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Category Archives: Investing

Luck Meets Perseverance: The Creation of IBM’s Competitive Advantage

On Monday October 28, 1929, the stock market took one of the worst single-day tumbles anyone alive might have seen, with the Dow Jones averages falling about 13%. The next day, October 29th, the market dropped yet again, a decline of 12%. By the end of the year, the Dow Jones average was down more than 45% from its high of 381. Market historians are familiar with the rest of the story: The sickening slide would not stop at 45%, but continue until 1932 to reach a low of 41 on the Dow, a decline of about 90% from peak to trough. American business was in a major Depression. But at least one businessman would decide that, like General Erwin Rommel would say years later, the path was not out, but through.

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International Business Machines, better known as IBM, was created from the ashes of the Computing-Tabulating-Recording Company (C-T-R) in 1917 by Thomas J. Watson, who’d learned his business skills at the National Cash Register Company (NCR). Before Watson’s reorganization of C-T-R, the company was basically in three businesses: computing scales (to weigh and compute the cost of a product being weighed), time clocks (to calculate and record wages), and tabulating machines (which used punch cards to add up figures and sort them). Watson’s first act of genius was to recognize that the future of IBM was not going to be time cards or scales, but in helping businesses do their work more effectively and with a lot less labor. He set out to do just that with his tabulating machines.

The problem was, IBM’s products weren’t yet all that different from its competitors’, and the company was small. IBM’s main tabulating product was the Hollerith machine, created by Herman Hollerith in Washington D.C. in 1890 to improve the Census tabulating process, of all things. (It sounds mundane, but he saved the government $5 million and did the work in about 1/8th of the time.) By the late 1910s, the Hollerith machine had a major competitor in the Powers Accounting Company, which had a similar product that was easier to use and more advanced than the Hollerith.

Hollerith_Punched_Card
Hollerith Punch Card

 

HollerithMachine.CHM
Hollerith Machine

 

Watson knew he had to push the research and development of his best product, and he did, hiring bright engineers like Fred Carroll from NCR, who would go on to be known for his Carroll Press, which allowed IBM to mass-produce the punch cards which did the “tabulating” in the pre-electronic days. By the mid 1920s, IBM had the lead. The plan was set in late 1927.

Watson then pointed to where he wanted IBM to go. ”There isn’t any limit for the tabulating business for many years to come,” he said. “We have just scratched the surface in this division. I expect the bulk of increased business to come from the tabulating end, because the potentialities are greater, and we have done so little in the way of developing our machines in this field.”

Underneath that statement lay a number of reasons—other than the thrill of new technology—why Watson zeroed in on the punch card business. When seen together, the reasons clicked like a formula for total domination. IBM would never be able to make sure it was the world leader in scales or time clocks, but it could be certain that it was the absolute lord of data processing.

[…]

Watson had no epiphanies. No voice spoke to him about the future of data processing. He didn’t have a grand vision for turning IBM into a punch card company. He got there little by little, one observation after another, over a period of 10 to 12 years.

(Source: The Maverick and his Machine)

Watson’s logical, one-foot-at-a-time approach was reminiscent of Sir William Osler’s dictum: Our main business is not to see what lies dimly at a distance, but to do what lies clearly at hand. And with a strategy of patenting its proprietary punch-cards, making them exclusively usable with IBM tabulators and sorters, IBM was one of the market darlings in the lead-up to 1929. Between 1927 and 1929 alone, IBM rose about four-fold on the back of 20-30% annual growth in its profits.

But it was still a small company with a lot of competition, and the punch card system was notoriously unreliable at times. He had a great system to hook in his customers, but the data processing market was still young — many businesses wouldn’t adopt it. And then came the fall.

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As the stock market dropped by the day and the Depression got on, the economy itself began to shrink in 1930. GDP went down 8% that year, and then another 7% the following year. Thousands of banks failed and unemployment would eventually test 30%, a figure that itself was misleading; the modern concept of “underemployment” hadn’t been codified, but if it had, it probably would have dwarfed 30%. An architect working as a lowly draftsman had a job, but he’d still fallen on hard times. Everyone had.

Tom Watson’s people wondered what was to become of IBM. If businesses didn’t have money, how could they purchase tabulators and punch cards? Even if it would save them money in the long run, too many businesses had cut their capital spending to the bone. The market for office spending was down 50% in 1930.

Watson’s response was to push. Hard. So hard that he’d take IBM right up to the brink.

IBM could beat the Depression, Watson believed. He reasoned that only 5 percent of business accounting functions were mechanized, leaving a huge market untapped. Surely there was room to keep selling machines, even in difficult times. Watson also reasoned that the need for IBM machines was so great, if businesses put off buying them now, certainly they’d buy them later, when the economy picked up. His logic told him that the pent-up demand would explode when companies decided to buy again. He wanted IBM to be ready to take advantage of that demand.

He’d keep the factories building machines and parts, stockpiling the products in warehouses. In fact, between 1929 and 1932, he increased IBM’s production capacity by one-third.

Watson’s greatest risk was running out of time. If IBM’s revenue dropped off or flattened because of the Depression, the company would still have enough money to keep operating for two years, maybe three. If IBM’s revenue continued to falter past 1933, the burden of running the factories and inventory would threaten IBM’s financial stability.

[…]

Watson’s logic led him to make what looked to outsiders like another insane wager. On January 12, 1932, Watson announced that IBM would spend $1 million—nearly 6 percent of its total annual revenue— to build one of the first corporate research labs. The colonial-style brick structure in Endicott would house all of IBM’s inventors and engineers. Watson played up the symbolism for all it was worth. He would create instead of destroy, despite the economic plague.

(Source: The Maverick and his Machine)

Most companies pulled back, and for good reason. Demand was rapidly shrinking, and IBM’s decision to spend money expanding productive capacity, research, and employment would be suicide if demand didn’t return soon. All of that unused capacity was costly and would go to waste. Watson took an enormous risk, but he also had faith that the American economy would recover its dynamism. If it did, IBM would come out on the other side untouchable.

Somehow, Watson had to stimulate demand. He had to come up with products that companies couldn’t resist, whatever the economic conditions. Again, thanks to Charles Kettering’s influence, Watson believed that R&D would drive sales. (ed: Kettering was chief engineer at General Motors.) So Watson decided to build a lab, pull engineers together, and get them charged up to push the technology forward.

Throughout the 1930s, IBM cranked out new products and innovation, finally getting its technology ahead of Remington Rand or any other potential competitors.

[…]

Within a few years, Watson’s gamble of manufacturing looked disastrous. As IBM pumped increasing amounts of money into operations and growth, revenue from 1929 to 1934 stalled, wavering between $17 million and $19 million a year. IBM edged toward insolvency. In 1932, IBM’s stock price fell to 1921 levels and stayed there—11 years of gains wiped out.

(Source: The Maverick and his Machine)

By 1935, IBM was still stagnating. Watson made the smart move to get out of the money-losing scale business and use the money to keep the remaining businesses afloat, but he was drowning in excess capacity, inventions be damned.

Then IBM got a stroke of luck that it would ride for almost 50 years.

After all of his pushing and all of his investment, after the impossible decision to push IBM to the brink, Tom Watson was rewarded with The Social Security Act of 1935, part of FDR’s New Deal. It was perfect.

No single flourish of a pen had ever created such a gigantic information processing problem. The act established Social Security in America—a national insurance system that required workers to pay into a fund while employed so they could draw payments out of it once they retired, or if a wage-earning spouse died. To make the system work, every business had to track every employee’s hours, wages, and the amount that must be paid to Social Security. The business then had to put those figures in a form that could be reported to the federal government. Then the government had to process all those millions of reports, track the money, and send checks to those who should get them.

Overnight, demand for accounting machines soared. Every business that had them needed them more. An officer for the store chain Woolworth told IBM that keeping records for Social Security was going to cost the company $250,000 a year. Businesses that didn’t have the machines wanted them. The government needed them by the boatload.

Only one company could meet the demand: IBM. It had warehouses full of machines and parts and accessories, and it could immediately make more because its factories were running, finely tuned, and fully staffed. Moreover, IBM had been funding research and introducing new products, so it had better, faster, more reliable machines than Remington Rand or any other company. IBM won the contract to do all of the New Deal’s accounting—the biggest project to date to automate the government…

This period of time became IBM’s slingshot. Revenue jumped from $19 million in 1934 to $21 million in 1935. From there it kept going up: $25 million in 1936, $31 million in 1937. It would climb unabated for the next 45 years. From that moment until the 1980s, IBM would utterly dominate the data processing industry—a record of leadership that was unmatched by any industrial company in history.

(Source: The Maverick and his Machine)

By combining aggressive opportunism and a great deal of luck, IBM was forged in the depths of the Great Depression. Like John D. Rockefeller before him, who bought up refineries during periods of depression in the oil industry, and Warren Buffett after him, who scooped up loads of cheap stocks when the stock market was crumbling in the 1970s, Watson decided that pushing ahead was the only way out.

History certainly didn’t have to go his way — FDR might not have been elected or might not have been able to enact Social Security. Even if he’d done it two years later, IBM still might never have made it.

But Watson’s courage and leadership did open the possibly of serendipitous fortune for IBM if the world didn’t end. Like oxygen combining with fuel to create internal combustion, those elements forged a monstrous competitive advantage when the match was finally lit.

Still Interested? Check out the excellent The Maverick and his Machine by Kevin Maney, where the excerpts above come from.

Second-Level Thinking: What Smart People Use to Outperform

“Experience is what you got when you didn’t get what you wanted.”
— Howard Marks

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Second Order Thinking

Successful decision making requires thoughtful attention to many separate aspects.

Decision making is as much art as science. The goal, if we have one, is not to make perfect decisions but rather to make better decisions than average. To do this we require either good luck or better insight. And since luck isn’t really much of a plan, we should probably focus on better insight.

In most of life you can get a step ahead of others by going to the gym or the library, or even a better school. In thinking, however, a lot of what you’d think gets you ahead is only window dressing.

Would be thinkers and deciders can attend the best schools, take the best courses and, if they are lucky, attach themselves to the best mentors. Yet only a few of them will achieve the skills and superior insight necessary to be an above average thinker. And we live in a world that, if it rewards anything, rewards better decisions. The rest is increasingly automated.

But how do we get there in a world where everyone else is also smart and well-informed? How do we get there in a world that is increasingly becoming computerized? You must find an edge. You must think differently.

In his exceptional book, The Most Important Thing, Howard Marks hits on the concept of second-level thinking.

First-level thinking is simplistic and superficial, and just about everyone can do it (a bad sign for anything involving an attempt at superiority). All the first-level thinker needs is an opinion about the future, as in “The outlook for the company is favorable, meaning the stock will go up.” Second-level thinking is deep, complex and convoluted.

Second-level thinkers take into account a lot of what we put into our decision journals. Things like, What is the range of possible outcomes? What’s the probability I’m right? What’s the follow-on? How could I be wrong?

The real difference for me is that first-level thinkers are the people that look for things that are simple, easy, and defendable. Second-level thinkers push harder and don’t accept the first conclusion.

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“It’s not supposed to be easy. Anyone who finds it easy is stupid.”
— Charlie Munger

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Marks writes:

First-level thinkers think the same way other first-level thinkers do about the same things, and they generally reach the same conclusions. By definition, this can’t be the route to superior results.

This is where things get interesting. Extraordinary performance comes from being different. It must be that way. Of course, below average performance comes from being different too — on the downside.

So it’s not enough to be different — you also need to be correct. “The problem is that extraordinary performance comes only from correct nonconsensual forecasts, but nonconsensual forecasts are hard to make, hard to make correctly and hard to act on,” writes Marks. The goal is not blind divergence but rather rather a way of thinking that sets you apart from others.

In short, you can’t do the same things that other people are doing and expect to outperform.

We can look at this as a simple two-by-two matrix (via The Most Important Thing).

Second Level Thinking Matrix

I’m generalizing a bit here, but if your thoughts and behavior are conventional, you’re likely to get conventional results. Steve Jobs was right.

This is where loss aversion comes in. Most people are simply unwilling to be wrong because that means they might look like a fool. Yet this is a grave mistake. The ability to risk looking like an idiot is necessary for being different. You never look like a fool if you look like everyone else. (Bringing to mind Keynes’ dictum: Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.) Only by doing — or, in our case, thinking — something different do you put yourself at risk.

