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.)

Waves in tech and how Apple and other tech companies grow

When looking at the rhetoric around whether or not Apple has will or will not continue to grow rapidly, I often sense that people are not looking back at the history of tech and trying to understand how the tech giants grew in the first place. I’ll try to go into this a bit here, and from this, I’ll try to understand Apple’s chances of future growth and escape from maturity.


Tech did not grow by companies single-handedly creating new markets out of thin air. Instead, tech grew on top of waves. The successful companies are those that rode these waves. The unsuccessful ones were the ones that missed them, or fell off half way. There have been many waves in tech.

Digital productivity wave

This is the era when word processors and spreadsheets became popular (1980s to early 1990s). This created the first growth wave of PCs. Microsoft was the company that benefited the most from this wave. Both IBM and Apple initially rode this wave quite well, but they fell off half way.

Essentially, this was the digital productivity age. Word processors relieved us of the need to start out with a new sheet of paper every time we mis-typed a word (it allowed us to edit). Spreadsheets saved us from hours typing digits into calculators.

One thing to note is that the GUI revolution did not create a new wave, but instead empowered the ongoing digital productivity wave. The core job of tech remained more or less the same.

Internet wave

This was when the Internet took off and GUI-capable PCs made it accessible to mere mortals (mid 1990s to mid 2000s). Microsoft again rode this wave with Windows 95. The rise of the Internet also provided the wave which Amazon, Google, Facebook and others rode to become giant companies.

The Internet provided us with instant communication and information. It connected friends and co-workers via email. It connected shops and customers via the WWW. It allowed us to share photos via Facebook. It allowed us to find documents in the form of web-pages through search. This is significantly different from the previous digital productivity wave, which can easily be understood if you consider how the Internet wave changed how we presented our work. In the digital productivity wave, we still printed our work out onto paper. In the Internet wave, our work was shared digitally and instantaneously.

The Internet wave is still with us

The Internet wave has actually been very long. Starting in the mid 1990s, it is still going strong in 2016. The Internet companies are still growing, not necessarily because they are amazingly well run, but simply because the pie is growing. E-commerce is still a small portion of total commerce in the US and growing. Amazon is still riding this wave. Similarly, Internet ad spend is still a small fraction of total ad spend and this is what allows Google to continue to grow. The Internet is still growing, and in fact, this has been fuelled by none other than the iPhone which put the Internet in our pockets.

Apple’s maturity

Apple’s growth has slowed because although it dramatically expanded the time we spend on the Internet, it’s business model does not benefit proportionally with Internet usage. A single iPhone today is used much more on the Internet than it used to be in 2007. It is used for much more tasks, consumes much more data, and much more time is spent staring at their screens. However, Apple still charges basically the same amount of money per device. Apple does not earn significantly more money from the extended usage.

This is in contrast to Google and Amazon, both of which benefit proportionally from extended Internet usage. This is why Google still consistently grows at double digits whereas Apple’s growth comes and goes.

Finding the next wave

Although Apple could grow by finding a business model that grows in proportion to Internet usage (charging for content, services and payments), another way for Apple to escape maturity is to ride the next wave after the Internet.

Fundamentally, the Internet is about information and communication. Although these are very important facets of human existence, they are not the most important. Apple first entered the scene with digital productivity, and then rode re-ignited the Internet and communication wave with the iPhone. Similarly, Apple could provide tools to set fire the next wave.

One of these waves could be health. People living in developing countries spend their money on a plethora of things or which health is a huge portion. In fact, the money that we spend on health is much larger than what we spend on communication and entertainment (the current revenue centres of tech).

If Apple could find a way to benefit our health, keep us healthy, reduce our dependence on medication, improve the effectiveness of physicians, make health insurance more efficient, Apple would be opening the door to a huge market.

Thinking about Uber

The interesting thing about Uber and other services like AirBnb is that they are not just information services; they are the whole stack. Uber does not just notify taxi drivers where their customers are or assist payments; they provide the cars and the drivers.

In this sense, they operate outside of the Internet wave. They are entering the real world, and this is very different from what Microsoft, Google and Facebook have done before them.

This provides us with a hint at what the next wave could be.

What’s next for Apple

For Apple to find significant growth, they must find the next wave outside of the Internet. There is only so much left to do in communications if they continue with their business model of creating great products. Even VR has only limited potential if it is to stay within the boundaries of entertainment and communications.

