Keyboards As Legacy Devices

One of the common arguments against the tablets as productivity devices, is that writing is an essential part of “content creation” and that long-form writing necessitates a keyboard.

I have strongly questioned the validity of both these assertions. I do not think that writing is an essential part of “content creation”, nor do I think that long-form writing needs a keyboard. Here I will focus on the second assertion and illustrate how the new generation might consider keyboards as legacy I/O.

Japanese students are faster with smartphones than with keyboards

A Japanese article in ITMedia tested how fast 16 Japanese students could enter text with smartphones and with PCs. The author found that many students could type up to 2x faster on smartphones, and that the fastest smartphone typer was faster than the fastest PC typer. They also found that the two students who were faster on a PC were using QWERTY keyboards on smartphone, instead of the flick input.

If we consider the comfortability of long-form text entry to be an essential part of a “content creation” device, then at least for the Japanese youth, smartphones are better than PCs.

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QWERTY is holding back Western languages

One might think that the above only applies to non-Western languages. However, I believe that we can also extend this argument to Western languages as well.

The issue is that Western language users still are using the inefficient and legacy QWERTY keyboard layout instead of something that has been designed for and optimised for smartphones (or even PCs for that matter). If Western users started to use a keyboard layout that was designed for smartphones, then maybe they wouldn’t need hacks like Swype to type faster. It is possible that what is holding tablet text entry behind is not the lack of a physical keyboard, but the lack of new ideas and the unwillingness to try a new input method.

Implications for the future

There is a possibility that the legacy of QWERTY keyboards is holding back innovation. The physical keyboards that Blackberry insisted on, prevented them from pioneering phones that had large touch-screen displays. The insistence on physical keyboards is probably a huge factor in keeping US schools from embracing the tablet form-factor (and is helping float the Chromebook market). If this continues, then it is very likely that innovation in the next wave of “content creation”, if it is to happen on tablets, will not come from QWERTY countries, but from non-Western language ones.

I see physical keyboards as legacy devices. They are slowing down innovation. Instead of discussing whether future “content creation” devices should have keyboards (like the 2-in-1 form factor), the real discussion should be how to create a better keyboard layout that is completely free of the century-old typewriter QWERTY legacy.

Appendix: About Flick Input

Flick input uses a keyboard like the one shown in the image below. There are 12 light grey keys that are used to enter characters. The Japanese phonetic writing system uses roughly 50 characters which is much more than the 12 grey keys. However, when you press one of the grey keys, you are presented with 5 different options. Flicking in the direction of any of these keys allows you to select one of these (no flicking selects the centre one). Therefore, from the 12 light grey keys, you can generate 12 x 6 = 60 different characters. Proficient users will memorise the flick direction, and will not need to wait for the options to appear on the screen. Instead, they will simply put their finger on any of the keys and immediately flick in the appropriate direction.

Since three Japanese characters contains about as much information as a single English word, you can see how efficient Flick input can be. Add the fact that the keys are much larger (fewer mistakes) and can comfortably be accessed with a single hand, and you can understand why Japanese youth are so fast with this.

Similar concepts are available for Western languages like MessagEase. One problem for Western languages may be that QWERTY is bad but not hurting enough to convince people to learn a new keyboard layout.

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Xiaomi Rethink

No so long ago, it was trendy to argue that Xiaomi was the next Apple, or at least a disrupter to the traditional hardware-based vendor model of which Samsung was the main incumbent. 

However, we now see Xiaomi losing market share, even in their home market of China. What happened?

The main arguments around Xiaomi were;

  1. They copy Apple and since Apple is a very aspirational brand, the Chinese flocked to Xiaomi. 
  2. They sell good quality and performance smartphones for a very affordable price. 
  3. They have a distinct UI, which makes their phones look much closer to Apple than their competitors. Furthermore, their UI is good and differentiates their products in a positive way. 
  4. They monetise on services, and that is why they can afford to not make profits on hardware. 

Most pundits were unanimously positive about the prospects for Xiaomi, at least in their home market. So what went wrong, or putting the blame on the pundit’s side, how did they get their predictions so wrong?

Fortune reported that 

Xiaomi, facing flattening revenues, launched efforts to bounce back with the opening of new retail outlets across China

IDC reported that;

In the past, Xiaomi started the trend of selling its phones online and other vendors soon followed suit and created their own online brand. After vendors witnessed OPPO’s success with its R9, they also started riding on the trend of hiring celebrity endorsers to represent their brand and appeal more to the young crowd.

