In 1973, the Washington Post Company couldn’t have been a more widely revered media company. The Watergate scandal, which Bob Woodward and Carl Bernstein begun reporting on in mid-1972, came to a spectacular end with President Nixon’s resignation in August 1974. But the reverence of the publication didn’t match the company’s popularity on Wall Street. The Post—along with many other stocks at that time—was trading at historic lows.

Below is the letter that Warren Buffett wrote to Katherine Graham in June 1973 after he had acquired over 5% of the stock. By the end of the year his stake had increased to 10%. The letter gives a lot of insight into how Buffett viewed the Post—not only as an investment, but as a business with noble purposes that brings out his sentimental side.

This purchase represents a sizable commit ment to us—and an explicitly quantified compliment to the Post as a business enterprise and to you as its chief executive. Writing a check separates conviction from conversation. I recognize that the Post is Graham-controlled and Graham-managed. And that suits me fine.

Some years back, a partnership which I managed made a significant investment in the stock of Walt Disney Productions. The stock was ridiculously cheap based upon earnings, asset values and capability of management. That alone was enough to make my pulse quicken (and pocketbook open), but there was also an important extra dimension to the investment. In its field, Disney simply was the finest—hands down. Anything that didn’t reflect his best efforts—anything that might leave the customer feeling short-changed—just wasn’t acceptable to Walt Disney. He melded energetic creativity with a discipline regarding profitability, and achieved something unique in entertainment.

I feel the same way about The Washington Post. The stock is dramatically undervalued relative to the intrinsic worth of its constituent properties, although that is true of many securities in today’s markets. But, the twin attraction to the undervaluation is an enterprise that has become synonymous for quality in communications. How much more satisfying it is going to be to watch an investment in the Post grow over the years than it would be to own stock in some garden variety company which, though cheap, had no sense of purpose.

I am additionally impressed by the sense of stewardship projected by your communications to fellow shareholders. They are factual, complete and interesting as you bring your established newspaper standards for integrity to the newer field of corporate reporting.

You may remember that I was in your office about two years ago with Charles Munger, discussing the New Yorker. At the time I mentioned to you that I had received my financial start delivering the Post while attending Woodrow Wilson High in the mid 1940’s. Although I delivered about 400 Posts per day, my record of loyalty is slightly tarnished in that I also had the Times-Herald route (much smaller—my customers were discriminating) in the Westchester. This was perhaps the first faint sign to keenly perceptive Washingtonians that the two organizations eventually would get together.

I should mention that Berkshire Hathaway has no radio or television properties, so that we will not be a complicating factor with the FCC. Our only communications property is the ownership of Sun Newspapers of Omaha, a group of financially (but not editorially) insignificant weekly newspapers in the metropolitan Omaha area. Last month our whole organization, seventy people counting printing, went into orbit when we won a Pulitzer for our reporting on Boys Town’s undisclosed wealth. Incidentally, Newsweek and Time used approximately equal space in covering the story last year, but Newsweek’s reporting job was far superior.

You can see that the Post has a rather fervent fan out in Omaha. I have hopes that, as funds become available, we will add to our holdings, at which time I will send along amended 13-D filings.

Cordially,
Warren E. Buffett

This letter was taken from Katherine Graham’s wonderful autobiography, Personal History.

Over time, species tend to move up the landscape to the nearest peak (A), where all future paths of variation lead downward. The peak that a genotype “settles” on is most likely to be a local optimum, which is not necessarily the highest peak in the landscape (a global optimum). This is because selection pushes fitness towards nearby peaks (what is called a basis of attraction), but lacks the foresight to select the highest peak.

To get to a higher peak, a species may have to reduce its fitness in the near term (C) as it slowly traverses across a valley in order to improve fitness in the long term. In order to make this shift, there has to be sufficient instability or challenge; otherwise, an organism will not opt to leave the intermediate peak and suffer the unknown prospects of the valley. If the valley is too low or the higher peak too far away, it may be unreachable as the low fitness hurdle can’t be overcome. (An example is the lack of wheeled animals, which although beneficial is inaccessible due to the valley of low fitness genotypes around it.)

