After reading Walter Isaacson’s biography and the last few months worth of articles on Steve Jobs, I put together a collection of my favorite quotes about and related to him:

At the company he founded after being ousted from Apple, Jobs was able to indulge all of his instincts, both good and bad. He was unbound. The result was a series of spectacular products that were dazzling market flops. This was the true learning experience. What prepared him for the great success he would have in Act III was not his ouster from his Act I at Apple but his brilliant failures in Act II. – Isaacson (page 219)

It was yet another example of Jobs consciously positioning himself at the intersection of the arts and technology. In all of his products, technology would be married to great design, elegance, human touches, and even romance. – Isaacson (page 41)

Jobs’s interest in Eastern spirituality, Hinduism, Zen Buddhism, and the search for enlightenment was not merely the passing phase of a nineteen-year-old. Throughout his life he would seek to follow many of the basic precepts of Eastern religions, such as the emphasis on experiential prajñā, wisdom or cognitive understanding that is intuitively experienced through concentration of the mind. – Isaacson (page 48)

[Jobs's] reality distortion field was a confounding melange of a charismatic rhetorical style, indomitable will, and eagerness to bend any fact to fit the purpose at hand.  – Andy Hertzfeld 

The unified field theory that ties together Jobs’s personality and products begins with his most salient trait: his intensity. . . . This intensity encouraged a binary view of the world. Colleagues referred to the hero/shithead dichotomy. You were either one or the other, sometimes on the same day. The same was true of products, ideas, even food: Something was either “the best thing ever,” or it was shitty, brain-dead, inedible. — Isaacson (page 561)

What makes Steve’s methodology different from everyone else’s is that he always believed the most important decisions you make are not the things you do, but the things you decide not to do. He’s a minimalist. – John Sculley

Jobs’s sensibility was editorial, not inventive. His gift lay in taking what was in front of him—the tablet with stylus—and ruthlessly refining it. – Malcolm Gladwell (The Tweaker)

A genius is a genius, [Dean] Simonton maintains, because he can put together such a staggering number of insights, ideas, theories, random observations, and unexpected connections that he almost inevitably ends up with something great. “Quality,” Simonton writes, is “a probabilistic function of quantity.” — Malcolm Gladwell (Creation Myth)

He was, indeed, an example of what the mathematician Mark Kac called a magician genius, someone whose insights come out of the blue and require intuition more than mere mental processing power. Like a pathfinder, he could absorb information, sniff the winds, and sense what lay ahead. — Isaacson (page 566)

Steve Jobs’s natural talent is to imagine not only what consumers want now but also what they will want in the future — and pay a premium price for. He searches for discontinuities in the external landscape. He figures out trajectories of new opportunities. . . . He figures out precisely what problems need to be solved, however impossible they may seem, and searches for the best people to solve them, regardless of their status. — Fortune (How Steve Jobs gets things done)

Steve Jobs was an enemy of nostalgia. He believed that the future required sacrifice and boldness. He bet on new technologies to fill gaps even when the way was unclear. — Mike Daisey (Against Nostalgia)

That emphasis on consilience, even if it came at the expense of convenience, has always been a defining trait of Steve Jobs. In an age of intellectual fragmentation, Jobs insisted that the best creations occurred when people from disparate fields were connected together, when our distinct ways of seeing the world were brought to bear on a singular problem. — Jonah Lehrer (Technology Alone is Not Enough)

The nasty edge to his personality was not necessary. It hindered him more than it helped him. But it did, at times, serve a purpose. Polite and velvety leaders, who take care to avoid bruising others, are generally not as effective at forcing change. — Isaacson (page 565)

(* I put together the above image as a compilation of WSJ hedcuts of Jobs, mostly from this blog.)

Arbitrage is “the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices.” Once the arbitrage spread closes, the profit is made and the opportunity no longer exists. According to Austrian Economics, entrepreneurs’ profits “derive from the services he performs in detecting and eliminating arbitrage opportunities, thereby allowing supply and demand for a given good to meet.” By recognizing and acting on opportunities, the entrepreneur moves markets toward equilibrium. So entrepreneurial arbitrage is a low-risk way of exploiting gaps between what the market demands and what it’s being supplied until the spread closes.

There is very little “invention” involved—startups imitate or slightly modify someone else’s idea and only introduce breakthrough products or new business models many years later. This is what Peter Drucker calls creative imitation. The technology and market demand already exist, but the creative entrepreneur understands what the innovation represents better than the original innovators. This also includes packaging current technologies into new business models. Paul Graham calls this an idea that’s “a square in the periodic table”—if it didn’t exist now, it would be created shortly.

