Predictions For 2017: iPad Sales Growth

This is the second in my series of posts where I make predictions for 2017. The first one was about Autonomous Driving.

iPad sales growth

2016 was the year when we started to see revenue growth (but not unit growth) in the iPad. Many were quick to say that this was due to the introduction of the iPad Pro, but I think this misses the fundamental dynamic of what is happening in the tablet market. In fact, I have said in this blog multiple times, that most tech pundits have not understood the dynamic of the tablet market from the very beginning. The people who attribute revenue growth squarely on the iPad Pro inevitably expect a very slow growth going forward, since they do not see continuous growth drivers. My prediction is different in that I expect accelerated growth that will be in the high single digits.

Here I will illustrate my thesis and show why we should expect strong growth in 2017.

名称未設定 numbers

The above chart shows my hypothesis for what has been happening in the iPad market from the beginning; why we saw a very strong introduction, followed by a decline, and then a plateau.

  1. First of all, I separate the iPad market into two distinct segments. The first is the “Entertainment” segment which includes gaming, video watching, etc. The second is “Productivity” which includes writing, drawing, video/audio production, etc.
  2. In the initial phase of the market, we saw a huge uptake of usage in the first “Entertainment” segment. Even though the iPad was a new category device, looking at its gaming and video capabilities, it was a clear and obvious replacement for mobile game consoles like the Play Station Portable and the Nintendo 3DS. It was also a simple replacement for secondary TV screens. Since consumers could easily see the benefits and how it would work, the initial adoption was very rapid. That is, there was no need for an early adopter phase where only a fraction of the population would understand the merits of the device.
  3. However, as smartphones gained processing power and larger screens, they also started to satisfy the “Entertainment” segment. Hence the later decline in sales for this segment which started to happen in 2013-14.
  4. All this while, the “Productivity” segment of the market was going through a regular adoption curve of new category products. That is in the first few years, only the brave early adopters used iPads for “Productivity”. However, the number of these users has slowly but steadily been rising. In many cases, this has been happening more in the corporate market than in consumer markets because frankly, “productivity” is more important for our work than for leisure. It is important to note that whereas larger screen smartphones are adequate for playing games and watching videos, it is really torture editing a spreadsheet on smartphones. The benefits of a larger screen tend to be more pronounces in the “productivity” segment.
  5. Therefore, looking at the sum of both segments, we will see something like the yellow curve where a period of decline will be followed by steady growth.

Although I have made the “productivity” segment to show linear growth in the above chart, in reality, it is more likely to be sigmoidal. Therefore, when the “productivity” segment gains steam, we are likely to see quite steeper growth.

From my thesis, I can predict the following;

  1. We will see strong growth of the iPad in 2017 onwards. 2017 will start slow, but growth will accelerate.
  2. Since growth will come from “productivity” segments, the seasonality of iPad sales will become less severe.
  3. We will continue to see strong sales coming from corporations, but sales to consumers may continue to be weak.

Since 2017 is still the early phase of “productivity” segment adoption, it might yet be a bit early to see a strong impact in 2017Q1 and Q2. However, I do expect 2017Q3 to show a significant effect. 2017Q4 will be less impressive due to the “entertainment” segment dominating during the holiday season.

Predictions For 2017: Autonomous Driving Reality Check

I am planning a series of posts where I make predictions for 2017. I will put each prediction out one by one, and I will only pick those that have a strong implication for how we think about tech and innovation in general. I will also try to pick those that are likely to actually happen in 2017, rather than something that will happen eventually. That is to say, I will make it possible to check if the prediction was correct at the end of 2017.

Serious autonomous driving fatalities

In 2016, we saw a Tesla owner killing himself in a self-driving car. We also saw Uber self-driving cars running red lights in San Francisco.

Tesla managed to wiggle out of the problem by putting the blame on the driver, who may have been watching a Harry Potter movie instead of being ready to resume control of the vehicle. Uber managed to put the blame on the driver, by saying that the driver was actually in control of the vehicle at that time (which frankly sounds rather unconvincing).

In 2017, more companies will put their self-driving cars into public roads. Fierce competition and investor pressure will mean that some companies will even do this prematurely, before the technology is truly ready. In effect, it is likely that we see something like the Titanic crashing into an iceberg. That is, we will see companies hastily putting autonomous cars onto roads before they are ready, possibly with more fatal consequences. For the sake of prediction, I would say that we will see at least two fatalities by June.

What will subsequently happen is very politic and depends on the huge lobbying power of the large tech companies. There will no doubt be a move towards regulation, but on the opposing end, we will also see an eagerness from governments to embrace the promise of innovation. It is difficult to predict which way the scales will tip.

What Is AI Good For?

With all the hype surrounding artificial intelligence, it is important not to get too immersed in the technical and science fiction aspects. Steve Jobs said “You‘ve got to start with the customer experience and work backwards to the technology.” and this is true of artificial intelligence as well. Although a full discussion is totally out of the scope of a short blog post, I would like to provide a few perspectives.