Evolution usually moves in small steps, but occasionally it takes wild leaps—a single mutation might give a creature an extra pair of legs or another radically different feature. Most of the time these leaps result in much lower fitness (B), and therefore don’t last. But other times it allows the genotype to jump to a higher peak without the slow process of going down before going up.

Every landscape has different terrain that can be on a scale from flat to rugged. A rugged or coarse landscape has many local peaks and deep valleys, while a flat landscape has only very small hills (all genotypes have about the same success rates).

Landscapes don’t remain static—they shift over time due to either environmental changes or adjustments as organisms move across it. The movement can vary from being stable (relatively flat and slow to change) to roiling (likely rugged and changing quickly). Given the likelihood of ever-shifting landscapes, the evolutionary mix of small steps and occasional wild leaps is the best possible way to adapt to the environment.

It’s a compilation of every letter Warren Buffett wrote to the shareholders of Berkshire Hathaway. I first created it a few years ago for myself and friends. Last year I got Buffett’s endorsement — plus a few non-public letters — to publish the book for the benefit of fans and shareholders of Berkshire.

Here is the official page with all the details. There you can find a more detailed description, plus some sample pages and a chart detailing the performance of Berkshire’s insurance operations. (For any programmers out there, the chart was created with D3. You can check out the development version on GitHub.)

Features of the book:

• Berkshire Hathaway annual shareholder letters from 1965 to 2012 (706 pages), including the 11 earliest letters not available on Berkshire’s website
• Tabulated letter years so you can easily flip to the desired letter
• Topics index
• Company index
• Person index
• Charts of:
  • The growth in Berkshire’s book value and market price relative to benchmarks
  • Insurance float and performance
  • The operating businesses of Berkshire

The entire book is paginated, and has easy-to-flip-to labels for each letter’s year.

It is available for pre-order now. The first batch will be sold at the Berkshire Hathaway Annual Meeting on May 4 in the convention center. The rest of the copies will be available on Amazon on May 7.

Future projects

• The obvious next step is to publish a digital version, easily readable on iPads or potentially Kindles. This is normally an easy transfer, but that’s not the case with this book due to the many tables that have to be converted. So no timeline on this but it will be forthcoming.
• A book of letters to the partners of Buffett Partnership, Ltd., Buffett’s hedge fund he ran from 1957 to 1970. This will be a similar format to the Berkshire book, with indexes, page numbers, etc.

When we were designing our blue box, we wrote a lot of custom programs to help us design it, you know, and to do a lot of the dog work for us in terms of calculating master frequencies with subdivisors to get other frequencies and things like that. We used the computer quite a bit to calculate, you know, to calculate how much error we would get in the frequencies and how much could be tolerated.

So we used them in our work, but much more importantly, it had nothing to do with using them for anything practical. It had to do with using them to be a mirror of your thought process; to actually learn how to think.

I think the greatest value of learning how to—I think everybody in this country should learn how to program a computer—should learn a computer language, because it teaches you how to think. It’s like going to law school. I don’t think anybody should be a lawyer, but I think going to law school would actually be useful, because it teaches you how to think in a certain way, in the same way that computer programming teaches you in a slightly different way how to think. And so I view computer science as a liberal art.

You know, throughout the years in business I found something, which was I’d always ask why you do things. And the answers you invariably get are “Oh, that’s just the way it’s done.” Nobody knows why they do what they do. Nobody thinks about things very deeply in business. That’s what I found. I’ll give you an example. When we were building our Apple I’s in the garage we knew exactly what they cost.

When we got into a factory in the Apple II days, the accounting had this notion of a “standard cost”—where you’d kind of set a standard cost and at the end of a quarter you’d adjust it with a “variance.” And I kept asking, “Well, why do we do this?” And the answer was, “Well, that’s just the way it’s done.” And after about six months of digging into this, what I realized was, the reason you do it is because you don’t really have good enough controls to know how much it costs, so you guess. And then you fix your guess at the end of the quarter.

And the reason you don’t know how much it costs is because your information systems aren’t good enough. But nobody said it that way. And so later on when we designed this automated factory for Macintosh, we were able to get rid of a lot of these antiquated concepts and know exactly what something cost to the second.

So in business, a lot of things are—I call it “folklore.” They’re done because they were done yesterday and the day before. And so what that means is if you’re willing to sort of ask a lot of questions and think about things and work really hard, you can learn business pretty fast. It’s not the hardest thing in the world.