Now, since I’ve started this post by hinting that the pundits were wrong, I have no intention of taking Fortune’s and IDC’s reports at face value. However, it is still notable how the argument has completely moved away from the software and services angle, and is now completely about marketing. 

The way it looks now is;

  1. Anybody can copy Apple given China’s hardware prowess. 
  2. Any major Chinese vendor can make good phones at affordable prices. 
  3. Distinct UI was not really a differentiator. 
  4. Services didn’t generate nearly enough revenue to allow Xiaomi to sell their phones at meaningfully lower prices. They made their profits on hardware
  5. Xiaomi’s strength was actually in their marketing tactics. However, like many other marketing stunts, it was just a fad. Losing their marketing power means losing almost everything. 

In general, tech pundits have a strong tendency to underestimate sales & marketing, and how it defines undifferentiated markets more forcefully than any new features can. Silicon Valley pundits assumed that services-based features and business models would define the smartphone market after hardware maturation. This is was not the case. It is likely that sales, marketing & distribution will be much more powerful. 

Apple’s Hidden Privacy Agenda

Is Apple being reckless?

One observation that some Apple pundits like throwing around is that Apple tends to add features with a broader future implementation in mind. For example, Apple added TouchID initially for unlocking your phone only. Then after a year or two, they added Apple Pay.

Although I think it would be wrong to expect Apple to be doing this for every feature, I do consider it very helpful to keep this in mind. That is, do not dismiss their actions unless you have throughly considered the possibility of a hidden agenda that will only reveal itself a few years into the future.

Apple’s stance on privacy is one of these actions.

  1. Most people have commented that Apple’s focus on privacy will strongly hinder, maybe even cripple their artificial intelligence efforts. This is very dangerous for Apple’s future because it is predicted that artificial intelligence will be a huge part of future personal computing.
  2. The plus side of a privacy focus is that it becomes a selling point for their products. However, we also know that today’s consumers do not care too much about privacy; at least, they seem to be happy to post photos on Facebook and search on Google.

Taking the two points above, it would seem reckless for any tech company to take the privacy position that Apple is holding today. The demerits are huge while the merits look benign. It looks like a totally irrational move for Apple that maybe enforced only because of Tim Cook’s personal beliefs in human rights. It does not make any sense, that is unless Apple has a larger agenda for the future; an agenda in which privacy plays an essential role.

Looking at Apple’s future markets

As I have mentioned previously, Apple cannot grow significantly larger than it is today without expanding into markets outside of tech. The market that tech can directly address, the market to which Apple can sell its current devices, is limited by the size of the economies in the countries which it sells to, and the amount of money each household is willing to spend on communications and entertainment. Apple has to move into different household buckets of spending. Furthermore, these buckets have to be large enough to drive revenue that can significantly contribute to Apple’s huge earnings.

Looking at what households actually spend their money on, one obvious contender is health. US households spend a huge proportion of their income on health, and for the countries which have an adequate healthcare system in place, health is a huge proportion of their government expenditure. There is a lot of money in health, and as populations in both developed and developing countries age, it is only going to get larger.

Apple is already actively involved in health. Not only does Apple have HealthKit, it also has ResearchKit which allows researchers to easily conduct large studies on patients and CareKit which allows patients to track and manage their own medical conditions. Importantly, privacy of health information is taken very seriously (unlike web history or location tracking data), and although I am no expert, it seems that there are rules and laws even in the USA for this.

For any company that seriously wants to get into health, data privacy is a hugely important issue. In particular, IT giants like Google or Apple will be held to higher standards, and expected to develop the necessary technologies if not yet available. They will be scrutinised by not only the authorities, but also by the regular press. If Apple wants to go further into health, prove the value of their services, and to extract revenue from this huge market, then they have to get the privacy issues sorted out first, and apply leading edge technology to protect patient privacy. This will be the prerequisite.

This is where I find Apple’s hidden privacy agenda. Apple does not need to have strict privacy to compete in the tech world against Google and Amazon. In fact, its privacy stance is detrimental for cutting edge artificial intelligence since server hardware will always be much more powerful than tiny smartphones for machine learning, and differential privacy will always negatively impact what patterns can be observed. However, to impact some key non-tech markets that Apple needs to venture into, privacy will be important and essential. Apple’s stance on privacy should be viewed not by which markets they are selling now, but on which markets they intend to sell to in the future.