Contextual interfaces

In my opinion, the right click in Windows 95 was a huge innovation. Prior to that, one had to look inside a huge array of options under a menu bar, or scan through a panel of small and often obfuscated icons. The right-click contextual menu showed a short list of tasks that were all relevant to the object that was currently selected and relieved you of this wasteful routine.

Although this may not strictly be classified as AI, the way that that it lessened the burden on the human brain was significant.

Similarly, one application of AI that would most certainly be very popular with users would be UI improvements that significantly reduced the need to scan through a list of options to find the relevant actions. In iOS 10, Apple has introduced AI that learns which emails should be sorted to which folders and intelligently provides a shortcut so that sorting emails is much quicker and easier.

Automated processing

Email spam filters learn what emails have a high probability of being span. From a user perspective, this is by all means artificial intelligence.

Although spam filters occasionally make mistakes, they help save our time and cognitive load by pre-filtering out stuff that is completely irrelevant to our work. Good spam filters also protect us from phishing attacks which can compromise whole corporate networks, and so it is no surprise that these are in high demand.

This is a very important market for AI.

Data Detectors

Apple had a patent for a very powerful technology commonly known as Data Detectors. This technology can detect addresses, event dates etc. inside text, and dramatically improves the user experience on smartphones where it is inconvenient to copy and paste.

The analysis of text, prediction of what a user might want to do with it, and providing a convenient and intuitive UI that enables the user to quickly get it done, can be a great timesaver.

Voice Recognition

It is well know that machine learning techniques have greatly improved voice recognition. Voice recognition has historically been valued by people who have difficulty typing. With mobile devices, voice recognition is convenient when you cannot use your hands.

Voice Interface

Graphical user interfaces are great for a stepwise approach for getting things done. However, since they operate by providing a list of options on a 2D screen, there is a limit to the breadth of commands that can be issued at any one time.

Command line interfaces and voice interfaces can get around this issue because they do not have to present a list of options. They are limited only by the ability of the user to memorise the available commands, and to issue them without referring to a menu. Hence voice interfaces are a convenient way to issue tasks quickly.

Summing up

My intention with this post was to show that there is much more to AI than a voice UI, and that from a practical perspective, the other applications have already proven to be very significant in terms of user benefit. Although voice UIs and predictive assistants like Google Now are interesting and futuristic, there is no reason why these applications will be the most useful and revolutionary.

Current advances in machine learning (Deep Learning) will build upon what we already have, and for smartphones with big screens, what we already have is a good graphical user interface.

AI, Voice UIs and predictive assistants should be evaluated based on their merits. How will they save us time and for what tasks? How will they help us when we cannot or it would be inconvenient to view our phone screens? How can they reduce our cognitive load?

Apple is pretty good at understanding what the user experience should be, and arguably, this will be just as important or maybe even more so than the underlying algorithms.

Peak Google Revisited

Almost a year ago, I noted that while a few prominent tech pundits had pronounced “Peak Google” at the beginning of 2015, Google was actually as strong as ever 12 months later.

In my post, I said that since no company keeps succeeding forever, anybody that predicts the demise of a company without giving a specific timeframe will always eventually be right. That is to say, any prediction without a timeframe is utterly valueless. I also noted that giving a timeline is extremely difficult.

However, I think we now have enough information to give a rough timeline on when we can expect “Peak Google” in financial terms.

Data points

I will lean on the following data points.

  1. The historically constant size of advertising spending
    In 2014, Eric Chemi writing for Bloomberg noted that the US advertising industry has always been about 1 percent of US GDP since the 1920s. This is significant because the US is much wealthier than it was 100 years ago, and it has gone though many ups and downs, even one world war in this time.
  2. The share of internet advertising within the whole advertising market
    According to eMarketer, total digital ad spending in 2017 will be 38.4% (77.37 billion USD) of total media ad spending. It will surpass TV ad spending which will be 35.8% of total.
  3. Google’s US advertising revenue is 31.00 billion USD in 2015, calculated from 67.39 billion global revenue of which 46% comes from the US. This is close to half of total digital ad spending (77.37 billion USD as noted above).
  4. Facebook’s 2015 advertising revenue was 17.08 billion USD. This is roughly a quarter of Google’s.
  5. As noted by Horace Dediu, economic growth in developing nations is not accelerating Google’s revenue growth. Despite rapid economic growth, developing nations are not becoming a larger part of Google’s revenue.

Assumptions

I will also assume the following;

  1. Google will not find a new revenue source that will be large enough to significantly add to its top line.
  2. Google’s revenue growth will continue to be dependent on and on par with growth in the US.

Logic

  1. Since the size of total media ad spending is constant as a percentage of GDP, this is the hard ceiling of advertising growth in the US.
  2. Digital ad spending is rapidly approaching this ceiling. With already close to 40% of total ad spending, there is less and less room left for digital to grow.
  3. Google has close to half of total digital ad spending. Of the remainder, it is likely that Facebook is taking half of this. Google has little space to grow by increasing its share within the total digital ad market. In fact, it is more likely that Facebook will eat into Google’s ad market share. Note that one estimate suggests that Google & Facebook own 85% of the US the digital ad market.
  4. Since Google’s ad revenue growth has largely been independent of developing countries, it is reasonable to assume that this will continue for the mid-term.

In simple terms, there is no longer room in the advertising industry for both Google and Facebook. Since Facebook has more momentum, it is likely that we will see Google being increasingly squeezed. Although the total digital ad spending will likely still see mid double digit growth, Facebook will take the majority of this growth and Google will probably drop to single digit growth before 2020.

What to expect in the future

We are already seeing signs of more disciplined spending at Google/Alphabet, most likely in anticipation of a slow-down in growth. Given the highly talented people at Google, it is no surprise that they understand that the end of double digit ad revenue growth is near.

However, disciplined spending can significantly alter what projects companies chase. Unlike the current Google which constantly throws spaghetti on the wall, a fiscally disciplined Google would probably be more cautious. Within the next few years, I expect that we will see a very different Google from what we are seeing now.

Update

One important thing to note is that “Peak Google” will be a result not of any strategic mistake made by the company, but rather a result of the saturation of the digital advertising market. This has the following implications;

  1. The whole digital advertising industry will suffer along with Google. In fact, smaller and less established players are more susceptible to adverse environments. This is already happening

  2. The saturation of the digital advertising industry also means the saturation of the ad-driven Internet. Startups without a monetisation model will find it harder to bolt-on an ad-driven one later.

  3. Being the most established brand in digital advertising, it is likely that Google will maintain a very strong position in the market for years to come. Like Apple, the issue will be the lack of rapid growth.

Who Will Win The Next Big Thing?

Many people seem to think that the next big thing in tech will be artificial intelligence, and that Google is much better positioned to win than Apple. Other people think that VR/AR is the next big thing, and again, at least one of the companies that is currently announcing hot new VR/AR gadgets is going to win (and not Apple).

However, history has clearly shown that this discussion is without merit. In fact, when a next big thing does come along, the most unexpected company or a company that simply did not exist before, is the one that actually wins. Very rarely if ever, does the company that invests tons of money on the early stage research emerge as the victor.

Google did not exist yet when Yahoo, Lycos, Altavista and many others were first battling to become the telephone directory of the web. Apple was just a failed PC company that was finding success in music when Blackberry, Palm, Microsoft and Nokia were battling to bring smartphones to the masses. Again Apple was a company that was fighting a losing war against IBM when Steve Jobs visited Xerox PARC which had invested heavily in next generation computing research. Compaq did not exist when IBM introduced the IBM Personal Computer. Microsoft was not even in the OS market when IBM knocked on the door looking for an OS for the x86 CPU.

Time and time again, history has shown that when something really new comes along, the companies that seem to have the strongest position from both market and technical standpoints, are rarely the ones that win in the end. The companies that do win are those that we would not even think about, or the ones that didn’t exist. This is what Clayton Christensen’s Disruption Theory is all about.

Therefore from a historical standpoint, if AI or VR/AR succeeds in disrupting tech, it is actually very unlikely that Google, Microsoft of Facebook would win in the end. These companies are in the exact same positions regarding AI and VR/AR as were Blackberry and Palm prior to iPhone, or as were Yahoo, Lycos and others were prior to Google Search. They have invested heavily into research and also into developing the early market. However, they have not yet discovered the formula that would propel them into the mass market.

No matter how unlikely it may seem today, history is actually quite unequivocal on this. The large and established companies that pioneer an early market, do not reap the rewards when disruption happens and the market goes mainstream. The odds are against Google for winning in AI, and the odds are against Microsoft and Facebook for winning in AR/VR (assuming though that AI and AR/VR do end up being disruptive technologies and not simply sustaining).

Although it is almost impossible to predict what will happen, I will just end this post highlighting a couple scenarios under which the Google might find itself vulnerable for illustrative purposes only.

  1. What if privacy became a block for AI penetrating the mainstream? What if consumers started to feel uneasy with the suggestions that Google’s AI made. What if a data breach at a major internet advertising company made it clear to mainstream customers that far more information was being collected about them than they had ever imagined? What if the technology emerged that made machine learning possible without compromising privacy? Would Google invest in this technology, or would it try to improve the AI results with its current privacy compromising methods? It is likely that Google will invest in the latter, which might be a bet on the wrong horse.
  2. AI could actually become the biggest threat to Google’s business model. What would happen if somebody came along with a good enough AI service which made web search obsolete, and which was combined with a monetisation scheme that was far less profitable than Google’s search advertising? Would Google copy that scheme, or would it wait until it found something that was at least as lucrative as the search business that it was cannibalising? What if this service took off, while Google was still looking for ways to maintain profits?

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.