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Category Theory

Invulnerable

On a recent podcast I noted that Google was perceived as invulnerable. In contrast, Apple is seen as temporarily enjoying a stay of execution.[1] This is not necessarily a bad thing for Apple. The more gushing the loathing or scorn, the more likely it’s a reaction to love and attraction. A brand dies not from hate but from apathy.

But nor is it necessarily a good thing for Google be be seen as invulnerable. There might be no “Google death knell counter”. There might not be a “Google is doomed” trope. If an executive from Google quits or is fired there is no investor panic. If a product is withdrawn there is no mourning. There are no journalists pursuing Pulitzer prizes by describing some seamy underside of Google. But there are no overt displays of affection either. Google is seen, on balance, as benevolent and hopeful. The discussion on business robustness is simply missing.

I suspect the absence of scrutiny comes from Google being seen as an analogy of the Internet itself. We don’t question the survival of the Internet so we don’t question the survival of Google — its backbone, its index, and its pervasive ads which, somehow, keep the lights on. We believe Google is infrastructure. We don’t dwell on whether electric grids are vulnerable, or supplies of fuel, or the weather(!)

Too complex, too pervasive. These are systems, not things. And people are not designed to contemplate systems. We leave that to experts, or better yet, computers.

The reason Apple is contemplated at all is that it’s not seen as a system. Even the suggestion that Apple is a system is implicitly treated as an impossibility. Because it’s not a system it’s fragile. It’s a person, or an idea, or a product or a singular “key” to something. It is, ultimately, mortal. The only debate is when it will die and points are earned for calling it sooner rather than later.

But what if Apple were a system? And what if Google were a person (or three?)

 

Notes:
  1. The list of Apple Achilles’ Heels is so long and creatively composed that it would take ages to compile, but here are just a few: the Mac (vs. Windows), Digital Rights Management (which kept the iPod alive), dozens of lawsuits (including from The Beatles), the Mac (when it ran Windows), PlaysForSure, Music Labels retaliation, the Zune, Android and clones, the Kindle and Amazon in general, more Mac, iTunes, iPod and iPhone and iPad killers than can be counted; Steve Jobs is ill, Jony Ive will quit, Tony Fadell quit, Rubinstein quit, Forstall was fired, etc. Feel free to add more through comments. See also Apple Death Knell Counter. []

But Apple does not pursue profits either!

In my essay on Google’s absence of profit (or income or business) motives questions were raised on the stated absence of hunger for profits from Apple and what difference there might be from Google’s philosophy.

Indeed, Jony Ive stated:

“Our goal isn’t to make money. Our goal absolutely at Apple is not to make money. It may sound a little flippant, but it’s the truth.”[1].

He was probably repeating what Jobs had previously stated:

“I remember very clearly Steve announcing that our goal is not just to make money but to make great products”[2]

However, note that both quotes are qualified. In the case of Jobs, he said “not just to make money”. Jobs clearly stated that great products lead to money. That great products are causal to money and therefore that if you make great products you make money. One leads to the other.

Ive also continued in this reasoning:

“Our goal, and what gets us excited, is to try to make great products. We trust that if we are successful people will like them. And if we are operationally competent we will make revenue. But we are very clear about our goal.”

I would paraphrase the Apple logic as “Great products are the means by which we sustain our business. By focusing on the product, the customer is satisfied and through that satisfaction we create the free cash flows which can be used to fund more products.”

There is a difference between Apple’s “indifference to money” and the “indifference to business models” that Google exhibits.

Google steps even further away from cash flows. Its goals are to build great things guided by their vision and patterns in the data they collect. The value is in the data itself rather than in any transaction.

As long as the source of money is unfettered, its provenance is uninteresting. A business model is a profit algorithm. It could be linked to the data but it need not be. Markets are messy and imperfect. Data provides much clearer views into value. You could conclude that value itself cannot be trusted to the judgement of the public. Value is to be determined through the recognition of patterns on data privately collected.

So when I say that Google has disdain for market mechanisms I mean that they believe they can do better. Apple still values the user as the ultimate adjudicator of its actions. Google looks past the user and interprets their intentions.

Google sees markets as ultimately obsolete.

Notes:
  1. at the British Embassy’s Creative Summing in July 2012 []
  2. Walter Isaacson’s Steve Jobs []

Google’s three Ps

A company is nothing more (and nothing less) than three things: people, processes and purposes. In the language of the software engineer these would be inputs, algorithms and specifications. In the language of classical business analysis they are assets (or resources), organization structures and business models. In military theory, these are logistics, tactics and strategy.

This is the trinity which allows for an understanding of a complex system: the physical, the operational and the guiding principle. The what, the how and the why.

When approaching any analysis problem, these questions form the foundation of causal inference. What is it, how does it work and why does it exist?

When analyzing nature the sciences often help with the what and the how but rarely address the why.[1] In contrast, man-made systems (e.g. systems of law, religion and commerce) require an answer to the why as there is a presumption of a will in their creation and preservation. The why allows ultimate judgement on the merit of an enterprise. The why may escape us but it’s assumed to always be there. For instance, in criminal law the motive is often a crucial piece of evidence but it’s not always found. In business, the motive for action or for organization is a crucial piece of the puzzle which often explains the what, who and how, but here the ultimate why is usually profit. This the characteristic of a for-profit business, the purpose is explicit.

Notes:
  1. Religion attempts to answer the whys which science leaves as unanswerable. []

Moonshot

When describing the process of disruptive innovation, Clay Christensen set about to also describe the process by which a technology is developed by visionaries in a commercially unsuccessful way. He called it cramming.