Doing The Hard Things In Tech

When observing all the mega-hits that Apple has brought to the market the past 40 years, there is one consistent theme. Apple tries to do the things that are considered hard or even impossible at that time.

With the original Mac, they created a GUI-only computer that had a mere 128K bytes of memory. With the iPod, they synced 1,000 tunes (5GB’s worth) to your PC in an age where the predominant I/O (USB 1) was woefully inadequate (and tiny hard drives had just become available). With the iPhone, they shrunk a full blown PC into the size of a chocolate bar. With Mac OS X, they implemented a radically new graphical rendering system (Quartz Compositor) that taxed memory and CPU power and was unbearably slow on the hardware at the time, which only became usable years later with powerful new GPUs (MacOS X 10.2).

In all these cases, Apple was not shy to do something that most people at that time considered very difficult, if not impossible. Sometimes even Apple failed to do it well enough, and suffered the consequences of an inadequate product (low early Mac sales, super slow MacOS X 10.0, 10.1). But in the end, that is why they managed to differentiate, because others had not even started.

Apple’s approach to privacy can be seen in the same way. Whereas the common narrative was that you needed huge servers and massive data sets for good photo recognition, Apple has implemented machine learning on a smartphone that fits into your pocket. Of course they may be taking shortcuts, but so did the Mac 128K. What is important is that they took the challenge while everybody else was doing machine learning the old way (on powerful servers with less regard for privacy). Similarly, Apple has implemented a differential privacy approach which still has no guarantee of success. Even experts in the field are split and some say that the privacy trade-offs between machine learning effectiveness might result in a product that won’t work. Apple made the bet nonetheless. Apple chose to take the hard, possibly impossible way, by hobbling itself with the self-imposed shackle that is a privacy focus. They have thought different.

The simple reason why Apple’s approach has worked even once, is Moore’s law. Moore’s law is the central source of rapid technical progress and disruption, and it makes what is impossible today into something easy to achieve tomorrow.

No one who has seen the progress of silicon would doubt that Moore’s law will eventually make the processing tasks done exclusively on high power servers today, possible on the smartphones of tomorrow. We should also consider that the amount of data collected from smart devices must be growing even faster than Moore’s law (thanks to the shrinking size and ubiquity made possible by Moore’s law in the first place). Tomorrow, we will have many times more data than we collect today, and it is totally possible that the sheer vastness of data will make it possible to infer meaningful conclusions from differential privacy data, even when anonymised under very stringent noise levels.

Therefore, I predict that even though Apple’s approach to privacy may lead to a worse experience for the next couple of years, as Moore’s law kicks in, the difference will end up being negligible. By the time the general public become acutely aware for the need for privacy, Apple will have a powerful solution that in terms of user experience is just as good as Google’s.

The boldness to go all-in on a technology that just barely works, based on the hope that Moore’s law will save them in the next couple of years, is a defining feature of Apple’s hugely successful innovations. This is a formula that has worked for them time and time again.

This is what I see in Apple’s current privacy approach, and this is why I find it so typically and belovingly Apple.

Opening Up iOS And Implications

In the 2016 WWDC Keynote, Apple showed how it was going to open up Siri, Messages and Maps. It also showed how it was going to allow VoIP apps to show incoming calls just like how the default Phone app does; using the full screen.

Now if this was just Messages, then we might think that this was in response to the popularity of messaging apps like WeChat which work as platforms. However, if you listen to the State of the Union presentation after the Keynote, then you learn that even Xcode has opened up. It then becomes apparent that this is not just a simple response to WeChat, but a deliberate iOS-wide and even Apple ecosystem-wide direction that Apple is coordinating with their extensions system.

This extension system is not something that is new. In fact, it is an extremely old idea that is more often referred to “plug-in”. It is the idea that allowed browsers to provide rich multimedia experiences before the advent of HTML5. It is the idea that allows programming editors like Eclipse to become very rich tools for a huge number of programming languages. It has already been proven that this mechanism allows programs to be used for occasions that were never envisioned by their original creators, and can be very useful and effective. Although it does tend to add a layer of complexity for the end user, it is undoubtedly a feature that can have widespread impact.

Given that the extensions are likely to be very popular, then it is worthwhile to try to predict how they will advantage Apple and/or dis-advantage its competitors.

