- What is an innovation? What is the difference between ideas, inventions, novelties, discoveries and creations?
- What is a disruption, formally-speaking? Also, what is it informally speaking? Why the distinction?
- What is Performance (or P-space)? How does Performance relate to utility, quantity, price and other microeconomic concepts?
- What is a new market? How do we distinguish a new industry from a new market.
- Do firms matter? Are firms causal to economic growth?
- What is the role of technology in innovation? Can we innovate without technology? Can we fail to innovate with technology?
- Do firms have life in the biological sense? If not how do we measure their existence?
- What are entrepreneurs? What do they do that’s different than what managers or business leaders do?
- How do you navigate P-space? How do you think about your business and economy in performance terms.
- What is a diffusion? Is adoption a good measure of technological progress? What is the connection between diffusions and disruptions?
- How does Capital map to P-space?
- How can we maximize economic growth? Is there a growth algorithm? What is the role of government policy in growth? What about the role of the creation of customers?
- What is the difference between consumption and non-consumption? What is non-production?
- What are the known forms of disruption? Can there be forms we don’t yet know about?
- Why do customers buy solutions? Understanding the causes of purchase decisions.
- Are profit motives necessary for growth? What motivates individuals, if not profit?
A merger is the result of two entities in the same business joining forces. It is usually justified through “synergy”, a euphemism for removing redundancies from their unity. Arithmetically, the desired outcome is that the resulting organization should be smaller than the individual parts (which is desired if the available market is shrinking.)
An Integration is the answer to the question of “Why two companies in different businesses are better off together.” Arithmetically, it suggests that the proposed sum is greater than the individual parts.
The spin-off is the response to a situation where one company houses two unrelated businesses.
For completeness, we can define an acquisition as the purchase of an unequal entity in order to improve the value of the acquirer.
The logic of any of these is that there is a disequilibrium which offers an opportunity to those who can exploit it. What the analysis fundamentally assumes is that the status quo of firm boundaries is not optimal.
Much of the measurement of the balance in the equations assumes that the overall industry is stable and that the problems (technical or market) are largely understood and that there is no learning that needs to happen. Boundaries need to be re-drawn because they are imperfect. Boundaries may have grown imperfect for many reasons: founders/owners were separate, markets and technologies evolve at different rates, resources are inflexible, processes are entrenched and values are outdated.
However, all this arithmetic can be safely thrown out of the window if there is a new industry in the making. If there is no balance to begin with because there is an entirely new problem being posited. That is, not only do we not know the answers to technical or market questions, we don’t even know what the right questions are.
When looking at the history of industry creation, the breakthroughs were always about the discovery of the right questions to ask. The early automobile industry was a scramble for solutions to a huge number of technical and market questions: technology, business model, infrastructure, usability, customer segmentation. From 1886 until 1915 there were many grand experiments with thousands of automobile firms springing up.1
Early computing, internet, mobile and consumer durables industries went through similar periods of grand experimentation. But the breakthroughs occurred when someone was able to ask (and subsequently answer) a question that nobody had asked before.
Henry Ford asked, “What would enable everyone to have a car”. The result was not a better car but a better production system.
Steve Jobs asked, “What would enable everyone to have a computer”. The result was not a faster computer, but a more approachable computer.
Akio Morita asked, “What would enable young people to have their own music”. The result was not a better audio quality but a smaller audio player.
Kiichiro Toyoda asked, “How can a car be built without faults”. The result was not a bigger factory but many smaller ones.
Jeff Bezos asked, “What would cause people to do their shopping online”. The result was not a lot of unique sites but one infinite one coupled to a logistics and computing service.
Having great taste in questions turns out to be the principal quality of the successful industrialist: The creation of economic value and power well beyond the boundary of the firm itself.
When the correct question is asked, resources can be efficiently marshaled to answer it. The wrong or incomplete question leads to inefficient resource allocation. And so the architecture of the solution can be built. If the new problem statement is a technical one then an integrated implementation is required. If the new question identifies non-consumption then a modular implementation is required. Each of these approaches suggests different customer sequencing strategies and different application of resources and processes.
There is no right architecture for industry creation. What matters is asking the right question.
So is Elon Musk, today, asking the right question? Continue reading “Tesla and SolarCity: Straddling the modular/integrated divide”
- Estimated 3000 world-wide [↩]
When Spotify and Pandora were starting their streaming services many were quick to point out that Apple was about to be disrupted. The future, they said, was streaming because (young) people could not be bothered with ownership of music and the limitations of a personal collection. Who would want to pay for a few hundred songs when they could listen to millions for free?
