Tag Archives: Power law

The Power Law and Venture Capital – according to Sebastian Mallaby

Sebastian Mallaby has just published a new book about Venture Capital which looks very interesting. I have already explained here what the Power Law is and will not do it again. But I will quote Mallaby as I do when I read good books.

About the term : “Venture capital” had also cropped up in 1938 when Lammont du Pont, the president of E. I. du Pont de Nemours & Company spoke before the US Senate Committee to Investigate Unemployment and Relief. “By Venture Capital I mean that capital which will go into an enterprise and not expect an immediate return, but will take its chances on getting an ultimate return” du Pont clarified. […] but this phrase making did not stick and the term was not widely used until at least the 60s. [Note 28 page 418]

And what about this : “All progress depends upon the unreasonable man, the creatively maladjusted. Most people think improbable ideas are unimportant, but the only thing that’s important is something that’s improbable”. From Vinod Khosla [Page 3]

About the return of VC. The normal distribution applies to size, weight of individuals, traditional stock markets, but the power law applies to the exceptional – wealth of individuals when not really regulated, as well as venture capital: Like the 7-foot NBA star, unexpected large price jumps are rare enough and moderate enough that they do not affect the average. The S&P500 budged less than 3% in 7763 days out of 7817 between 1985 and 2015, that is 98% of the time. […] Now consider venture capital. Hosley Bridge is an investment company which had stakes in venture funds that backed 7,000 startups between 1985 and 2014. A small subset of these deals, accounting for just 5% of the capital deployed generated fully 60 percent of all the Hosley Bridge returns. [Page 8]

Examples of Khosla’s deals [Page 10]

Startup Investment Return Multiple
Juniper Networks $5M $7B 1,400
Siara A few $M $1,5B >150
Cerent $8M Bought for $7B

About predictions: The revolutions that will matter – the big disruptions that create wealth for inventors [and investors! HL note] and anxiety for workers, or that scramble the geopolitical balance and alter human relations – cannot be predicted based on extrapolations of past data, precisely because such revolutions are so thoroughly disruptive. Rather, they will emerge as a result of forces that are too complex to forecast – from the primordial soup of tinkerers and hackers and hubristic dreamers – and all you can know is that the world in ten years will be excitingly different. […] the future can be discovered by means of iterative, venture-backed experiments. It cannot be predicted. [Page 11] “I always tell my CEOs, don’t plan. Keep testing the assumptions and iterating” Khosla again. [Note 32, page 416] All this of course reminds me also about the Black Swan.

Why is venture capital so different from other sources of finance? Most financiers allocate scarce capital based on quantitative analysis. venture capitalists meet people, charm people, and seldom bother with spreadsheets [*]. Most financiers value companies by projecting their cash flows. Venture capitalists frequently back startups before they have cash flows to analyze. Other financiers trade millions of dollars of paper assets in the blink of an eye. Venture capitalists take relatively small stakes in real companies and hold them. Most fundamentally, other financiers extrapolate trends from the past, disregarding the risk of extreme “tail” events. Venture capitalists look for radical departures from the past. Tail events are all they care about. [Page 14]

[*] Academic survey work confirms that one in five venture capitalists do not even attempt to forecast cash flows when making an investment decision. [Note 36 page 416]

All this is from the introductory chapter only and I liked it very much. Maybe more soon but in the mean time, you can always have a look at my visual history of venture capital.

Return on Investments – IRR & multiples

In venture capital, returns on investments is the ultimate metric and although it is not very difficult to understand, there are many little tricks worth knowing about!

The reason of this short post is a recent article my friend Fuad advised me to read from the Financial Times : The parallel universe of private equity returns by Jonathan Ford. If you are not a subsciber to the FT (and I am not), you may not be able to read the article so here are short extracts: “Ever wondered about the extraordinary performance figures that listed private equity firms trumpet in their official stock market filings? […] Not only do the firms generate stratospheric numbers — far higher than anything produced by the boring old stock market — but they can apparently do it year in, year out, with no decay in returns. […] The reality is that these consistent IRRs show nothing of the kind. What they actually demonstrate is a big flaw in the way the IRR itself is calculated.”

When I looked at venture capital (VC) returns in the past, I learned you must carefully look at what IRR means. It looks simple at first sight as the next table shows, just simple math:

So the first question you care about is what matters: IRRs or multiples? And my simple answer is “it depends”. Up to you!

Secondly, measuring returns makes a lot of sense when you have your money back. Of course! But IRR and multiples can also be measured while you are still invested and when your investment is not liquid, which is the case for private companies in which invests private equity (PE) – venture capital belongs to PE. You can have a look at a former post of mine, Is the Venture Capital model broken? and among other figures look at this:

The VC performance according to the Kauffman foundation

The peak IRR is measured when your assets are not liquid whereas the final IRR is when you have your money back… A fund as usually a 10-year life (or 120 months) and you can check the peak IRR month.