The customer rearely buys what the company thinks it is selling him. — Peter Drucker

Google Glass was finally announced to the public yesterday.

Glass is a solution looking for problems. It’s too hard to say what jobs-to-be-done Glass will be hired to do at this stage, or how widely used it will be. We’ll only know after it’s released.

The lean startup way of thinking heavily emphasizes the reverse sequence: find a problem (job), think of a way(s) to solve that problem, test your hypothesis using a minimum viable product, repeat. This method should work for most startups. It worked well for companies like Microsoft (Problem: I need an Operating System to put on the computers I sell so people can use them. Solution: Build/Buy/Copy Basic/DOS/Windows).

But there are some innovations where the solution=>problem sequence is necessary — anything that requires a lot of R&D and isn’t easily demo’ed on a large scale. Google Glass, Tesla cars, Segway, iPad, Lytro, etc. These are physical, more capital intensive examples, but the same still holds for some smaller software projects. Sometimes you just need to build the full version to see what it’s best used for.

One of the problems of this method is what I call the “Segway Paradox“: a new technology with huge initial interest and possibilities turns out to only be used in a few niche cases.

This may happen for a number of reasons (see Paul Graham’s The Trouble with the Segway). I think Google Glass may fall prey to this problem.

There are a few use cases I can think of that may make Glass worth the cost:

1. Hands-free sports — biking, skiing, football, climbing
2. Search & rescue, emergency — alerting the user to visual/audio anomalies
3. Jobs that require detailed visual instructions (“advanced checklists”)

But it seems from the videos that Google is focusing more on everyday consumer uses, competing more with smartphones.

Re-posted from the Atlastory blog.

In Nassim Taleb’s new book “Antifragile,” there’s an interesting segment about how an entire system can be antifragile (benefiting from variability / disorder / stressors) precisely because its individual parts remain fragile (harmed by variability). A few examples:

The engineer and historian of engineering Henry Petroski presents a very elegant point. Had the Titanic not had that famous accident, as fatal as it was, we would have kept building larger and larger ocean liners and the next disaster would have been even more tragic. So the people who perished were sacrificed for the greater good; they unarguable save more lives than were lost. . . . Every plane crash brings us closer to safety, improves the system, and makes the next flight safer.

Thankfully the errors we encounter while developing Atlastory don’t involve anyone dying. But the same principle applies — every bug, problem, server crash, chokepoint, or design flaw we encounter leads to a better system. We want to run into problems, because that means we know about them and can now fix them — eventually making the user experience better as a result.

“Some businesses love their own mistakes,” Taleb continues. “Reinsurance companies, who focus on insuring catastrophic risks . . . manage to do well after a calamity . . . All they need is to keep their mistakes small enough so they can survive them.”

The more you benefit from low-downside mistakes, the more “antifragile” your business is. I see this as a function of both the industry you’re in and the internal culture of the company.

If everyday work and life is viewed as a science experiment (the circle of observe > guess > test > interpret), then any screw-ups or failures are a good thing in the end. You know something’s wrong, and you can work on fixing it. Taleb again: “…every attempt becomes more valuable, more like an expense than an error. And of course you make discoveries along the way.”

Continual improvement is everyday life in software development, but it is only just catching on for personal development.

Luck is a chance occurrence that affects a person or a group (e.g., a sports team or a company). Luck can be good or bad. Furthermore, if it is reasonable to assume that another outcome was possible, then a certain amount of luck is involved. In this sense, luck is out of one’s control and unpredictable. Randomness and luck are related, but there is a useful distinction between the two. You can think of randomness as operating at the level of a system and luck operating at the level of the individual. Luck is a residual: it’s what is left over after you’ve subtracted skill from an outcome.

The definition of skill depends on how much luck there is in the activity. In activities allowing little luck, you acquire skill through practice of physical or cognitive tasks. In activities incorporating a large dose of luck, skill is best defined as a process of making decisions. Here, a good process will have a good outcome but only over time. Patience, persistence, and resilience are all elements of skill.

Separating luck and skill

At the heart of making this distinction lays the issue of feedback. On the skill side, feedback is clear and accurate, because there is a close relationship between cause and effect. Feedback on the luck side is often misleading because cause and effect are poorly correlated in the short run.