Duration of the arbitrage spread depends on the size of the imbalance and the nature of competitive advantages. The imbalance size is the difference between what the market demands (total size of the “job” and the level of performance demanded) and what is currently offered.  Competitive advantages can affect the duration because very low barriers to entry can cause a gap/imbalance to be filled much more quickly by the many entrants who seek any remaining excess profit.

Startups usually start in niche markets but can expand based on a number of factors. For example, durable moats don’t exist at first but can be developed over time while the arbitrage gap is closed. According to Mohnish Pabrai, “these events have no pattern and cannot be forecast when a startup is being formed. They happen to a very small minority of startups.” Only certain kinds of innovations lend themselves to strong competitive advantages, and hence large operations. (I.e. Standard Oil, US Steel and Nabisco succeeded while National Wallpaper, Standard Rope & Twine, and US Button failed.)

Examples:

• Ford—combined assembly line & interchangeable parts with gas-powered auto production. Most consumers already had the need for a car but couldn’t afford high-end models. Size of the need was huge, achieved strong economies of scale at first but after ~15 years competitors caught up and the gap closed.
• Starbucks—there was a gap between low-end convenience coffee and high-end coffee shops. Combined chain restaurant with coffee shop experience, market was very large & performance demand wasn’t too high at first. Entrants couldn’t match the reach and brand trust of Starbucks (which emerges from the habitual nature and consistently good experience across locations.)
• Facebook—there was demand for a way to connect to others on the internet that was being poorly met. Copied the “social network” concept but implemented it much better, gaining a foothold in the niche school market first then expanding based on advantages in network effects. Size of imbalance was huge and entry barriers prevented entrants from closing gap ahead of them.

Sources: Innovation and Entrepreneurship, The Origin and Evolution of New Businesses, The Dhando Investor

Animal species reside on a scale with “generalist” on one end and “specialist” on the other. Specialists can live only in a narrow range of conditions: diet, climate, camouflage, etc. Generalists are able to survive a wide variety of conditions and changes in the environment: food, climate, predators, etc.

Specialists thrive when conditions are just right. They fulfill a niche and are very effective at competing with other organisms. They have good mechanisms for coping with “known” risks. But when the specific conditions change, they are much more likely to go extinct. Generalists respond much better to changes/uncertainty. These species usually survive for very long periods because they deal with unanticipated risks better. They have very coarse behavior: eat any food available, survive in many climates, use a simple mechanism to defend a wide range of predators, etc. But unlike specialists they don’t maximize their current environment, because they don’t fill a niche where they could be more successful. It’s tough being a generalist—there’s more competition.

An environment with more competition breeds more specialists. Rainforests have huge diversity and competition, and therefore many specialist species.

Specialist examples: Orchid mantis (colorful mantis with appendages like leaves, thrives only on orchids and in tropics), sword-billed hummingbird (beak longer than body, co-evolved with flowers having very long corollas and difficult getting food elsewhere), koala (lives almost entirely on eucalyptus filling a niche that is toxic to most animals).

Generalist examples: Cockroach (survives in most climates, only needs water/moisture and a food source, only defense is responding to puffs of air), raccoon (wide diet, omnivore, lives in any area with trees, brush, or structures), rat (found everywhere in the world but the Artic, not picky eaters), horseshoe crab (wide diet on floor of sea bed, tolerates wide range of water temperature, can survive in low oxygen waters and out of water for extended periods; species over 360MYO).

Specialists & Generalists in Business

This model can be applied to many different areas.

Investors themselves can be put on the specialist/generalist scale. The most specialized investors focus only on narrow segments of the market or certain types of securities. They can be very successful during certain time periods but in the long run are usually disrupted by a changing investment landscape or black-swan-like event. The most generalized investors use very coarse, unchanging rules and are truly “go anywhere”, willing to buy or sell any type of security around the world. They may underperform or lag behind their specialized brethren in the short term but will likely do well in the long run when averaged out over many different environments. Most investors (including Warren Buffett) lie somewhere in between these two extremes. Specialists include investors in certain industries like Sam Zell (real estate) and Ron Burkle (retail), or in certain situations like Jim Chanos (shorting) and David Tepper (distressed). True generalists are more rare, but include great investors like Ben Graham and Seth Klarman.