This perception continued and became more vocal over the years. Seven years in fact. Spotify collected 20 million paying subscribers while Apple did nothing. Pandora grabbed 80 million active listeners and possibly 4 million paying subscribers while Apple did nothing. The boat had sailed and Apple was not only not on it but oblivious that there was a boat in the first place.
At first Apple launched a half-hearted streaming service and then a paid service finally showed up with Apple Music in mid 2015. Since then the company managed to add 15 million subscribers. A tiny number compared to the 900 million iTunes accounts it had reported a year earlier. Pathetic. The number of music subscriptions relative to iCloud accounts, iTunes accounts and active devices is shown the the graph below.
It may be paltry compared to the count of users Apple may have in total, but how does a 15 million user base in 1 year compare with the growth rate for the incumbents Spotify and Pandora?
The following graph shows the ramps for Spotify, Pandora and Apple Music since their moments of market entry. The accumulation of users by Apple looks to be the fastest yet.
This is, of course, due to a maturing use case. Apple did not have to educate people to the notion of music as a subscription. It could just announce it and users would discover it and just sign up, especially if they were already iCloud subscribers and had a credit card attached to their iTunes account.
But that’s the whole point. Apple did not have to move first in music subscriptions. It did not even have to move second or third. When it did move it could just skim the market and add to its already healthy Services revenue (orange line in the first graph above.) Missing the boat in music in this case meant capturing all the value quickly and with minimal expense.
Fundamentally, Apple’s entry into music subscriptions was a sustaining effort. Streaming sustained Apple rather than disrupting it. The difference may seem merely one of semantics, but it is also the difference between life and death for a challenger. Meaning matters.
This is a cautionary tale for those who would pronounce every new idea as “disruptive” to Apple or anyone else on the basis of novelty alone. The tests for disruptiveness are easy enough and it behooves the analyst to apply them before dropping the d-bomb.
In Apple’s first 40 years it shipped 1,591,092,250 computers1.
This shipment total is higher than any other computer company in its first 40 years. Actually there are no other PC makers that are 40 years old. One computer maker (IBM) is older but they only sold PCs for 24 years and what they still sell they don’t sell in high numbers.
That does not make it the top seller in a given year. Looking at only the Mac, Apple’s traditional form factor personal computer, Apple has only returned to the top 5 last year. Only if including the iPad it was the top computer vendor in 2011 and including iPhone, it was first in 2009.
After having a 40 year run and after selling more computers than all American and Japanese computer companies put together, how should we think about the next 40 years?
First, clearly Apple shifted from being a “computer company”. It has already changed its name to exclude the word “Computer” but that has been interpreted as saying that it sells devices (which happen to also be computers.) The word “computer” is already archaic. We stopped using computers to compute in the 40s. We used them to make decisions, keep track of things, speed things up and then to communicate and then to entertain.
Devices, it seems, are what customers mainly use to do, well, everything. Computing has grown to encompass most activities we engage in. So is Apple then a device maker? Continue reading “The Next 40”
- including Apple II, Mac, iPhone, iPad and iPod touch; excluding Apple Watch, Apple TV and other iPods. Includes Q1 shipments estimated at 63,597,000 Macs, iPhones and iPads [↩]
How Apple is managed is one of its enduring mysteries. The idea that a company with $235 billion in sales is managed with a single P/L1 is fascinating in many ways. Not least of which is how it allocates resources. The fundamental question of which great idea gets to be funded and which great idea gets to be ignored is the core of every manager’s dilemma. The Apple problem is at scale when each decision’s consequences are so momentous. In the case of Apple there are so few projects that reach the market and their impact is so great that one wonders how they can be sure they are doing the right thing.
Conventionally, product development is filtered through a sieve of metrics, market sizing and impact on top/bottom income lines. These “financial” measures of success are considered prudent and optimized for return on equity (also known as the maximization of shareholder returns).
But this can be a toxic formula. The financial optimization algorithm always prioritizes the known over the unknown since the known can be measured and is assigned a quantum of value while the unknown is “discounted” with a steep hurdle rate, and assigned a near zero net present value. Thus the financial algorithm leads to promoting efficiency at the expense of creation. Efficiency may be the right priority when times are difficult and resources are scarce but creativity is the right priority in a time of plenty. And abundance is what being big is all about.
To allow for some creation large firms create divisions. Some divisions are tasked with “core products” which are measured by a set of firm metrics and some are tasked with “emerging products” which are given a set of loose metrics. This leads to obvious resentment and war between divisions when resources need allocating.
