with this emotional disconnect. This culture war between the blue bubble and the green bubble. And a culture that subtly encourages people to get iPhones. That 75% statistic is only going to get higher and higher. Because it doesn’t matter what features your phone has. At the end of the day, phones are all about communication. And whichever platform allows people to communicate with a higher level of emotional efficiency - whichever platform is better at communication theory. That’s the platform that’s gonna win out.
Thanks for reading. I’ll see you guys next time I’ve got something to say. See ya.
Woah. What a great write-up. This is so well-researched and informative :D
Great