  1. Let’s ponder whether Google would open up Maps for example. Would they let third party apps provide the restaurant and shop recommendations layered onto Google Maps? What would be the implications for their business model that depends on showing sponsored recommendations in a more prominent way?
  2. Would wireless carriers be happy with VoIP apps that can integrate into the iOS to behave in just the same way as the default Phone app?
  3. Would Amazon open up its store so that random online stores can integrate themselves in the categorical listings and search results?

Many of Apple’s competitors provide the app layer for free and monetise at the extension layer. Google Maps plans to monetise by providing advertisements relevant to your location, but the Apple Maps extensions will allow third parties to provide this instead of Apple. Similarly Amazon provides an online store website with good search, recommendations and reviews. It monetises when people actually make purchases, which is similar to the layer that Apple’s extensions live in.

What we see here is that Apple has created a powerful extensions mechanism ecosystem-wide, that is almost guaranteed to be popular, and which may conflict with the business models of Google, Amazon and many other competitors.

The implications will be interesting to watch.

Thoughts on WWDC 2016

Here I want to jot down some of my key thoughts after viewing Apple’s WWDC 2016 keynote.

Core Apps as platforms

We saw a lot of the core apps being opened up to developers. We saw this for Siri, Maps, Messages and even the regular Phone app. Developers can now write code that directly extends the functionality of these core apps. This makes each app its own platform.

  1. This provides a path through which Apple Maps may become much better than Google Maps for many parts of the world. Third parties can innovate on how to provide better shop recommendations/information, transit information, rather then replicating core functionality.
  2. The same can be said of VoIP apps. I have never had a VoIP app that had nearly as nice a UI as the iOS default Phone app. Now VoIP apps can simply focus on providing good connection and voice quality.
  3. Ditto for Siri and Messages.
  4. This approach is only possible in some cases because Apple’s business model does not rely on advertising. For example, Google Maps could have trouble integrating information from Yelp, because this would conflict with their business model of profiting from the recommendations.

Differential Privacy

This is still a bold experiment. It has not yet been proved that this will allow sufficiently advanced artificial intelligence. In the following months, this will be put to the test. Differential privacy may prove to be just as useful as the lax privacy that companies like Google employ.

More importantly in my view though, is that differential privacy will allow Apple to get the most valuable data.

Privacy of health data is considered to be very important, especially genomic data. In genomic experiments using human-derived samples, great care is often demanded to defend the privacy of the donor. Google’s approach would probably be considered too relaxed to entrust such data, whereas Apple’s differential privacy may be sufficient. As a result, people might be very hesitant to give Google their DNA sequence information but not so for Apple (it might even be an FDA recommendation).

If this becomes the case, then Apple will have a huge advantage, not because it has better AI algorithms or more data, but because it has the most valuable data.

The same may occur with many other types of data. If this becomes that case, then Apple may gain preferential access to the more valuable and important data (that is not readily available by spying on your interactions with your phone). This will benefit Apple in the kind of conclusions that its AI will be able to make.

Google’s Justification

At Google I/O 2016, CEO Sundar Pichai showed a future filled with their artificial intelligence (AI).   It is all very interesting, but I do have some questions.

How much data does Google’s AI need?

Google’s AI is backed by enormous amounts of data about us. Data that is collected from photos that are publicly posted onto the Internet, and from photos that we upload onto the Google Cloud services from our mobile phones. Data from our messages on Gmail or events on Google Calendar. Data from the GPSs on our Android phones which tell Google where we are every hour of the day. Data from our browsers which tell Google (often without us knowing it), which website we have been visiting. No other company has access to similar amounts of private information.

However, what has not been answered is how much data Google’s AI actually needs.

Can effective AI be created without too much data?

A recent article by Steve Kovach on Apple’s next generation AI system is very interesting.

Siri brings in 1 billion queries per week from users to help it get better. But VocalIQ was able to learn with just a few thousand queries and still beat Siri.

This suggests that it is possible to construct a advanced AI system with magnitudes smaller data sets; data sets that do not have to be aggregates of private user information, but can simply be collated from a relatively small number of people who were paid for the work.

Of course we need to see the results to be sure. At the same time, I find it interesting that IBM Watson was able to win Jeopardy without tapping into huge data sets like those that Google uses.

Does an intelligent assistant mean you have to give up your privacy?