Even more tricky, the money is called by periods to make the holding as short as possible: basically, when the money is needed to invest, though you commit to it for the full life of the fund. Measuring the real IRR begins to be complicated but what matters to me is the multiple from the day of commitment to the finaldah when the money is back… And to you?

A final point I love to mention all the time is that VC is not so much about a portfolio of balanced investments. In the same post mentioned above, I added two links, and one of the best quote is “Venture capital is not even a home run business. It’s a grand slam business.”

Have a look at The Babe Ruth Effect in Venture Capital or In praise of failure. VC statistics are not gaussian, they follow a power law:

When Peter Thiel talks about Start-ups – part 5: a vision of the future of technology

I am still not sure how Thiel’s class notes on start-ups will finish, but they are more and more fascinating, class after class. At least his vision of this world is.

Class 14 is about cleantech and energy. “Alternative energy and cleantech have attracted an enormous amount of investment capital and attention over the last decade. Almost nothing has worked as well as people expected. The cleantech experience can thus be quite instructive. […] To think about the future of energy, we can use the [another] matrix. The quadrants shake out like this:
Determinate, optimistic: one specific type of energy is best, and needs to be developed
Determinate, pessimistic: no technology or energy source is considerably better. You have what you have. So ration and conserve it.
Indeterminate, optimistic: there are better and cheaper energy sources. We just don’t know what they are. So do a whole portfolio of things.
Indeterminate, pessimistic: we don’t know what the right energy sources are, but they’re likely going to be worse and expensive. Take a portfolio approach.”

Both for energy and transportation, Thiel’s fills his quadrants with interesting examples:

and he adds: “Petroleum has dominated transportation. Coal has dominated in power generation. […] Typically a single source dominates at any given time. There is a logical reason for this. It doesn’t make sense that the universe would be ordered such that many different kinds of energy sources are almost exactly equal. Solar is very different from wind, which is very different from nuclear. It would be extremely odd if pricing and effectiveness across all these varied sources turned out to be virtually identical. So there’s a decent ex ante reason why we should expect to see one dominant source. This can be framed as a power law function. Energy sources are probably not normally distributed in cost or effectiveness. There is probably one that is dramatically better than all others.”

But the analysis explaining the cleantech bubble were far from clear. “One problem was that people were ambiguous on what was scarce or problematic. Was there resource scarcity? Or were the main problems environmental?” […] “To have a successful startup, you must have good answers—or at least a good plan for getting those answers.” Answers to many issues such as
– the market
– the secrets
– the team and its culture
– the funding
and unfortunately many mistakes were made.

Regarding the market, there was the issue of both explaining how to become a leader of one segment (PV, wind,…) and why a segment was better. Regarding the secret: “If you want to start a company, you should have some important secret. But in practice, most wind, solar, and cleantech ventures relied on incremental improvements.” Even worse, “most cleantech companies in the last decade have had shockingly non-technical teams and cultures. Culture defaulted toward zero-sum competition. Savvy observers would have seen the trouble coming when cleantech people started wearing suits and ties. Tech people and computer people wear t-shirts and jeans. Cleantech people, by contrast, looked like salesmen. And indeed they were. This is not a trivial point. If you’re dealing in something that’s incremental and of questionable durability, you actually have to be a really good salesman to convince people that it’s dramatically better.” Finally “a good, broad rule of thumb is to never invest in companies who are looking for less than $1 million or more than $1 billion. If companies can do everything they want for less than a million dollars, things may be a little too easy. There may be nothing that is very hard to build, and it’s just a timing game. On the other extreme, if a company needs more than a billion dollars to be successful, it has to become so big that the story starts to become implausible.”

If Thiel were to bet on soemthing, it would apparently be Thorium as a nuclear fuel.

Class 15 is about other future bets.


Thiel is a strong believer in contrarian (and sometimes huge) bets. He is interested in or at least puzzled by transportation, robotics, weather and energy storage. And his way of choosing is to look at what did not work (yet) in the past: “Various VC firms in Silicon Valley warned expressed concern about [investing in unique technologies]. They warned us that investing in SpaceX was risky and maybe even crazy. And this wasn’t even at the very early stage. […] (Danielle Fong:) People like to act like they like being disruptive and taking risks. But usually it’s just an act. They don’t mean it. Or if they do, they don’t necessarily have the clout within the partnership to make it happen. (Peter Thiel:) It is very hard hard for investors to invest in things that are unique. The psychological struggle is hard to overstate. People gravitate to the modern portfolio approach. The narrative that people tell is that their portfolio will be a portfolio of different things. But that seems odd. Things that are truly different are hard to evaluate. […] The upside to doing something that you’re unfamiliar with, like rockets, is that it’s likely that no one else is familiar with it, either. The competitive bar is lowered. You can focus on learning and substantive things over process, which is perhaps better than competing against experts.”