A more interesting application is to the competitive business world. Like in the animal kingdom, generalists are rare and are usually much bigger than the specialists. They include big multinationals like Johnson & Johnson, Wal-Mart, Coca-Cola, and Proctor & Gamble. Also included are conglomerates that may hold many diversified specialists like General Electric or Berkshire Hathaway. Specialists are businesses that focus on a local niche whether in geography or product space. Because many specialists can dominate their niche, they’re usually protected by moats and thus have high returns.

This is what I call the Specialist’s Dilemma. The stronger your competitive position, the more vulnerable you are to eventually being disrupted and replaced.

Let me explain further. Out of the universe of companies that have strong competitive moats, many of them have advantages originating from the niches they occupy. (Which can lead to barriers like economies of scale, brand attachment driven by habit, and being ahead on the learning curve.) These advantages are durable only as long as the niche itself remains viable. In other words, the more specialized a company’s dominance is, the stronger its advantages are — but the higher the odds of the niche itself eventually disappearing. Not disappearing due to competitors within the industry, but due to the niche being completely destroyed and replaced by something else. The timing of when this happens partially depends on the “clockspeed” of innovation within the industry (more on that in my last post).

Just something to think about if you’re a long term investor or business manager.

The following is a simplified chart/timeline of innovations in the computer industry:

Consumers purchase computer systems, with new innovations or shifts in one component (processors or operating systems) driving innovation in computer design and vice versa. Other components like storage and display also drove innovation but were less important in this context. Most of the above innovations are technical, with the exception of the commodity PC makers (Dell, Compaq, etc.) which were an innovation in business model.

After money was transferred from consumers to computer makers, it went primarily to chip makers and OS developers. Because suppliers like Intel and Microsoft had strong competitive advantages, they had strong bargaining power, and therefore received and kept most of the value.

Retail Industry

The progression of innovation doesn’t just apply to industries as technical and complex as computers. Below is another timeline (dates are approximate) of the progression of the retail industry:

As you can see, the speed of innovation in retail is much slower than the computer industry (but still faster than other businesses, like consumer staples). Innovations and shifts in the retail industry were driven by both (1) prior retail innovations, like discount retailers combining the variety store model with the grocery model of low margin/high turnover; and (2) outside-industry innovations, like railroad transportation leading to nationwide chains, free rural postal delivery leading to mail-order catalogues, and the internet leading to online retail.

If you’ve got any suggestions or critiques of the above charts, let me know in the comments or through email. I tried to get all the facts right for a simplified version of the timelines, but may have missed something.

The hefty valuation, along with the youth of the company (2.5 years) and the reported operating loss may lead observers and the media to cry “bubble.” While I think that $25 billion is a very rich valuation and wouldn’t pay that amount if it went public today, I think people in general underestimate the potential of Groupon’s business model. In other words, they were probably right to turn down Google’s offer of $6 billion (even if they don’t cash out during the offering).

Before going into Groupon’s business model and competitive advantages, here’s a quick run down of some of their customer statistics from the S-1:

In the above equation, those 5 metrics are multiplied to arrive at Groupon’s net revenue amount (the amount Groupon gets to keep after giving merchants their cut). So in the first quarter they made $270 million before expenses.

First the market, then the moat

Before Groupon and all the other deal sites began, local businesses had many lackluster options for advertising their product. They could send coupons in the mail; pay for ads in a local newspaper; pay for outdoor advertising; or pay for online advertising via Google, local news sites, etc. Most of these options (Google less so) are what Seth Godin calls interruption marketing. They are made to interrupt what you are normally trying to do. And because of that, people usually don’t like them, and they have a very low hit-rate in acquiring customers.

Groupon sells what Godin calls permission marketing. People want Groupon to send them daily emails, even if they don’t bother with 95% of them. This “permission” asset that Groupon owns is very valuable. It is not only the huge email list alone that is valuable, but the fact that the people on the other end look forward to Groupon offers (even if they don’t come in the form of daily deals in the future).

Felix Salmon has a good article going into more detail on the value of the “collective buying” model to consumers.

The absence of a well-run permission marketer in the local advertising space (a HUGE market) was an enormous opportunity. It was a form of entrepreneurial arbitrage — find the gap, and close the spread before everyone else catches on. But filling an unmet market opportunity, even if you’re the first mover, doesn’t necessarily mean your profits are protected from assault by competitors.