Ominously, the core divisions tend to always win. At the root of divisional power struggles are measurements–the roots of financial metrics. Managers fight with data. “You can’t manage what you can’t measure” they’re taught. But if you have something that can be easily measured and something that is difficult to measure, won’t the easily-measured be managed and the hard-to-measure be ignored?
There is a general principle at work here: Managing by measurement is fraught with the pitfall of measuring the wrong thing. Making sure that you’re measuring the right thing becomes the value-adding role of the manager. A role that is increasingly being neglected.
The mass phenomenon of measuring the wrong thing because it’s the easiest to measure is called “financialization”. Financialization is the process by which finance and finances (rather than creation) determine company, individual and society’s priorities. It comes about from an abundance of data that leads to fixation on what is observable to the detriment of awareness of hazards or obstacles or alternatives. This phenomenon is more likely when the speed of change increases and decision cycles shorten.
Financialization is creeping into all aspects of society and the extent to which it infects companies is the extent to which they suffer from early mortality.
So is Apple avoiding financialization? How can anyone avoid the tyranny of mis-optimization?
The unified P/L might be a clue. It obviates the need for divisional rivalry and power-based or financial politics. Without divisional P/L, management can be organized functionally with the obvious benefit of de-politicization. The singular P/L does create another problem however: the absence of an alternative resource allocation algorithm centralizes the decision. By centralizing decisions at the highest level, few decisions can be taken and that means each decision has to be right more often. We swap a distributed but financialized process for a central but capricious one.
So how is the central decision process made fair? What guides the allocation process? Continue reading “Priorities in a time of plenty”
- Profit and Loss statement is an income statement usually applied for a subset of a larger company which allows subdivisions to be managed individually. [↩]
Since writing Peak Cable six months ago, surveys, research and analysis have contributed to the themes of unbundling the TV package. The data under scrutiny is, as usual, the data that can be gathered. Unfortunately the data that can’t be gathered is where the insight into what is happening may lie. For instance, what matters for an entertainer is not how much you’re watched but how much you’re loved. Measuring love is done poorly with data on payment for subscriptions.
A better proxy might be time. Liam Boluk makes the point in his post that “focusing on cord cutting or even cord shaving largely misses the point.” Don’t follow the dollars, he says, follow the time or engagement. “Relevance” is what matters.
His data shows how linear TV has fallen by roughly 30% among the young (12-34) in the last five years. The trouble for the TV bundle (and advertisers) is that this is the most culturally influential group. They are also the group which will grow into the highest income group over the next decade. And this group does not love TV.
We have to remember that it was the youth who drove early radio, TV and consumer electronics markets. Those young are now the old which still cling to the old media, served by companies that grew old with them. They are the “high-end” customers with which Nielsen itself has grown. They have the most money to spend and they are the targets for the ads1
Paying $150/month to watch incontinence and erectile dysfunction ads—at a time not of your choosing—is preposterous for the young. They may like the programs but not the way they are packaged, delivered or interrupted. They are not smarter than their parents. They, like their parents, took to new technology more quickly. What makes the technology new is also what lets its makers separate the content from its delivery. These new technologies allow “modularizing” or unbundling that which was was integrated/bundled and thus allow their developers to focus on the customer’s real jobs-to-be-done.
Unsurprisingly, incumbents have responded by throttling access to original programming–an asset over which they still exert influence as distributors. Netflix and Amazon are taking the path of responding with their own blockbuster productions. Although Silicon Valley has more capital to deploy than Hollywood this battle of attrition is by no means one that incumbents will win, and generally, it’s not going to be pretty.
Tweaking the nose of the incumbent might not be the way to establish asymmetry. The better tactic may be to help the system survive but offer a “short-term alternative”. This is how iTunes took on and won Music. When Napster and file sharing created a clear and present danger to the industry, Apple’s approach of a controlled alternative allowed the industry to finally move to a digital download model.
- no longer the Pepsi generation, they are the Depend and Viagra and pharmaceuticals generation [↩]
What makes a product great? I struggle with this question because being great is not just being better than good. Greatness is to goodness as wisdom is to smarts. Just like getting smarter and smarter may never make you wise, getting better and better does not mean ever becoming great.
Greatness is transcendental. It’s hard to pin down. It inspires debate. It divides as much as it unites. It creates emotions as much as thoughts. It builds legends. It engages and persists. It lives in memory and penetrates culture. It implants itself in our consciousness persistently, to linger and dwell in our minds while we are bombarded with stimuli.
We use words such as “iconic” or “epic” to capture this permanent “mental tattoo” that we get from greatness. As important as this notion is, we struggle to define it. We don’t even have a proper word for it. Perhaps it is what art tries to be, or what drives us to achieve beyond surviving. As vague a notion as it may be, it is one of the most important notions I can think of. Greatness is the cause, perhaps, of our ascent.