In most cases, characterizing what’s going on at the extremes is not too hard. As an example, you can’t predict the outcome of a specific fair coin toss or payoff from a slot machine. They are entirely dependent on chance. On the other hand, the fastest swimmer will almost always win the race. The outcome is determined by skill, with luck playing only a vanishingly small role.

On the far right of the luck-skill continuum are activities that rely purely on skill and are not influenced by luck. Physical activities such as running or swimming races would be on this side, as would cognitive activities such as chess or checkers. Marion Tinsley, the greatest player of checkers, could win all day long, and luck played no part in it. He was simple better than everyone else.

On the far left are activities that depend on luck and involve no skill, like roulette or the lottery. Also on the left side are things like investing, business strategy, and poker. It doesn’t mean that skill doesn’t exist in those activities. It does. It means that we need a large number of observations to make sure that skill can overcome the influence of luck.

There is a quick and easy way to test whether an activity involves skill: ask whether you can lose on purpose. In games of skill, it’s clear that you can lose intentionally, but when playing roulette or the lottery you can’t lose on purpose.

Reversion to the mean

Consider the relationship between where the activity is on the luck-skill continuum and the size of the sample you are measuring. A small number of results tell you very little about what’s going on when luck dominates. And in an activity where the results are nearly all skill, you don’t need a large sample to draw reasonable conclusions.

An understanding of where an activity is on the luck-skill continuum allows you to estimate the likely rate of reversion to the mean, and vice versa. Any activity that combines skill and luck will eventually revert to the mean. If what happens is mostly the result of skill, then reversion to the mean is scant and slow.

The position of the activity on the continuum defines how rapidly the outcome goes toward an average value, that is, the rate of reversion to the mean. In activities that are all luck, there is complete reversion to the mean. So if you estimate the rate of reversion to the mean, you can help place an activity on the luck-skill continuum.

Estimated true average = Grand average + shrinking factor (observed average – grand average)

The shrinking factor is the rate of reversion to the mean. For activities that are all skill, the shrinking factor is 1.0, which means that the best estimate of the next outcome is the prior outcome. For activities that are all luck, the shrinking factor is 0, which means that the expected value of the next outcome is the mean of the distribution of luck. So we can assign a shrinking factor to a given activity according to where that activity lies on the continuum.

Francis Galton’s insight was that reversion to the mean and correlation are two elements of the same concept. So in the above equation, shrinking factor usually approximates correlation. A high correlation suggests weak reversion to the mean. The most obvious hazard is that correlations are not stable in many fields. In unstable and nonlinear activities, relying on past correlations won’t work in the long run.

Placing Activities by Answering Three Questions

1. Cause and effect. First, ask if you can easily assign a cause to the effect you see. In some activities, the relationship of cause and effect is clear. You can repeat behavior and get the same result. If you can easily identify the cause of a given effect, you’re most likely on the skill side of the continuum.

Consider two elements of a manufacturing business. The first is the actual manufacturing process. This is an activity that falls near the all-skill side. A proper process using statistical control yields a favorable outcome a very high percentage of the time. The second element is simply deciding which products to manufacture. We call this strategy, and even a well-conceived strategy can fail catastrophically. So even with the same company, some activities will rely mostly on skill and others on luck.

2. The rate of reversion. Very simply, what is the rate of reversion to the mean? To answer this question you need some way to measure performance.

3. Where prediction is useful. Where can we predict well? In other words, where are experts useful? Areas that have high predictability include engineering, some areas of medicine, and games such as chess and checkers. Experts are notoriously poor at predicting the outcomes of political, social, and economic systems. Complex adaptive systems effectively obscure cause and effect. You can’t make predictions in any but the broadest and vaguest terms.

Variance (observed) = Variance (skill) + Variance (luck)
Skill = Observed outcome – luck

If you can calculate the standard deviation of the observed outcomes in an activity, and what the standard deviation would look like if luck were the only factor, you can estimate the relative amount of skill and luck involved (luck/observed = amount of luck involved).