No company, outside of one with government contracts, has a competitive moat right off the bat. But as Groupon grew to fill-in the market opportunity, it grew its barriers to entry along the way:

• Trust & habit — similar, though not as strong, is why people continue to use Google despite many low-barrier alternatives. This is the initial, fundamental reason for Groupon’s success. As long as they keep their customers happy (and don’t screw anything up), people will keep using what their familiar with and trust. That will also be the one they tell their friends about.
• Initial network effects – the more people that use the service, the better discounts Groupon can get because they can better guarantee a certain amount of people will buy. The group buying aspect provides a kind of mini-economies-of-scale for local businesses. If they have a certain amount of fixed costs, the more customers they can bring in the door (even at little or no variable profit) the better.
• Long-run network effects – local businesses want to sell/advertise their product with the company with the biggest customer base (email distribution, popularity, trust). This is the key advantage. Because Groupon already has a dominant market share, local businesses will seek them more than anyone else. This also leads to a feedback loop—the bigger and better quality Groupon’s “deal” base is, the better they can serve their customers. Combined with trust/habit, these network effects allow Groupon to capture a significant amount of consumer’s attention and time. That attention is obviously very valuable to local businesses around the world. It is much more valuable than say an ad in the newspaper, yellow pages, or on the side of the road.

Groupon also has a funding source in the form of float. It receives the cash from selling a Groupon in an average of 8 days, but waits an average of 79 days to pay merchants their share. This is mainly due to a policy of paying merchants 1/3 five days after their debut, 1/3 thirty after, and the remainder in 60 days. As of March, this amounted to total float of $268mm (payables – receivables). This float has funded most of their recent cash needs in excess of non-growth profit.

All the above also applies to LivingSocial, Groupon’s largest competitor. Past a certain point, customers will get “deal fatigue”. No one wants to receive and sort through 20 daily email offers, or to browse 20 different deal sites. People have a limited amount of “mindshare” to devote to things like this. But at the same time, despite the above competitive advantages, people will still subscribe and solicit other deal sites. This business model is well designed for an oligopoly between Groupon, LivingSocial, and maybe a few other smaller players (Gilt, Travelzoo, etc.).

Not all growth is free

Here is a quote from a blog post today on Forbes: “…investors would be wise to mind the gap between dazzling revenues what it costs to get them.” These are probably the thoughts, at first glance, of many observers.

But if Groupon cut its advertising to nothing, there wouldn’t be a mass customer exodus. In fact, I would guess that growth wouldn’t even slow that much — at this point, it seems that a good portion of obtaining new subscribers would be organic (spread by word-of-mouth). It was certainly smart to spend as much on marketing as possible at first in order to grow market share, for the reasons stated in the above section.

So a certain percentage of marketing spend could be classified as “maintenance expenditures”, which would be the amount they would have to spend to maintain the current customer base. I have no guess as to what that number is, but it’s a lot smaller than their total marketing spend in the 1st quarter of $208mm. To arrive at an adjusted figure (they call it “Adjusted CSOI”) Groupon adds back $180mm of that amount. This would mean it costs them $28mm a quarter to maintain the current customer base. Seems reasonable to me.

Using $28mm in maintenance marketing, Groupon’s first quarter steady-state operating income was $63mm.

There’s always a “but”…

Groupon’s model and growth story aren’t a perfect fairy tale. There is certainly a “fad” element to Groupon (but the same could be said for Facebook, and Facebook has a more difficult job of converting attention to money). As Andrew Mason mentioned is his S-1 letter to investors, there will be bumps along the way. That’s inevitable for a company only 30 months old, no matter what its size.

Groupon is not unassailable. Although they have a moat, it’s definitely not as big as the moat around Google, Apple, or Facebook. In going back to the “oligopoly” argument — even if this is the case, LivingSocial has similar advantages and could potentially outdo Groupon in terms of service and efficiently acquiring customers. I’ve been a customer of both for a while now and recently it seems LivingSocial has done an excellent job. Regardless of whether it’s Groupon or LivingSocial on top — and LivingSocial has a long way to go at ~10% market share vs. 70-80% for Groupon — the business model is a powerful one that will continue to produce large amounts of profit for years to come (until it is inevitably disrupted at some point in time by another business model).

In this week’s New Yorker, Malcolm Gladwell writes about innovation and how Xerox PARC failed to profit from the many incredible inventions that came out of its lab. (You can read the summary here.)