If you look at where a individual in a developed country spends their money throughout their lifetime, housing, health, transportation, education are much larger than entertainment and communications. If Apple can successfully contribute to any of these markets, the current discussion of Apple maturing will soon seem utterly ridiculous.

Piece Of The Puzzle : Chromebooks In The U.S. And The Rest Of The World

I just found a very interesting pair of reports by Puneet Sikka on Market Realist/Yahoo Finance.

  1. “Why Apple Devices are Losing Share to Chromebooks in Schools”
  2. “Microsoft Gained Presence in the International Education Market”

The former article describes how Chromebooks are now more than half of all devices sold to US Schools (3Q15). The latter one tells us that in the international market, Chromebooks only have a 3% market share. In particular, it tells us that Chromebooks have a tiny 1% share in Brazil, Mexico and India, all markets where cheap Google/Android phones are doing exceedingly well due to high price sensitivity.

In fact, if you look at the chart below, it clearly shows that Chromebook market share is much higher for developed countries than for emerging ones. Although one might presume that cheaper Chromebooks are more suited for low-income countries, the reality is that the inverse is true; low-income countries prefer Windows.


The reason is clearly stated in the article;

The main issue with these countries is that they do not have the required broadband infrastructure to support the cloud-based storage requirements of Chromebooks.

We often only look at the flashy devices that we use, made by the most powerful tech companies in the world; Google, Microsoft and Apple. We often forget that to make these devices work, we need a lot of infrastructure. We also forget that WiFi can be very, very expensive when you want to deploy a network capable of handling hundreds of simultaneous connections. We forget the infrastructure because unless you have to dealt with it directly, it is invisible.

This is something to keep in mind.

  1. Google exists only because broadband Internet access is cheap. Its business model and its data collection relies on the infrastructure of vast network of Internet equipment that most people in developed countries now take for granted.
  2. Amazon exists only because of a highly developed and inexpensive network of deliveries to your doorstep. This was not common 30 years ago in Japan, and I assume, most other countries.
  3. Microsoft and Apple built their businesses before this infrastructure. They have business models that work without it.

One could ask the question; what infrastructure enables Uber? What recent changes have occurred to it? Or they could ask, what infrastructure enables self-driving cars? Then they should look at other countries to see if the infrastructure is there.

I strongly believe that to understand the underlying current flowing through technology and innovation, one has to understand the gradual changes of the infrastructure. The tech that we see are often just the rocks that are being pushed downstream. The Chromebook example is a strong reminder of what we should keep our eyes on.

Why The “Disruptive” Label Matters

In a recent “Critical Path” episode, Horace Dediu discussed the definition of disruption (00:46:40). Basically he outlines the boundaries of disruption theory and sets 3 criteria for Disruption.

  1. Entrant product serves customers who are over served by current incumbent product, or serves non-consumers.
  2. Asymmetry of motivation. The incumbent is not motivated to directly compete with the entrant.
  3. There exists a “Technological Core” that enables the entrant to continually improve the product.

Now this is totally great. It is great that the criteria for a “Disruptive Innovation” is clearly laid out, and that we can now identify whether a certain upheaval in the market qualifies for the Christensen-ish definition of Disruption or not. I totally agree with the criteria, as should, I believe, anybody who has carefully read Christensen’s books.

The problem is, if you have three criteria that you can answer with yes/no, then you have 2 x 2 x 2 = 8 possible situations. Only one of these Christensen Disruption. What are the others?

Furthermore, it becomes important to understand what it means to be a “Disruptive Innovation”. Clearly, Christensen does not intend “Disruptive Innovation” to describe all situation where there has been a significant disturbance in the market. He only talks about one. Then the question is, what makes his single segment so important? More importantly, why are so many Venture Capitalists upset and why do they complain so much when a single academic declares that the companies that they are investing in do not fit his criteria of “Disruption”?

The key to this question is to understand what the power of “Disruptive Innovation” is. The companies that are “Disruptive” are the ones that can benefit from this power. The ones that do not qualify cannot.

In any market competition, the companies with the more resources generally win. The weaker companies typically lose. However, this isn’t much fun. Venture Capitalists looking for incredibly high returns, are instead betting on small and weak companies that will somehow accomplish something that much larger companies cannot. They are essentially betting on David beating Goliath.

Sometimes smaller companies will out-manoeuvre larger companies by being more nimble. Sometimes the willingness of smaller companies to experiment with crazy ideas will allow them to win. However, until Disruption Theory, there was no theoretical framework that could predict which smaller companies would have a high probability of success. And there still isn’t any other.