Performance measurements

Useful statistics have two features. (1) Persistence, which means what happens in the present is similar to what happens in the past. This is also called reliability—if luck is more important, than you would expect the reliability to be low. (2) Predictiveness of the goal you seek. This is also called validity, meaning it is valid to conclude that one measurement causes another. It compares two values. In basketball, an example would be percentage of shots made and total points scored.

Statisticians asses persistence and predictive value by examining the coefficient of correlation. The process of determining which statistics are useful begins with a definition of your objective. Knowing your objective is important because it’s hard to chart a course without knowing the destination. Next, you have to determine what factors contribute to achieving your objective.

You can plot a measurement on a chart that has the luck-skill continuum as the horizontal axis and the predictive value on the vertical axis. The most predictive and persistent measurements are best. (Examples include on-base percentage for baseball, or active share for investing.)

But while a skill is persistent, it is not always the case that persistence reveals skill. A careful consideration of what elements of performance are within the command of an individual or a sports team is essential.

In October they raised $2.3 million from Canaan Partners and Khosla Ventures. Below is a  a very brief analysis if I were considering a potential investment in Instacart.

Quick analysis

So basically Instacart uses software (algorithms & data analysis on the back-end, with good UI design on the front-end) to connect “deliverers” in need of cash with “buyers” who need quick delivery of basic items.

Opportunity: arbitraging the demand for instant satisfaction and convenience, using software + crowdsourcing. This will be disrupting convenience stores on the low-end, and potentially grocery stores in the future. It is taking advantage of the trends in mobile computing, data analysis, and e-commerce (willingness to trust online vendors).

Potential moats: brand habit developed through repeated purchases. Learning curve — should remain ahead of competition on the learning curve because of technology (software) advantage. This is a business where it pays to have lots of data on: customer habits, traffic, prices, store traffic, etc. It is a virtuous circle: the learning curve reinforces customer experience, which improves the brand. These advantages are all geographically local, so it will be best to roll out to new cities as quickly as possible once the kinks are worked out.

Management: with only doing minimal due diligence with public information on the founders, I didn’t see any red flags. Apoorva Mehta has worked on the Amazon supply chain, so he has some experience in the business. All founders on the surface seem to be very talented. What am I looking for? Amar Bhide found that the most important traits for the founders of a typical startup are the dichotomies of: (1) seeking uncertainty while being risk averse; and (2) persevering while being adaptable.

What could go wrong: (1) other cities are not as receptive to the concept; (2) Amazon or other grocery company catches on and preempts their growth in new cities.

Investment edge: structural (not very many participants at this early stage) and psychological (grocery delivery has failed many times in the past, sometimes spectacularly — Webvan — investors are turned off by the concept because of these past failures).

Final note

This seems like a company with a good future ahead of it. That usually makes it a good investment, especially at this stage. I’m not sure what the valuation of the company is at the moment. But for a startup at this stage, the precise valuation you invest at isn’t usually as important as how well the company does (within limits, of course — refer to the internet bubble).

Disclosure: I have no ownership in Instacart.

References:

Crunchbase: Instacart
Mobile first, desktop second…
I Trusted a Total Stranger to Buy My Groceries…
Instacart Bags $2.3M To Become Amazon of Groceries
How Instacart Hacked YC

1895: Milwaukee City Hall, Milwaukee, USA (353 ft.)

1899: Park Row Building, NYC, USA (391 ft.)

1901: Philadelphia City Hall, Philadelphia, USA (511 ft.)

1908: Singer Building, NYC, USA (612 ft.) (to the left in the picture; Woolworth is in the background in 1913)

1909: Met Life Tower, NYC, USA (700 ft.)

1913: Woolworth Building, NYC, USA (792 ft.)

1930: Chrysler Building, NYC, USA (927 ft.)

1931: Empire State Building, NYC, USA (1,250 ft.)

1972: World Trade Center, NYC, USA (1,368 ft.)

1974: Sears Tower, Chicago, USA (1,450 ft.)

2004: Taipei 101, Taipei, Taiwan (1,474 ft.)

2008: Shanghai World Financial Center, Shanghai, China (1,599 ft.) (the one that looks like a bottle opener)

2010: Burj Khalifa, Dubai, UAE (2,717 ft.)

Related:

Pyramids vs. Skyscrapers

Vikram Mansharamani: Towering Indicators