PARC (Palo Alto Research Center), located on the Stanford University campus, was founded in 1970 as a division of Xerox Corporation. They were an R&D lab that Xerox planned to use to both create new products and augment their current ones. They were tasked with creating “the office of the future.” In the mid-1970s, almost half of the world’s top 100 computer scientists were working at PARC. Within five years of its founding, PARC had developed a wide array of important computer technologies, including the following:

• Xerox “Alto”– the first personal computer with a mouse and graphical user interface (GUI) that included windows, icons, and pull-down menus.
• A WYSIWYG (what you see is what you get) text editor.
• Computer generated graphics.
• An Ethernet local-area-network.
• Laser printing.

In Everett Roger’s book Diffusion of Innovations, he uses Xerox PARC as a case study in the “commercialization” phase of the innovation-development process. What led the engineers and scientists at PARC to such an amazing track record? Rogers breaks it down as follows:

1. Outstanding R&D personnel. Several key R&D employees moved to PARC from nearby SRI International, where they had been working under visionary computer scientist Douglas Engelbart. It was around that time that funding to SRI from DARPA, NASA, and the Air Force began to diminish.
2. Management style that was conductive to creating technological innovation. Led by Dr. Robert Taylor, PARC encouraged the free exchange of technical information among the research workers. Their regular meeting room was equipped with beanbags, and the walls were lined with white boards. There was little hierarchy in PARC, and resources were plentiful.
3. R&D employees used the innovations that they created in their daily work.
4. Timing. The time was ripe for technological innovation in personal computing. A crucial prior innovation, the microprocessor, had recently been invented at nearby Intel Corp. Rapid advances in miniaturizing semiconductor functions, with a corresponding decrease in price per unit of computer memory, occurred in the early 1970s.

For anyone not familiar with the history of the computer, the question is obvious — if the above is true, why don’t we all have Xerox computers and Xerox operating systems in our homes right now? The simple answer is that although Xerox PARC invented them, they failed to commercialize and market most of their best discoveries.

In Gladwell’s article, he makes the distinction between the inventions created at PARC and the adaptations of those inventions by Steve Jobs and his team at Apple. The mouse that the PARC engineers designed cost $300 to build and would only work for two weeks of use. The mouse that Jobs contracted to design and eventually distribute with the Macintosh cost less than $15 to build and was much more functional and durable for a typical user. This difference, Gladwell claims, is not trivial. “It is the difference between something intended for experts, which is what Xerox PARC had in mind, and something that’s appropriate for a mass audience, which is what Apple had in mind.” In other words, Xerox created a breakthrough sustaining innovation and Apple adapted and packaged that technology into a disruptive innovation.

Gladwell concluded that although PARC was a great place to research and design cutting-edge innovations, it was indeed a bad place to commercialize them. “For an actual product, you need threat and constraint — and the improvisation and creativity necessary to turn a gold-plated three-hundred-dollar mouse into something that works on Formica and costs fifteen dollars.”

In his case study, Rogers lists the following reasons that Xerox ultimately failed to take the final step of commercializing the technologies developed at PARC: (Adapted from the book Fumbling the Future by Douglas Smith and Robert Alexander.)

1. Xerox was the leading company in the paper copier business, and it perceived itself as only in the office copier business, not in the personal computer business. In Gladwell’s words, “Xerox was a multinational corporation, with shareholders, a huge sales force, and a vast corporate customer face, and it needed to consider every new idea within the context of what it already had.” This is why one of the only innovations it did end up using was the laser printer.
2. No effective mechanisms were created for technology transfer from Xerox PARC to the manufacturing and marketing/sales divisions of Xerox.
3. The button-down organizational culture at Xerox’s Stamford, Connecticut headquarters clashed with PARC’s freewheeling hippie culture.

This is another classic case of the innovator’s dilemma for big corporations. Clayton Christensen names the following attributes as their downfall: (1) Current customers aren’t served by new market; (2)New market is too small for large companies; (3) Use of new technology isn’t fully known yet; (4) Processes that help them with current business hurt them with new business; and (5) New technology isn’t good enough yet to meet higher-end market demand.

Almost all of these attributes can be applied to Xerox and their PARC subsidiary. PARC, in essence, had too much resources at its disposal. Apple was serving a different customer base and was very nimble. They had no choice but to deliver a ship-ready product that they can make a profit on under their current cost structure. Xerox was devoted to their current money-makers and had little incentive to focus on the small, untested personal computer market. “Maybe the only lesson of the legend of Xerox PARC,” concludes Gladwell, “is that what happened there happens, in way way or another, everywhere.”