Disruption Theory is still the only well accepted business theory that has been demonstrated to be capable of identifying (probabilistically) the winners from the losers. It is the only theory that gives us a path that David could take to reliably beat Goliath. It is the only well known wave.

Companies that qualify as “Disruptive” in Christensen’s definitions are the ones that could benefit from “Disruptive” dynamics, and who can ride the waves to beat the most powerful incumbents. This means that they can make the most of the resources (capital) that investors pour into them.

On the other hand, companies that do not qualify must take a different path. Although each individual path has not been fully explored, weak entrants will typically not survive on these paths. There is not well identified wave to ride. There may be other waves, but we can’t reliably count on them.

I hope that this describes what not being “Disruptive” in the Christensen sense means. It means that if you are not “Disruptive”, you are not riding Christensen’s wave. If you are a battleship, then you are probably OK, but that means that there isn’t much fun for the investors who poured billions of dollars to build the battleship. Unless you ride the wave, you probably aren’t going to get insanely high returns.

Peak Google?

When I look back at 2015, I am reminded that we started out with the idea of “Peak Google”. It was not only Ben Thompson, but other analysts too chimed in with this theme.

However, this hasn’t happened. At least not yet.Google’s revenue and profits are as strong as ever, and the end of search volume is nowhere in sight.

Google s first quarter under Alphabet continues strong growth in revenue and profit The Verge

The Verge

So were Ben Thompson and other analysts wrong?

Well the question really isn’t who is wrong or right. The problem is that the analysts were not providing enough information to be proved wrong or right in the first place.

In the case of Ben Thompson, he provides a disclaimer;

Still, I hope the subtle point I’m trying to make is clear: I think Google is quite safe when it comes to search, and that they will be a very profitable company for the foreseeable future. I just suspect we will all think differently about that dominance when it’s a small percentage of total digital advertising, just as we thought differently about IBM’s dominance of mainframes in the age of the PC, or Microsoft’s dominance of PCs in the age of the smartphone.

He makes it clear that one will be able to neither validate nor disprove his statement based on Google’s finances, and that he is talking about the fuzzy term “dominance”. And as expected, no clear definition of how “dominance” should be measured is provided.

And that’s what most shrewd analysts would do to cover their behinds, so that’s OK.

So how should we really think about the situation?

Companies don’t live forever

IBM and Microsoft seemed utterly invincible in their heyday and yet, they have lost a lot of their power now. There is a lot of discussion about how long companies will live, and right now, the average lifespan of a company in the S&P 500 is a mere 15 years. If we looked at tech alone, the lifespan would probably be much shorter. Hence even if you were to randomly predict that a company would decline in the next 5-10 years, you would still probably be right.

It’s harder to predict how companies will survive

Given that companies will tend to die in a short span anyway, discussing how they will die isn’t very constructive. It doesn’t add much value to the prediction that companies will die pretty soon anyway. It’s much harder, and much more relevant to discuss how they could survive. That is, one should presume that a company’s present business will surely decline. On top of this, one should strive to seek out what it’s next business could be. Regarding Google, calling Peak Google based on a prediction of the demise of its search advertising business doesn’t add much. It will happen, and no matter how hard you discuss, you will not get an accurate prediction of when. Instead, one should look at what Google’s next business could be, and if they are making significant progress on that. One should look at how Google could survive when (and it will surely come) the search ad business collapses.

When is almost impossible to predict

Let’s look at some very nice charts from, showing revenue/profit trends for Apple, Microsoft and Google.



What you can see is that Apple posted record revenues up until 1995. Although they were starting to have issues with profits since 1993, there was little to suggest that they would experience a total collapse in 1996. Just like very few economists could see the Global Financial Crisis coming, complex systems often show catastrophic behaviour. That’s why predicting the when is so hard.


Proof of “Peak Google” has not come this year, and in my opinion, this shows more than anything that predicting the when is hard, almost impossible. Although Ben Thompson was shrewd enough to not make any predictions, some analysts were not. My advice would be to stop trying to predict “Peak Google” or peak anything for that matter, treat the decline of any business as a given, and focus on what that company may have in store for the future.

Adding Common Sense To Disruption Theory

Clayton Christensen has been working to limit the meaning of “Disruptive Innovation” to the definition the one that he uses in his books. By this, he aims to counter the absurd notion that “Disruptive Innovation” was supposed to explain every kind of market upheaval, hence its inability to do so suggests that the theory itself is invalid.