JOHN MAYNARD KEYNES

Conventional thinking and behavior is safe. But it guarantees mediocrity. You need to know when your performance is likely to be improved by being unconventional.

Here’s a pro tip. If you want to have fun at work this week, do one of two things. First, start digging below the surface of people’s opinions. Ask them why they think what they think. Second, ask them to take the other side of the argument.

Investing: The Rules of the Road

Investing money can seem a little rudderless at times.

One day you hear that stocks are risky and the next that they’re indispensable. Some days it seems like stocks only go up and sometimes that they only go down. Real estate used to seem like an automatic path to wealth, and then the housing crisis hit. For the uninitiated, it sometimes seems there are no central truths. And unlike certain fields, we all have to deal with money. We can’t “opt out” from financial concerns unless we plan to live in a monastery, and it is useful for all of us to understand the basic ideas.

Investing is not a science but a craft, and a craftsman needs tools. In the case of investing, the tools are mostly mental. If we accumulate a few simple mental tools, we can start evaluating the claims of experts, salesmen, or simply well-intentioned friends.

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Decisions Under Uncertainty

"We confuse risk and uncertainty."
“We confuse risk and uncertainty.”

If you’re a knowledge worker you make decisions everyday. In fact, whether you realize it or not, decisions are your job.

Decisions are how you make a living. Of course not every decision is easy. Decisions tend to fall into different categories.  The way we approach the actual decision should vary based on category.

Here are a few basic categories that decisions fall into.

There are decisions where:

  1. Outcomes are known. In this case the range of outcomes is known and the individual outcome is also known. This is the easiest way to make decisions. If I hold out my hand and drop a ball, it will fall to the ground. I know this with near certainly.
  2. Outcomes are unknown, but probabilities are known.In this case the range of outcomes are known but the individual outcome is unknown. This is risk. Think of this as going to Vegas and gambling. Before you set foot at the table, all of the outcomes are known as are the probabilities of each. No outcome surprises an objective third party.
  3. Outcomes are unknown and probabilities are unknown. In this case the distribution of outcomes are unknown and the individual outcomes are necessarily unknown. This is uncertainty.

We often think we’re making decisions in #2 but we’re really operating in #3. The difference may seem trivial but it makes a world of difference.

Decisions Under Uncertainty

Ignorance is a state of the world where some possible outcomes are unknown: when we’ve moved from #2 to #3.

One way to realize how ignorant we are is to look back, read some old newspapers, and see how often the world did something that wasn’t even imagined.

Some examples include the Arab Spring, the collapse of the Soviet Union, the financial meltdown.

We’re prepared for a world much like #2 — the world of risk, with known outcomes and probability that can be estimated, yet we live in a world with a closer resemblance to #3.

Read part two of this series: Two types of ignorance.

References: Ignorance: Lessons from the Laboratory of Literature (Joy and Zeckhauser).

Will Bonner And Charlie Munger on Getting The Best Odds

In Mobs, Messiahs, and Markets: Surviving the Public Spectacle in Finance and Politics, Will Bonner writes:

…you don’t win by predicting the future; you win by getting the odds right. You can be right about the future and still not make any money. At the racetrack, for example, the favorite horse may be the one most likely to win, but since everyone wants to bet on the favorite, how likely is it that betting on the favorite will make you money? The horse to bet on is the one more likely to win than most people expect. That’s the one that gives you the best odds. That’s the bet that pays off over time.

And here is Charlie Munger speaking about the same topic:

The model I like to sort of simplify the notion of what goes o­n in a market for common stocks is the pari-mutuel system at the racetrack. If you stop to think about it, a pari-mutuel system is a market. Everybody goes there and bets and the odds change based o­n what’s bet. That’s what happens in the stock market.

Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so o­n and so on. But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2. Then it’s not clear which is statistically the best bet using the mathematics of Fermat and Pascal. The prices have changed in such a way that it’s very hard to beat the system.

And then the track is taking 17% off the top. So not only do you have to outwit all the other betters, but you’ve got to outwit them by such a big margin that on average, you can afford to take 17% of your gross bets off the top and give it to the house before the rest of your money can be put to work.