• +8,524% (37.7% annualized): Stock performance since Steve Jobs’ return to Apple in 1997.
• +821% (18.6% annualized): Revenue growth since Jobs’ return.
• +5,093% (66.4% annualized): Stock performance since the launch of the iTunes Store in April, 2003. (A disruptive innovation.)
• +951% (39.9% annualized): Revenue growth since iTunes Store launch.
• In the last 8 years, revenue has grown by $60 billion (1,000%). 73% of that growth came from newly launched products.
• In the last 3 years, revenue has grown by $40 billion (165%). 60% of that growth came from iPhone sales.
• $220 billion: Amount of products sold since the release of the first iPod.
• $19 billion: Apple’s cut of all sales through the iTunes Store, plus Apple iPod accessories (currently $5 billion a year).
• 298 million: Total number of iPod units sold.
• 90 million: Total number of iPhone units sold.
• If the cash and securities on Apple’s balance sheet (~$60 billion) was turned into a hedge fund, it would be the biggest in the world.

Apple Sales/Income Timeline

Apple’s unit volume for non-Mac products:

This is why we’re incredibly proud of Google Instant. Many of you guys speculated that we launched Instant to make more money. Well, let me tell you, that’s simply not the case. We launched Instant because it’s so much better for the user. In fact, from a revenue standpoint, its impact has been very minimal. And from a resource standpoint, it’s actually pretty expensive. So why did we do it? Well, we believe from a user standpoint, Instant is outstanding—and the data that we’re seeing actually bears this out.

The above was from Jonathan Rosenberg, Google’s SVP of Product Management. So, Google Instant was an expensive, non-revenue-producing upgrade to their lucrative search product. They did it, said Rosenberg, because it’s a huge improvement to the user experience. But how can that be measured? This got me thinking about what kind of metrics are truly important to Google in a broader economic sense. In Google’s financial reports they tout improvement in metrics like Traffic Acquisition Costs, Cost-Per-Click, and total number of Paid Clicks. All important to their business, but none that really capture Google’s overall business model. The most important metric to Google, I believe, is Revenue per Unit of User’s Time (or RUUT, for short).

Translating Time into Profit

Time is the ultimate scarce resource. Most businesses capture a portion of their customer’s wallets in exchange for a good or service. But businesses like Google (and TV networks, and most new media/web-based companies) capture a portion of customer’s time first, then translate that time into revenue.

Because time is scarce, when consumers choose to devote their time to a product or service, they are doing it at the exclusion of something else. So that company is literally capturing their customer’s time.  Before Google and other search engines, when people wanted to “find” something, they went about it a multitude of ways: white & yellow pages, classifieds, a library or bookstore, or just plain leaving your house and searching (hard to believe, I know). These things took up a lot of people’s time.

So when search engines and the internet came along, it hugely reduced the amount of time it took to find something when needed. This made searching and information-access very efficient, and instead of spending an hour or so searching for something in a library, you could use Google instead and it would only take a minute or two. So let’s say before the internet people spent an aggregate of 10 million hours “searching” for things. Then Google comes along, and because of their great product, they “capture” people’s search time. People now spend 1 million hours searching through Google and 9 million hours doing something else with their time (fictional numbers, but you get the point). So that’s the time part of the RUUT equation.

Google’s operating and capital costs—data centers, content acquisition, site service/maintenance—are for the most part commodities. Commodities is probably too harsh a term for Google’s talented and well-paid employee base, so let’s just say (ala Michael Porter) that Google’s suppliers have little bargaining power. In other words, ongoing operating costs are never going to be a problem for Google. Hence cost metrics aren’t really important to look at when determining Google’s future success.

In 2001, Google had already captured a good portion of user’s search time. Larry and Sergey knew that it was very valuable time, and they just needed the most efficient and user-friendly way to monetize it. The method Google came up with was to sell text-based ads on a cost-per-click basis and price them trough a Vickery Auction. When searching for something, people are much more likely to click on an ad if it is something related to their search. This is unlike other advertising methods like TV, newspaper, yellow pages, etc. The conversion rate for advertisers is much higher. And because of that, this “time” that Google sells is very valuable.

So the total amount of ad revenue that Google collects per unit of their user’s time measures how efficient Google is translating time into profit. Their main Google.com search has the best RUUT figure. Other ancillary services like Gmail or YouTube have much lower RUUT figures, but they still incrementally add to Google’s dominance. It follows that they can increase this metric by either increasing revenue through more efficient ad selling, or increase the time efficiency of Google’s users.

Back to Google Instant…

“It saves about two to five seconds a search,” says Rosenberg, “and users absolutely love it. The percentage of people that select Instant results before they finish their query is steadily rising. So, in other words, the more they use it, the more they like it.”

Saving two to five seconds on Google’s billions of searches a day adds up. They may not be increasing revenue with this new feature, but they are increasing the value of their product and the efficiency of translating time into revenue.

Rosenberg summarized Google’s advantages over other web companies when he said that “search is still the most monitizable moment on the web.” In other words, on the basis of Revenue per Unit of User’s Time, Google is the best out there. I think other companies that use the “time > profit” model like Facebook and Twitter will have an extremely difficult time matching Google in this regard. They may still be successful, but not nearly at Google’s level.

Apple has been a fantastic investment over the past decade. In fact, since April 2003 when they launched the iTunes store (and iPod sales took off), a dollar invested in Apple would be worth over $40 today – an annualized return of almost 70%. That’s a return that would make most venture capitalists blush. Not bad for a company founded 27 years prior.

One more statistic: even if Apple stock had gone nowhere from its IPO in 1980 up to 2003, its annual return over the three decades since going public would be 13%, which still beats the S&P 500 by over 3%. In other words, almost all of Apple’s current value (~$230 billion) was created over the last seven years.

Where did that value come from? For the seven years ending 2009, sales grew from $5.7bb to $42.9bb. Over 70% of that growth came from new products: the iPod, the iPhone, media sales, and other related peripherals. On a net profit basis, even more than 70% of Apple’s growth came from new products (segment margins aren’t disclosed, but overall margins have hugely increased and most of that likely came from new products). Aside from the storied brand name, Apple is basically a startup that was funded with the cash and income from their struggling Macintosh business.

Apple and the Red Queen Run the Hedonic Treadmill

“…it takes all the running you can do, to keep in the same place.” – The Red Queen, Lewis Carroll’s “Through the Looking-Glass”

So, clearly, the law of large numbers comes into effect when looking at Apple’s future growth prospects. To double revenues, Apple would have to sell an extra $43 billion a year in products – that’s over 68 million iPhones or 32 million Macs every year.

Of course, investors aren’t counting on Apple’s revenue doubling anytime soon, and the law of large numbers just means it’s more difficult for them to grow, not that it’s impossible. But to me, the more relevant model to use for Apple’s future growth is that of the Red Queen Effect.

In Lewis Carroll’s follow up to “Alice in Wonderland,” Alice comes across the Red Queen (not to be confused with the more popular Queen of Hearts) and for no reason at all they both begin to run. Alice notices that, despite their tireless efforts, they have remained in the same spot. The Queen informs her that she must keep running just to stay put (see the above quote).

In biology, the Red Queen’s race is translated into the principle that “for an evolutionary system, continuing development is needed just in order to maintain its fitness relative to the systems it is co-evolving with.” I think the Red Queen effect is an apt analogy for Apple’s current situation. Here’s why:

• Most of Apple’s growth in sales over the past 2 years has been from the iPhone (68% of growth to be exact).  The iPhone was launched in 2007, and most of these sales have been to new iPhone users. Certainly there are much more non-iPhone users to “convert,” but at some point most iPhone purchases will come from current users who are upgrading. This has already happened with the iPod – as seen in the chart above with iPod unit sales tapering off lately.
• Most future growth will have to come from new iPhone users, iPad sales, and any new products that Apple introduces.
• To justify Apple’s current valuation, the company must consistently come out with new (and popular) products to both maintain and grow profits. In other words, they have to keep running just to stay in the same place.

The one part of Apple’s business that isn’t susceptible to the Red Queen effect is their share of media/app sales through the iTunes & App store. Because of their closed system (disregarding the downside to this model), Apple controls and gets a cut of almost every application and piece of media consumed on their devices.

The iPod/iPhone/iPad act as mobile “delivering devices” for entertainment and productivity applications. If Apple can maintain their closed system – and Clayton Christensen’s prediction continues to be wrong – their share of content distribution will continue to rake in profits.

But as an investment, I don’t think there’s much of a margin of safety if Apple stops “running” and a new product launch fails. Investor’s high expectations have put Apple on a Hedonic Treadmill of sorts that only a fall in price can cure.

Disruptive Portable Music?

Before MP3 players, the only real option for portable music was a CD player. The first MP3 players were introduced in 1998, and had very low capacities. They could hold at most one or two CDs worth of music. In 2000, Creative released its NOMAD Jukebox, which had a capacity of around 1,200 songs. However, it was expensive and had limited usability.

The first generation iPod (5GB) was released in 2001 and could hold an average of 1,000 songs, or about 79 CDs at an equivalent quality. The cost of music (content) was low at first: consumers who already had a CD collection could transfer their songs to the iPod, or download them from the (usually illegal) filesharing programs on the internet.

The total cost per portable song for an iPod 1G was $1.48 or $0.39 if users converted old songs. This compares favorably to a CD player’s $1.95 cost per song (assuming someone can carry around a maximum of 10 CDs without it becoming too much of a burden – see notes for details). Despite this ability to carry more music for an incrementally cheaper cost, like earlier players the high total cost of the device—and the lack of convenience to use its capacity—confined sales to “fist adopters” and high-end users who were willing to convert their old music collection.

So at first, the iPod was a sustaining innovation relative to other portable music devices. Although it wasn’t made by a current industry leader, it was a breakthrough improvement upon other portable music devices and the performance metrics that customers valued (quality, capacity, cost per portable song, etc.).

The release of the iTunes store in April 2003 significantly reduced the barrier of purchasing and managing new music/content for the iPod. The store would offer individual songs for 99 cents or an album for $9.99 (also comparing favorably to CDs). As you can see in this chart, iPod sales go from an average of 123,000 a quarter to 545,000 a quarter immediately after the store’s release.

Although the iPod itself was not disruptive, the iPod/iTunes combination seems like a low-end disruptive innovation relative to both CD retailers/distributors and (to a lesser extent) record companies. It disrupted the “channel” of the music industry by coming in at a lower price point, a much lower cost structure, and more convenience for the end user. *

(* I should note that, unlike some innovations, the iPod & iTunes don’t neatly fit into any of Christensen’s categories. In the end, labels don’t really matter, though it does help to see where the product fits in and where its evolution may take it in the future.)

The value network of the industry was well situated before iTunes came along: Artists > Record companies > Music devices / CD manufacturers > CD retailers. Each constituent made money this way, and so their strategies were focused on lowering costs and satisfying current customers (CD purchasers). In the new network, the iPod/iTunes combo filled the roles of medium, device, and distribution. But it also had the ability to bypass record companies and distribute directly from artists. So although the record companies still play a major role, by disrupting their product channel iTunes forced them redefine how they made money.

Performance Oversupply

According to Clay Christensen, performance oversupply occurs when the performance of a technology under a certain attribute (whether it be quality, capacity, reliability, etc.) increases beyond what the market demands. Once market demands of that attribute are met, other attributes whose performance has not yet satisfied demands becomes more highly valued. Companies pursuing both sustaining & disruptive technologies will seek to use this “new” attribute to sustain and differentiate their product.

Both song capacity and purchasing convenience were what vaulted the iPod past CD players and other music devices. But as seen in this chart, the metric of song capacity (and in turn cost per song) quickly surpassed average market demand (though high-end users were still served by this ever increasing performance).

A typical pattern of attributes, according to Christensen, is the evolution from functionality to reliability to convenience to price. Price competition is usually the endgame once each dimension of performance has been fully satisfied. For the iPod, it seems that the most important attribute evolved from capacity to convenience to features—and finally to price:

When the iPod was competing for features (photos, bigger screen, video playback), it essentially branched out into the iPhone and iPod Touch. Although both have much less capacity than an iPod classic, they have enough capacity to satisfy average market demand along with the features that are now more important.

While the iPhone is a completely separate product, it evolved from the iPod’s value network and technology, and was an improvement upon existing cellphone designs. It is a sustaining innovation relative to both iPods and smart-phone devices. The iPhone (& Touch) transformed the iPod from a portable music device into a portable communication / productivity / entertainment device (with iTunes and the new app store acting as content distributors).

I’ll talk more about the iPhone—and where the iPad may fit in—in part II of this post.

Average song size is 5MB (Apple figures size is just over 4MB, though the actually average is probably higher). If you assume that for every 30 individual songs purchased the user purchases 1 album (13 songs for $9.99), the average cost per song is 92.3 cents. For the measure of song capacity, to partially adjust for the inclusion of “other media” (photos, videos, etc.) after the iPod 4G, it is assumed that 25% of the iPod’s capacity is not used for music, and in that space each “song” is an average of 100MB in size.

For any number that is estimated above, I tried to err on the side of caution so that if I was off it wouldn’t necessarily invalidate any conclusions.