Apple tries hard not to see your private data. Apple believes that your private data belongs to you only, and that you should be the only one who holds the keys. Many people have questioned this approach, based on the assumption that widespread access to private information from millions of people on the server level is the only way to create a sufficiently good AI system.

Apple’s approach does not preclude the storage and analysis of personal data, as long as it happens in a way that Apple itself cannot see. One way to do this is to handle analysis on the smartphone. This is what the NSDataDetector class in the Mac/iOS API does. It’s actually pretty neat, and Apple has a patent on it. Similar but more advanced approaches could easily be implemented in iOS, given the performance of today’s CPUs.

The question is, is this approach sufficient? Will analysing your private data on your device always be much less powerful than analysing it on the server? Furthermore, will there be a significant benefit in collating the private data from strangers to analyse your own? If so, then Google’s approach (which sacrifices your privacy) will remain significantly superior. If not, then Apple’s approach will suffice. That is, you will not necessarily have to give up your privacy to benefit from intelligent assistants.

Does Google need the data for other purposes?

Let us assume that there existed a technology that allowed you to create an effective intelligent assistant, but that did not require that you give up your personal data. Would Google still collect your personal data?

The answer to this question is quite obviously YES. Google ultimately needs your private information for ad targeting purposes.

Could Google be using the big data/AI argument to justify the collection of huge amounts of private data for ad targeting purposes? I think, very possibly YES.

What Was Mobile?: A Broader Look At Tech

Benedict Evans recently wrote an enlightening post “The end of a mobile wave” where discusses what may come after smartphones.

I try to take a different view. I try to focus not on the devices or platforms that have come and gone, but on the underlying needs that have been satisfied. Hence instead of looking at voice, SMS and smartphones, I try to look at the need for communications (including the synchronous and asynchronous modes) and how they have been satisfied. Hopefully this will give us a broader view, and will also help us assess what the AI breakthrough may mean for us.

The needs that tech has satisfied

In my view, tech has been applied to three basic needs.

  1. The need to be entertained: This has come in the form of games, video, music, e-books, etc. Indeed, looking at the the disproportionate value that this category earns from the iTunes Store and the App Store, this is a huge need that tech has helped satisfy. In video, music and e-books, tech has almost already fully satisfied all needs. It is very hard to think of what tech could do more, other than reducing price (maybe indirectly through streaming services). Games however are a different story. We can see VR contributing hugely to the gaming experience, and we can expect further meaningful innovation in this area.
  2. The need for faster and more complex calculations: Originally computers were conceived for doing things like decoding encryption, calculating the trajectory of missiles and simulating atomic bombs. They were valued for the computing power. Today, we see computers controlling robots in factories, controlling huge industrial plants, navigating spacecraft, navigating self-driving trains, calculating the best way to travel to a certain destination (either by car or by using multiple public transport services), processing images for higher visibility, optimising logistic operations, designing new pharmaceutical drug candidates, predicting the best timing to sell or buy financial assets (for better or worse), and even playing Chess or Go. The current deep learning algorithms that have greatly improved machine learning techniques will most likely contribute to this area, on top of other automation/AI techniques that have been worked on for decades; deep learning will be a sustaining innovation that might or might not greatly contribute, depending on how well they perform at the task on hand relative to other techniques. Most of this innovation has happened in a way that has been mostly invisible to consumers. However, a lot of this has transformed how business is done and the efficiency that is attainable. I do not see any saturation in the needs for this market, and I expect technical advances to continue, with or without deep learning. Although on a much simpler but nonetheless much more broader scale, the digital spreadsheet pioneered by VisiCalc and improved on with Multiplan, Lotus 1-2-3 and MS-Excel have also contributed greatly to the need for complex calculations.
  3. The need for communication: Other than entertainment, the largest direct impact of tech on the lives of consumers has been in communications. In the early days of tech, word processors allowed us to write paper documents more efficiently (with easy editing). DTP software allowed us to even create publishing content quickly and cost effectively. Then with the advent of email and the Internet, communication and publishing suddenly became much, much simpler, cheaper and effective. Mobile build upon this trend, allowing people to be contacted wherever they were, first using voice or text, and eventually (with smartphones), using full email or other apps. With easy and cheap video calling now available, one would think that the need for communication has almost been completely satisfied and that we can innovate no further. However, anyone in the real world can attest that these tools are still no substitute for face-to-face meetings. Even with face-to-face meetings, intentions may not be clearly conveyed. The tools that we use, like PowerPoint, are woefully underpowered and inefficient. Therefore, I think that this need to has yet to be sufficiently satisfied. We are still waiting for the ultimate tool that will allow us to remotely communicate as if we were meeting face-to-face. We are even further away from telepathy-like tools that might free us from the limitations of human language, and to communicate as if our brains were directly wired together.

Hence my opinion is that there are still many needs that await a solution, and are upon the trajectory that tech has pushed us thus far.

Future tech and how mobile fits in

Mobile is ultimately about how we carry tech with us all the time. It is how we are entertained on the train. It is how we can access in real-time and on the go, the results of complex calculations. It is about how human beings can communicate better wherever they may be.

As I see it, none of these needs have yet been sufficiently satisfied. We need to do better. Therefore, there is a lot of room for improvement in the devices that we carry around. I cannot pinpoint exactly what these improvements should be, since they are also governed by the cutting-edge technology that is available. One thing is sure; the people and companies who can see beyond the current devices (the ones that have a track record of doing this) are the ones who will likely find what is still left to do.

(And no, I don’t mean the companies that simply say that AI will come next. Technology should never come first.)

Social Network Dynamics

Given Facebook’s very strong earnings report, it is very easy to forget the inherent fragility of a business predicated on social contributions.

In the case of Apple, they create a superb device and sell it. Customers are primarily attracted by the device itself.

In the case of Google, they created a superb Search service and provided it for free. Users were primarily attracted by the speed and accuracy of the results. The attractiveness of the service itself increased the number of users.

In the case of Facebook and other social services however, the situation is not as simple. Facebook itself has nothing to provide its users. Instead its users are what attract new users. Increasing the number of users on Facebook and the content that they share, is the only appeal that Facebook has. If they can increase users, then the attractiveness of Facebook increases exponentially. However, the same mechanism can move in the wrong direction. If users start to feel negatively towards Facebook for any reason and users start to decrease, there is no inherent attractiveness inside the social service that will dampen the negative spiral.

This is the fundamental structural dynamic of a social network. This is the reason why social networks can rapidly arise out of nowhere and quickly dominate local societies. This is also the reason why they could very rapidly disappear if they fall into a negative spiral.

As the social networks find themselves under stronger pressure to monetise their user base, I expect a significant number of them to take a step too far. They will cross a line, causing negative feelings to build up among their users (or in the case of privacy awareness, the line might come to them). This will start the negative spiral that, without any inherent value in the service itself, will be uncontrollable.

Facebook is an amazing success story. However, I worry that they are still but a single misstep away from irrelevance.

How Large Can Apple’s Services Grow?

In a recent post, I discussed that Apple has to grow outside of tech to continue growing. I also mentioned that the reason why Google and Amazon continue to grow is because, although they are tech companies, what they are selling is actually non-tech products to non-tech audiences (they are selling stuff like advertising slots, books and diapers).

So even as we see Apple’s services growing at 20%, and that it makes more money from services than from Macs, I think the more important trend to look for is how much they are positioned to earn from non-tech products.

To clarify,

  1. Sales from the App Store are nice, but we have to be aware that the vast majority is from games, and most money comes from a small number of “whales” (online game junkies). It is overly optimistic to expect this segment to continue 20% growth in the mid-term. More likely, we will see a flattening of growth from games and maybe a slight increase do to healthy growth of the non-game segment (which will however not contribute too much to the total).
  2. Music is unlikely to grow rapidly.
  3. Other cloud business (probably mostly additional iCloud storage) may grow rapidly for a short time, but few people really need Terabytes of storage, and prices are likely to drop heavily with competition.
  4. Apple Pay is more interesting because people might use it to pay for all kinds of stuff, including non-tech stuff. It is easy to envision Apple Pay being used to purchase advertising slots, books and diapers for example. We know that VISA has revenues of almost 14 billion USD (and obviously, from its business model, a lot of that is profit).

In summary, to understand the growth potential of Apple’s services, I think it is important to look at the non-tech markets and to see how Apple could add a thin layer of their services on these. Apple Pay is a typical case, but I would not be surprised if they decided to play a more direct role in e-commerce, for example. In the long term, I expect that these will be the main contributors to Apple’s service revenue, not the App nor Music stores.

Update

Matt Richman has done a similar but much better analysis of the non-tech service opportunities for Apple.