Cramming is a process of trying to make a not-yet-good-enough technology great without allowing it to be bad. In other words, it’s taking an ambitious goal and aiming at it with vast resources of time and money without allowing the mundane trial and error experimentation in business models.

To illustrate cramming I borrowed his story of how the transistor was embraced by incumbents in the US vs. entrants in Japan and how that led to the downfall of the US consumer electronics industry.

Small upstarts were able to take the invention, wrap a new business model around it that motivated the current players to ignore or flee their entry. They thus successfully displaced the entrenched incumbents even though the incumbents were investing heavily in the technology and the entrants weren’t.

In the image below, the blue “path taken by established vacuum tube manufacturers” is the cramming approach vs. the green entry by outsiders who worked on minor new products which could make use of the rough state of transistors at their early stages of development.

Screen Shot 2013-12-17 at 12-17-3.04.59 PM

The history of investment in transistor-based electronics shows how following the money (i.e. R&D) did not lead to value creation, quite the opposite. There are many such examples: The billions spent on R&D by Microsoft did not help them build a mobile future and the billions spent on R&D by Nokia did not help them build a computing future.

There are other white elephant stories such as IBM’s investment in speech recognition to replace word processing, the Japanese government spending on “Fifth Generation Computing” and almost all research into machine translation and learning from the 1960s to the present.

But today we hear about initiatives such as package delivery drones and driverless cars and robots and Hyperloops and are hopeful. Perhaps under the guiding vision of the wisest, most benevolent business wizards, breakthrough technologies and new infrastructures can finally be realized and we can gain the growth and wealth that we deserve but are so sorely lacking.

Bundling and Pricing Innovation

This was initially posted on LinkedIn December 16, 2013.

Innovation comes in many forms. Many times innovation is thought of as technological improvement or as invention. We can all cite examples of inventions which turned into industries which re-defined civilization. The steam engine comes to mind but there were many others before and after. Inventing something is certainly a way to create value but it’s not as common or as reliable a method as it might seem. Creating Intellectual Property is one thing, finding a defensible market and business model is quite another.

More often companies innovate in terms of processes or the “algorithms” which are used to deploy existing resources. Wal*Mart was immensely innovative in the way it organized itself and laid out a low-cost business model. More recently Amazon has innovated in distribution and fulfillment based on the ability to dispense with showrooms for products and sell directly online. There is little in terms of technology which Amazon “invented”. Rather, it deployed off-the-shelf technology in a novel way.

But what I want to address is a more mundane sort of innovation: marketing innovation, specifically pricing. Few would consider a price model to be an innovation but in fact it’s a core lynchpin to many breakthrough innovations. It was pricing which permitted Henry Ford to build an industrial empire. He could have built cars for those who could afford them as cars were defined in 1907 but he chose to build a car around a price point which was around the median of the population. A car “so low in price that no man making a good salary will be unable to own one.” His business logic began with a price and the product and process followed.

How many Americans will be using an iPhone when the US smartphone market saturates?

As previously noted, the US smartphone market has followed an almost perfectly logistic growth. The measured data (via comScore, in green below) follows a predictive logistic function (thin blue whose formula is discussed here).

Screen Shot 2013-12-13 at 12-13-11.30.54 AM

The other notable market observation is how closely the iPhone follows the same pattern as the market. The red line representing the iPhone above is almost perfectly parallel to the green and blue lines which represent the overall market. The reason for this seems to be that consumers are absorbing the product in similar way to how they are absorbing the technology.[1] The “learning model” which underpins logistic models could offer clues as to the cause. It suggests that there is a direct communication that happens between the product and the consumer.

Notes:
  1. Note that this pattern of adoption has happened even though the product has been at least partially unavailable to the entire market until quite recently. []

When will there be one billion iOS devices in use?

iOS unit sales crossed over 700 million units last month. That is a significant milestone but the total number of units in use is likely to be lower. My estimate based on device replacement assumptions is that about 500 million are still in use.

The estimated break-down of units sold and in use by device type is shown below:

Screen Shot 2013-11-25 at 11-25-2.59.59 PM

Dark Matter

Benedict Evans explains well the problem with measuring Android tablets. There are no reliable data collected because many of the devices are invisible through the regular, measurable channels:

  • There are no firms which report their shipments
  • They are not sold through retail chains which normally are sampled in the US and Europe (NPD and GfK respectively.)
  • They don’t show up in browsing or ad transaction data
  • Google Play statistics are missing most of the activations since they are not sold as bona fide Google-sanctioned Android.

The only measured statistic happens to be component shipments. Items such as screens, CPUs or perhaps memory might be visible to market analysts. It’s therefore tempting to add up tires manufactured to determine what’s getting sold in auto dealerships.

But it’s also hugely problematic.

A way to measure one’s life

In the post Seeing What’s Next, I showed how the rate of change of adoption of technology varies with time and asked what might be experienced by present and future generations.

It turns out that knowing how what innovations become universal and the speed at which these technologies are replaced can give us an idea of what individuals might experience in their lifetimes.

Here’s how to think about it:

Screen Shot 2013-11-19 at 11-19-8.04.42 PM

Seeing What’s Next

The adoption of smartphones in the US is on track for reaching 90% of the available audience by August 2016. This is a mere eight years after smartphones reached 10% penetration. As far as technologies go, that’s pretty fast. To get an idea of how rapid, I plotted a few other technologies and the time they took to grow within the US market.

Adoption Rates of Consumer Technologies

A few things to note: