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Peter Thiel

Through notes from Peter Thiel's CS183: Startup class at Stanford University, we have a unique window into the mind of the venture capitalist and hedge fund manager. He's fascinated with human nature, and integrates what he learned from his former career as a chess master into his lectures. 

Chess is a contained universe: there are only 32 pieces on the board and 64 squares those pieces can occupy. But starting up a company takes much more than raw intellectual ability; it requires what Thiel calls "The Mechanics of Mafia," or the understanding of complex human dynamics. Linking the two worlds is Thiel's passion. Here are some of the chess concepts he highlighted in his class, thanks to notes from one of his former students, Blake Masters

Know the relative value of your pieces: 

In chess, the queen is the most valuable piece on the board. In the standard valuation system, it is given a 9, whereas the rook (5), bishop (3), knight (3), and pawn (1) are lower. In his lecture Value Systems, Thiel mentions Guy Kawasaki’s equation on how to assess the value of a company based on the types of people you have:

Pre-money valuation = ($1M x Number of Engineers) – ($500k x Number of MBAs).

So engineers are more valuable pieces than MBAs.

From his lecture If You Build It, Will They Come? Thiel points out that within any group, there is a wide range of talent. This goes for engineering as much as it goes for sales. “Engineering is transparent … It is fairly easy to evaluate how good someone is. Are they a good coder? An ubercoder? Things are different with sales. Sales isn’t very transparent at all. We are tempted to lump all salespeople in with vacuum cleaner salesmen, but really there is a whole set of gradations. There are amateurs, mediocrities, experts, masters, and even grandmasters.”

“But if you don’t believe that sales grandmasters exist, you haven’t met Elon [Musk]. He managed to get $500m in government grants for building rockets, which is SpaceX, and also for building electric cars, which is done by his other company, Tesla.”

The take-away lesson: Just like with chess pieces, people are not of equal value when it comes to your organization. You must be able to accurately assess their value. And within any field there are amateurs, mediocrities, experts, masters, and grandmasters.

Know how your pieces work best together: 

In his lecture The Mechanics of Mafia Thiel discusses two personality types: “nerds” and “athletes.”  “Engineers and STEM people tend to be highly intelligent, good at problem solving, and naturally non zero-sum. Athletes tend to be highly-motivated fighters; you only win if the other guy loses.” A company made up of only athletes will be biased toward competing. A company made up of only nerds will ignore the situations where you have to fight. “So you have to strike the right balance between nerds and athletes.”

The take-away lesson: You need some athletes to protect your nerds when it’s time to fight.

Know the phases of the game and have a plan:

In chess, there are three phases: the opening, the middle game and the end game.

From his lecture Value Systems Thiel notes: “People often talk about ‘first mover advantage.’ But focusing on that may be problematic; you might move first then fade away. The danger there is that you simply aren’t around to succeed, even if you do end up creating value. More important than being the first mover is the last mover. You have to be durable. In this one particular at least, business is like chess.  Grandmaster Jose Raul Capablanca put it very well: to succeed ‘you must study the endgame before anything else.’”

From his lecture War and Peace: “A good intermediate lesson in chess is that even a bad plan is better than no plan at all. Having no plan is chaotic. And yet people default to no plan.”

Take away lesson: Moving first isn’t always an advantage. Think about poker. If you’re the last to bet, you have the most information. The endgame is where the most decisive moves are made. Study it and make sure you’re around at the right time to make your move. Have a plan.

Talent matters; there is more to success than luck: 

In chess, talent clearly matters. In business and life, both talent and luck matter.

From his lecture You Are Not A Lottery Ticket, Thiel said that “when we know that someone successful is skilled, we tend to discount that or not talk about it. There’s always a large role for luck. No one is allowed to show how he actually controlled everything.”

In his lecture If You Build It, Will They Come? Thiel explained that "since the best people tend to make the best companies, the founders or one or two key senior people at any multimillion-dollar company should probably spend between 25 percent and 33 percent of their time identifying and attracting talent.”

Take away lesson: Some people hold more value and control more resources than you realize. Invest your time in finding those talented people for your organization.

Chess is a brutal mental game. So is life. Make your moves carefully. 

According to chess grandmaster Danny King's interview with 60 Minutes, “Chess is a really brutal game. I think because it’s so contained. It’s all going on in the head. And if you lose to your opponent, you feel stupid. You can call someone all the names under the sun, but if you call someone stupid, that’s the worst thing you can say to another human being. And that’s a bit what it feels like when you lose a game of chess. It’s all intellectual.”

Take away lesson: In the words of King: “You can’t take your moves back. Once you play your move you could be stepping into some horrible trap.”

© 2012 by Jonathan Wai

You can follow me on TwitterFacebook, or G+. Read my Psychology Today blog Finding the Next Einstein: Why Smart is Relative here.

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http://blakemasters.tumblr.com/peter-thiels-cs183-startup

This link is to a summary of Peter Thiel's class topics written in superb essay style. It centers entirely around technology, how a new supplier (an entrepreneur) brings it to the people, and how the creative process navigates the modern world.

The words are far-reaching and truly align to the best of humanity and our future potential.

Peter Thiel "gets it"

Purpose and Preamble

We might describe our world as having retail sanity, but wholesale madness. Details are well understood; the big picture remains unclear. A fundamental challenge—in business as in life—is to integrate the micro and macro such that all things make sense.

Humanities majors may well learn a great deal about the world. But they don’t really learn career skills through their studies. Engineering majors, conversely, learn in great technical detail. But they might not learn why, how, or where they should apply their skills in the workforce. The best students, workers, and thinkers will integrate these questions into a cohesive narrative. This course aims to facilitate that process.

I. The History of Technology

For most of recent human history—from the invention of the steam engine in the late 17th century through about the late 1960’s or so— technological process has been tremendous, perhaps even relentless. In most prior human societies, people made money by taking it from others. The industrial revolution wrought a paradigm shift in which people make money through trade, not plunder.

The importance of this shift is hard to overstate. Perhaps 100 billion people have ever lived on earth. Most of them lived in essentially stagnant societies; success involved claiming value, not creating it. So the massive technological acceleration of the past few hundred years is truly incredible.

The zenith of optimism about the future of technology might have been the 1960’s. People believed in the future. They thought about the future. Many were supremely confident that the next 50 years would be a half-century of unprecedented technological progress.

But with the exception of the computer industry, it wasn’t. Per capita incomes are still rising, but that rate is starkly decelerating. Median wages have been stagnant since 1973. People find themselves in an alarming Alice-in-Wonderland-style scenario in which they must run harder and harder—that is, work longer hours—just to stay in the same place. This deceleration is complex, and wage data alone don’t explain it. But they do support the general sense that the rapid progress of the last 200 years is slowing all too quickly.

II. The Case For Computer Science

Computers have been the happy exception to recent tech deceleration. Moore’s/Kryder’s/Wirth’s laws have largely held up, and forecast continued growth. Computer tech, with ever-improving hardware and agile development, is something of a model for other industries. It’s obviously central to the Silicon Valley ecosystem and a key driver of modern technological change. So CS is the logical starting place to recapture the reins of progress.

III. The Future For Progress

A. Globalization and Tech: Horizontal vs. Vertical Progress

Progress comes in two flavors: horizontal/extensive and vertical/intensive. Horizontal or extensive progress basically means copying things that work. In one word, it means simply “globalization.” Consider what China will be like in 50 years. The safe bet is it will be a lot like the United States is now. Cities will be copied, cars will be copied, and rail systems will be copied. Maybe some steps will be skipped. But it’s copying all the same.

Vertical or intensive progress, by contrast, means doing new things. The single word for this is “technology.” Intensive progress involves going from 0 to 1 (not simply the 1 to n of globalization). We see much of our vertical progress come from places like California, and specifically Silicon Valley. But there is every reason to question whether we have enough of it. Indeed, most people seem to focus almost entirely on globalization instead of technology; speaking of “developed” versus “developing nations” is implicitly bearish about technology because it implies some convergence to the “developed” status quo. As a society, we seem to believe in a sort of technological end of history, almost by default.

It’s worth noting that globalization and technology do have some interplay; we shouldn’t falsely dichotomize them. Consider resource constraints as a 1 to n subproblem. Maybe not everyone can have a car because that would be environmentally catastrophic. If 1 to n is so blocked, only 0 to 1 solutions can help. Technological development is thus crucially important, even if all we really care about is globalization.

B. The Problems of 0 to 1

Maybe we focus so much on going from 1 to n because that’s easier to do. There’s little doubt that going from 0 to 1 is qualitatively different, and almost always harder, than copying something n times. And even trying to achieve vertical, 0 to 1 progress presents the challenge of exceptionalism; any founder or inventor doing something new must wonder: am I sane? Or am I crazy?

Consider an analogy to politics. The United States is often thought of as an “exceptional” country. At least many Americans believe that it is. So is the U.S. sane? Or is it crazy? Everyone owns guns. No one believes in climate change. And most people weigh 600 pounds. Of course, exceptionalism may cut the other way. America is the land of opportunity. It is the frontier country. It offers new starts, meritocratic promises of riches. Regardless of which version you buy, people must grapple with the problem of exceptionalism. Some 20,000 people, believing themselves uniquely gifted, move to Los Angeles every year to become famous actors. Very few of them, of course, actually become famous actors. The startup world is probably less plagued by the challenge of exceptionalism than Hollywood is. But it probably isn’t immune to it.

C. The Educational and Narrative Challenge

Teaching vertical progress or innovation is almost a contradiction in terms. Education is fundamentally about going from 1 to n. We observe, imitate, and repeat. Infants do not invent new languages; they learn existing ones. From early on, we learn by copying what has worked before.

That is insufficient for startups. Crossing T’s and dotting I’s will get you maybe 30% of the way there. (It’s certainly necessary to get incorporation right, for instance. And one can learn how to pitch VCs.) But at some point you have to go from 0 to 1—you have to do something important and do it right—and that can’t be taught. Channeling Tolstoy’s intro to Anna Karenina, all successful companies are different; they figured out the 0 to 1 problem in different ways. But all failed companies are the same; they botched the 0 to 1 problem.

So case studies about successful businesses are of limited utility. PayPal and Facebook worked. But it’s hard to know what was necessarily path-dependent. The next great company may not be an e-payments or social network company. We mustn’t make too much of any single narrative. Thus the business school case method is more mythical than helpful.

D. Determinism vs. Indeterminism

Among the toughest questions about progress is the question of how we should assess a venture’s probability of success. In the 1 to n paradigm, it’s a statistical question. You can analyze and predict. But in the 0 to 1 paradigm, it’s not a statistical question; the standard deviation with a sample size of 1 is infinite. There can be no statistical analysis; statistically, we’re in the dark.

We tend to think very statistically about the future. And statistics tells us that it’s random. We can’t predict the future; we can only think probabilistically. If the market follows a random walk, there’s no sense trying to out-calculate it.

But there’s an alternative math metaphor we might use: calculus. The calculus metaphor asks whether and how we can figure out exactly what’s going to happen. Take NASA and the Apollo missions, for instance. You have to figure out where the moon is going to be, exactly. You have to plan whether a rocket has enough fuel to reach it. And so on. The point is that no one would want to ride in a statistically, probabilistically-informed spaceship.

Startups are like the space program in this sense. Going from 0 to 1 always has to favor determinism over indeterminism. But there is a practical problem with this. We have a word for people who claim to know the future: prophets. And in our society, all prophets are false prophets. Steve Jobs finessed his way about the line between determinism and indeterminism; people sensed he was a visionary, but he didn’t go too far. He probably cut it as close as possible (and succeeded accordingly).

The luck versus skill question is also important. Distinguishing these factors is difficult or impossible. Trying to do so invites ample opportunity for fallacious reasoning. Perhaps the best we can do for now is to flag the question, and suggest that it’s one that entrepreneurs or would-be entrepreneurs should have some handle on.

E. The Future of Intensive Growth

There are four theories about the future of intensive progress. First is convergence; starting with the industrial revolution, we saw a quick rise in progress, but technology will decelerate and growth will become asymptotic.

Second, there is the cyclical theory. Technological progress moves in cycles; advances are made, retrenchments ensue. Repeat. This has probably been true for most of human history in the past. But it’s hard to imagine it remaining true; to think that we could somehow lose all the information and know-how we’ve amassed and be doomed to have to re-discover it strains credulity.

Third is collapse/destruction. Some technological advance will do us in.

Fourth is the singularity where technological development yields some AI or intellectual event horizon.

People tend to overestimate the likelihood or explanatory power of the convergence and cyclical theories. Accordingly, they probably underestimate the destruction and singularity theories.

IV. Why Companies?

If we want technological development, why look to companies to do it? It’s possible, after all, to imagine a society in which everyone works for the government. Or, conversely, one in which everyone is an independent contractor. Why have some intermediate version consisting of at least two people but less than everyone on the planet?

The answer is straightforward application of the Coase Theorem. Companies exist because they optimally address internal and external coordination costs. In general, as an entity grows, so do its internal coordination costs. But its external coordination costs fall. Totalitarian government is entity writ large; external coordination is easy, since those costs are zero. But internal coordination, as Hayek and the Austrians showed, is hard and costly; central planning doesn’t work.

The flipside is that internal coordination costs for independent contractors are zero, but external coordination costs (uniquely contracting with absolutely everybody one deals with) are very high, possibly paralyzingly so. Optimality—firm size—is a matter of finding the right combination.

V. Why Startups?

A. Costs Matter

Size and internal vs. external coordination costs matter a lot. North of 100 people in a company, employees don’t all know each other. Politics become important. Incentives change. Signaling that work is being done may become more important than actually doing work. These costs are almost always underestimated. Yet they are so prevalent that professional investors should and do seriously reconsider before investing in companies that have more than one office. Severe coordination problems may stem from something as seemingly trivial or innocuous as a company having a multi-floor office. Hiring consultants and trying to outsource key development projects are, for similar reasons, serious red flags. While there’s surely been some lessening of these coordination costs in the last 40 years—and that explains the shift to somewhat smaller companies—the tendency is still to underestimate them. Since they remain fairly high, they’re worth thinking hard about.

Path’s limiting its users to 150 “friends” is illustrative of this point. And ancient tribes apparently had a natural size limit that didn’t much exceed that number. Startups are important because they are small; if the size and complexity of a business is something like the square of the number of people in it, then startups are in a unique position to lower interpersonal or internal costs and thus to get stuff done.

The familiar Austrian critique dovetails here as well. Even if a computer could model all the narrowly economic problems a company faces (and, to be clear, none can), it wouldn’t be enough. To model all costs, it would have to model human irrationalities, emotions, feelings, and interactions. Computers help, but we still don’t have all the info. And if we did, we wouldn’t know what to do with it. So, in practice, we end up having companies of a certain size.

B. Why Do a Startup?

The easiest answer to “why startups?” is negative: because you can’t develop new technology in existing entities. There’s something wrong with big companies, governments, and non-profits. Perhaps they can’t recognize financial needs; the federal government, hamstrung by its own bureaucracy, obviously overcompensates some while grossly undercompensating others in its employ. Or maybe these entities can’t handle personal needs; you can’t always get recognition, respect, or fame from a huge bureaucracy. Anyone on a mission tends to want to go from 0 to 1. You can only do that if you’re surrounded by others to want to go from 0 to 1. That happens in startups, not huge companies or government.

Doing startups for the money is not a great idea. Research shows that people get happier as they make more and more money, but only up to about $70,000 per year. After that, marginal improvements brought by higher income are more or less offset by other factors (stress, more hours, etc. Plus there is obviously diminishing marginal utility of money even absent offsetting factors).

Perhaps doing startups to be remembered or become famous is a better motive. Perhaps not. Whether being famous or infamous should be as important as most people seem to think it is highly questionable. A better motive still would be a desire to change the world. The U.S. in 1776-79 was a startup of sorts. What were the Founders motivations? There is a large cultural component to the motivation question, too. In Japan, entrepreneurs are seen as reckless risk-takers. The respectable thing to do is become a lifelong employee somewhere. The literary version of this sentiment is “behind every fortune lies a great crime.” Were the Founding Fathers criminals? Are all founders criminals of one sort or another?

C. The Costs of Failure

Startups pay less than bigger companies. So founding or joining one involves some financial loss. These losses are generally thought to be high. In reality, they aren’t that high.

The nonfinancial costs are actually higher. If you do a failed startup, you may not have learned anything useful. You may actually have learned how to fail again. You may become more risk-averse. You aren’t a lottery ticket, so you shouldn’t think of failure as just 1 of n times that you’re going to start a company. The stakes are a bit bigger than that.

A 0 to 1 startup involves low financial costs but low non-financial costs too. You’ll at least learn a lot and probably will be better for the effort. A 1 to n startup, though, has especially low financial costs, but higher non-financial costs. If you try to do Groupon for Madagascar and it fails, it’s not clear where exactly you are. But it’s not good.

VI. Where to Start?

The path from 0 to 1 might start with asking and answering three questions. First, what is valuable? Second, what can I do? And third, what is nobody else doing?

The questions themselves are straightforward. Question one illustrates the difference between business and academia; in academia, the number one sin is plagiarism, not triviality. So much of the innovation is esoteric and not at all useful. No one cares about a firm’s eccentric, non-valuable output. The second question ensures that you can actually execute on a problem; if not, talk is just that. Finally, and often overlooked, is the importance of being novel. Forget that and we’re just copying.

The intellectual rephrasing of these questions is: What important truth do very few people agree with you on?

The business version is: What valuable company is nobody building?

These are tough questions. But you can test your answers; if, as so many people do, one says something like “our educational system is broken and urgently requires repair,” you know that that answer is wrong (it may be a truth, but lots of people agree with it). This may explain why we see so many education non-profits and startups. But query whether most of those are operating in technology mode or globalization mode. You know you’re on the right track when your answer takes the following form:

“Most people believe in X. But the truth is !X.”

Make no mistake; it’s a hard question. Knowing what 0 to 1 endeavor is worth pursuing is incredibly rare, unique, and tricky. But the process, if not the result, can also be richly rewarding.

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Tags: cs183

This is only the first of 11 sections generously written by Blake Masters, the site's creator.

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