Some people have suggested that since Christensen stressed that “Disruptive Innovation” is not everything, a new theory for explaining the other phenomena is required. For example, in the HBR article, Christensen said that Uber is not disruptive. Then what theory can explain the market upheaval that Uber is causing?

My suggestion is that we don’t actually need a new theory. All we need is a good understanding of what areas “Disruption Theory” covers, and a little bit of common sense.

Let me explain.

First, we have to start with a good understanding of exactly what “Disruption Theory” covers and does not cover, and to see what the theory omits. To do this, I will focus on what I think is the core tenet of the theory; how the incumbent will respond.

In “Disruption Theory”, Christensen covers two options that the incumbent may take. These are,

  1. The incumbent responds directly to the entrant, and competes with it to drive it out, or to severely limit its penetration.
  2. The incumbent decides that it is not worthwhile to compete with the entrant at the low-end, and focuses on the high-end. The word that Christensen often uses is “flee to the high-end”.

Both of these options assume that the incumbent is capable of responding to the entrant, and beating it if it so wishes. Although this may be more common, there is however little reason why this should always be the case. This is the omission. It is very possible that in some cases, the incumbent is simply incapable of responding.

It is also clear from a logic point of view, that adding this will fully cover the scope of possible options. The incumbent either chooses to respond, or not respond. In the case he chooses to respond, he either succeeds in launching a response, or fails. There are no other alternatives, no holes left open to consider.

In the following table, I have added this third possibility in pink. This is the possibility that the incumbent did not have sufficient resources to launch an effective response to the entrant.

CommonSenseDisruption numbers

On observation of this third option, it becomes clear why Christensen did not bother to spend time elaborating this option. It is just common sense. That is why I took the liberty of filling up the pink boxes with what I consider to be straightforward reasoning.

In this third option, the incumbent is unable to mount a response, simply because it is weaker. It might be a smaller company with fewer engineering responses. It might not have the marketing might of the entrant, or its brand appeal may be much weaker. It might lack the bundling opportunities that the entrant has. Or, in the case of the quickly changing tech landscape, it might lack the expertise that is required of the next phase of computing.

In fact, I would say that a better explanation of why Christensen failed to predict the success of iPhone is because he misunderstood how difficult it would be for Nokia to adequately respond. He underestimated the resources required to create an iPhone competitor, and the know-how that Apple (and Google) had accumulated as software companies. He overestimated Nokia, Palm and Blackberry’s ability to create a similar operating system, despite trying hard either in conjunction with Microsoft, or by bringing in Jon Rubinstein to head the WebOS effort. Christensen failed to correctly assess the huge advantage that Apple had in terms of resources relevant to smartphone R&D, and instead only evaluated the resource advantage Nokia had in distribution, etc. He failed to understand that the R&D resources required to compete in the post-iPhone world were not the same as what Nokia already had, and were instead abundantly available at Apple and Google.

If the entrant’s new product is well received by the market but the incumbent cannot create a product that is competitive, then the incumbent will lose. The entrant does not necessarily need to position its product at the low-end because it can win at the high-end. There is absolutely nothing surprising about this; it is simply common sense.

To summarise my table, I believe that “Disruption Theory” left out just one option, and that has caused confusion. By filling this in, I think I have fully covered the options regarding what an incumbent could do (luckily, the omitted option was common sense, and so filling it in was trivial). It is my belief that how the incumbent responds is critical, and very often the key turning point. This is why I do not think that it is necessary to further sub-divide each incumbent response category based on other facets like cost-structure, for example. I believe that the outcome will mostly be determined by the incumbent response, and that sub-divisions will not lead to substantially different outcomes, hence sub-division loses any practical utility. (This is to say that sub-divisions that only predict the same result as the parent category are not very useful. For example a sub-division inside the “cannot respond” category that predicts that the incumbent will fall is not really worthwhile, because the “cannot respond” category already predicts the same.) Furthermore, I doubt that a detailed analysis of the “cannot respond” category is necessary since it is already common sense for the vastly more powerful to prevail. I would however appreciate any discussion on any aspect of my table.

Finally, I would like to discuss what this means for Uber, the controversial example that Christensen touched.

My understanding of Uber is the following.