In the absence of any measurement of greatness, how do we spot it?
It may just be down to “knowing when we see it”. But not everybody does.1
- Language is another indicator. When people attach brands to entire categories we get an indication of ubiquity and permanence. As much as the brand owner fears it, the genericization of a trademark is very probably an indication of greatness in consumer products. Aspirin, iPod, xerox, jell-o and app are examples where brands became words. [↩]
What if Apple did make a car? How significant could their products be? What would it take to influence the industry’s architecture?
The global market is forecast to reach 88.6 million vehicles in 2015 and there are many ways to segment it. One could look at geography or at product configurations or the emergence of new powertrain technologies.
One could also look at the participants.
In 2014 Toyota was the top selling automaker with a total sales volume of 10.23 million vehicles. The following graph shows the leading 15 producers and the percent of total production.
Executives at car companies have suddenly had to answer questions about potential entrants into their business. This is a big change. I don’t recall a time when this was necessary for over 30 years. For decades the questions have been about labor relations, health care costs, regulation, recalls and competition from other car makers. To ask questions about facing challengers posing existential questions must seem terribly impertinent.
For this reason, Bob Lutz, in his dismissal of Apple’s entry, is not alone. The industry has a century of history and has seen little disruption in the classic sense. I wrote a long piece on the fundamentals of the industry titled “The Entrant’s Guide to the Automobile Industry” which explained why this industry has been so resistant to disruptive change. At best a massive effort over multiple decades usually leads in a small shift in market share.
However, one should read that post as a thinly veiled threat. Just because disruption seems hard does not mean it isn’t possible. Indeed, the better you understand the industry the more easily you can observe its vulnerability and the more rigid the industry seems the more vulnerable it may be to dramatic change.
The formula for successful entry is the same for all industries: compete asymmetrically. This means introduce products which change the basis of competition and deter competitive responses by making your goals dissimilar from those of the incumbents. This is classic “ju-jitsu” of disruptive competition.
Here’s how it would work.
Bob Lutz suggests that there is no profit to be gained from selling cars on the premise that costs are very high while pricing will be held down by competition. That may be true but entrants could deploy new processes that lower the costs of production. Traditional car making is capital intensive due to the processes and materials used. There are however alternatives on the shelf. iStream from Gordon Murray Design proposed switching to tubular frames and low cost composites. BMW has an approach using carbon fiber and other composites. 3D printing is waiting in the wings. All offer a departure from sheet metal stamping.
With new materials, costs for new plants can be reduced by as much as 80% and since amortizing the tooling is as much as 40% of the cost of a new car, the margins on new production methods could result in significant boosts in margin.
There is a downside however. What is usually compromised when using these new methods is volume and scale of production. So that becomes the real question: how many cars can Apple target? 10k, 50k, 100k per year? Could they target 500k? That would be 10 times Tesla’s current volumes but only a bit more than the output of the Mini brand.
Now consider that the total market is 85 million vehicles per year. For Apple to get 10% share would imply 8.5 million cars a year, a feat that is hard to contemplate right now with any of the new production systems. On the other hand selling 80 million iPhones and iPads in a single quarter has become routine for Apple and that was considered orders of magnitude beyond what they could deliver. Amazing what 8 years of production ramping can offer.
So the answer to the operating margin might be in a combination of new processes and new ramp strategies.
But there are more levers of change. Continue reading “Soft Underbelly”
I was always bemused by the notion that the Internet was able to exist solely because most users did not know they could install an ad blocker. Like removing Flash, using an Ad blocker was a rebellious act but one which paid off only for early adopters. But like all good ideas, it seemed obvious that this idea would spread.
What we never know is how quickly diffusion happens. I’ve observed “no-brainer” technologies or ideas lie unadopted for decades, languishing in perpetual indifference and suddenly, with no apparent cause, flip into ubiquity and inevitability at a vicious rate of adoption.
Watching this phenomenon for most of my life, I developed a theory of causation. This theory is that for adoption to accelerate there has to be a combination of conformability to the adopter’s manifest needs (the pull) combined with a concerted collaboration of producers to promote the solution (the push). Absent either pull or push, adoption of even the brightest and most self-evident ideas drags on.
Ad blocking offers a real-time example of this phenomenon. On desktop or even laptop computers ads were tolerable and the steps required to naviagate in order to implement effective1 blocking were non-trivial. In addition, no platform vendors were keen to promote products which hindered revenues for their most important ecosystem partners.
Ad blocking as an activity had neither the pull nor the push.
- By effective I mean a combination of whitelists and customizations [↩]