  1. Uber is the entrant but is also the Goliath. As such, the Uber vs. taxi companies battle sits in the pink area of my table, the area where entrant Goliath predictably slays David.
  2. Uber has accumulated billions of dollars in venture capital which it spends heavily on customer acquisition, driver acquisition, marketing and government lobbying. This in stark contrast to the typically small and not-very-profitable taxi companies, which have not accumulated vast amounts of cash, and cannot spend tons of money for the same purposes as Uber.
  3. Uber is the world’s first global taxi company. It has benefits of scale in terms of its technology, branding and marketing.
  4. Uber’s valuation is dependent on its not being seen simply as a taxi company, but also as a company that can disrupt many other industries. None of these efforts have yet been successful, but nonetheless, investors are seeing huge potential opportunities. However, taxi companies have never been seen in this light. They cannot raise capital with the same promises as Uber.
  5. All this results in Uber being a Goliath in terms of the money it can spend, relative to local taxi companies. With vastly more resources at its disposal, it is able to invest in ways that its competitors simply cannot. Although there are no doubt other complicated reasons why taxi companies might have trouble launching an effective response, the enormous difference in cash cannot be ignored. Being Goliath, Uber will predictably slay the taxi Davids in the absence of regulation.
  6. Importantly however, the fact that Uber is not “Disruptive” limits its ability to fulfil its investors’ dreams, even if it demolishes taxis worldwide. Uber is definitely a better mousetrap, but the economics does not ensure sustainably low prices nor sustainable profits. It does not reduce the burden of insurance, gasoline costs, car maintenance costs nor car depreciation. It does not eliminate the fact that the driver still has to make a living. These are the major costs of a taxi ride, and without significantly reducing any of them, there is little systemic reason why an Uber ride would remain cheaper than a regular taxi. As a result, the market will not significantly increase. Most people are unlikely to suddenly be enlightened to the possibility that ditching their own cars and using Ubers exclusively would be cheaper for them overall. This is what it means to not be a Christensen “Disruption”, but merely my pink box upheaval.
  7. To be “Disruptive” to taxi industries, to bring taxi services to those who do not currently use taxis often, and to significantly expand the market, Uber has to either a) make taxis much easier to use for the people who currently have no access, or b) make taxis much cheaper. The first option is something that the green boro taxi program in August 2013 did in Brooklyn, with dramatic results. Applied to Uber, this would be like bringing taxis to rural areas where no taxi service is yet available. Option b) could be achieved by things like UberPool (which I don’t think is yet mainstream) or self-driving cars. UberPool is interesting and could fit the definition of “Disruptive Innovation” perfectly, if it worked. Of course, car pooling would compete more with busses than taxis, and we have to consider whether UberPool makes more sense. Self-driving cars are a bit too far out to sensibly evaluate.
  8. There remains the possibility that Uber will be disruptive to other industries, for example, automobiles. This is probably the rationale for its astronomical valuation. I personally don’t think much of the prospects, but instead of confusing this post with another controversial discussion, I will leave this for another time.


Cleared up the logic for Uber and added a few more details.


We might want to consider cases where the incumbent changes their mind. An incumbent may initially dismiss the entrant as non-significant and decide to “flee-upmarket”. After realising that the entrant is a larger threat then they initially envisioned, they may choose to respond. Whether that response is too late or not will depend on how strong the entrant has grown. If the entrant has grown to the point that the entrant is more resourceful than the incumbent, then the entrant will win. If the entrant has not, then the incumbent will win. This is a situation that is well documented in Christensen’s work.


With the pink category included, it is easy to see the large role that venture capital plays. Venture capital can instantly create Goliaths that dwarf the incumbents. Startups that have not accumulated any cash to date, can suddenly obtain resources many times larger than the incumbents’, if they can paint a picture that is sufficiently rosy to investors. Of course startups have to convince investors that the opportunity is much larger than the current market, much in the same way as Uber is pitching itself as more than a taxi company, but that is speculation-based, and startups do not necessarily have to provide hard figures.

In other words, the incumbents are at the mercy of the speculation that their industry could turn out to be but a small portion of a much larger enterprise. This does not always turn out to be true, as evidenced by how Google destroyed the RSS market (they obviously though that the RSS market would turn out to be something bigger that what it was), only to abandon it later.


Just to clarify, I do not wish to debate the adequacy of Uber’s current valuation, nor do I want to denounce it as a VC bubble. I am totally uninterested in how Uber managed to convince investors of its valuation. I do not care if Capital follows Opportunity, or if Opportunity follows Capital. The only thing I am interested in is that the entrant Uber has vast resources while the incumbent taxi companies do not. This to me is an indisputable fact.

I do say that the taxi business does not validate its valuation, but I do not intend to question its valuation based on future, speculative opportunities. That risk is for the investors to take. They’re not spending my money.