Class 16 is about maybe the highest of all bets: life and death.

I have not so far mentioned the sentence which comes at the top of each series of class notes: “Your mind is software. Program it. Your body is a shell. Change it. Death is a disease. Cure it. Extinction is approaching. Fight it.”

The problem.

“Like death itself, modern drug discovery is probably too much a matter of luck. Scientists start with something like 10,000 different compounds. After an extensive screening process, those 10,000 are reduced to maybe 5 that might make it to Phase 3 testing. Maybe 1 makes it through testing and is approved by the FDA. It is an extremely long and fairly random process. This is why starting a biotech company is usually a brutal undertaking. Most last 10 to 15 years. There’s little to no control along the way. What looks promising may not work. There’s no iteration or sense of progress. There is just a binary outcome at end of a largely stochastic process. You can work hard for 10 years and still not know if you’ve just wasted your time.

To be fair, we must acknowledge that all the luck-driven, stats-driven processes that have dominated people’s thinking have worked pretty well over the last few decades. But that doesn’t necessarily mean that indeterminacy is sound practice. Its costs may be rising quickly. Perhaps we’ve found everything that is easy to find. If so, it will be hard to improve armed with nothing but further random processes. This is reflected in escalating development costs. It cost $100 million to develop a new drug in 1975. Today it costs $1.3 billion. Probably all life sciences investment funds have lost money. Biotech investment has been roughly as bad a cleantech.”

The perspectives.

“Drug discovery is fundamentally a search problem. The search space is extremely big. There are lots of possible compounds. An important question is thus whether we can use computer technology to reduce scope of luck. Can Computer Science make biotech more determinative?”

“These are big secrets that play out over long time horizons, not web apps that have a 6-week window to take over the world.”

“The sequencing of the genome is like the first packets being sent over ARPANET. It’s a proof of concept. This technology is happening, but it isn’t yet compelling. So there is a huge market if one can make something compelling enough for people to actually go and get a genome sequenced. It’s like e-mail or word processing. Initially these things were uncomfortable. But when they become demonstrably useful, people leave their comfort zones and adopt them.”

“Biotech got quite a burst in late 70s early 80s, with new recombinant DNA and molecular biology techniques. Genentech led the way from the late 70s to the early 80s. Nine of the 10 biggest American biotech companies were founded during this really short time. Their technology came out some 7-8 years later. And that was the window; not very many integrated biotech companies have emerged since then. There was a certain amount of stuff to find. People found it. And before Genentech, the paradigm was pharma, not biotech. That window (becoming an integrated pharmaceutical company) had been closed for about 30 years before Genentech. So the bet is that while the traditional biotech window may be closed, the comp bio window is just opening.”

“There’s really no rush to spill the secret plans. This space is very much unlike fast-moving consumer Internet startups. Here, if you have something unique, you should nurse it.”

“Slow iteration is not law of nature. Pharma and biotech usually move very slowly, but both have moved pretty fast at times. From 1920-1923 Insulin moved at the speed of software. Today, platforms like Heroku have greatly reduced iteration times. The question is whether we can do that for biotech. Nowhere is it written in stone that you can’t go from conception to market in 18 months. That depends very much on what you’re doing. Genentech was founded the same year as Apple was, in 1976. Building a platform and building infrastructure take time. There can be lots of overhead. Ancillary things can take longer than a single product lifecycle to accumulate. [… the] VC is broken with respect biotech. Biotech VCs have all lost money. They usually have time horizons that are far too short. VCs that say they want biotech tend to really want products brought to market extremely quickly. “Integrated drug platform” is an ominous phrase for VCs. More biotech VCs are focused on globalization than on real technical innovation. VCs typically found a company around a single compound and then pour a bunch of money into it to push it through the capital-intensive trial process. Most VCs not interested in multi-compound companies doing serious pre-clinical research.”

And as a conclusion of class 16, “Startups are always hard at the start. There are futons and ironing boards in the office. You have to rush to clean up for meetings. But maybe the hardest thing is just to get your foundation right and make sure you plan to build something valuable. You don’t have to do a science fair project at the start. You just have to do your analytical homework and make sure what you’re doing is valid. You have to give yourself the best chance of success as things unfold in the future.”

Class 17 is about the brain, artificial intelligence, maybe the last frontier in technology, certainly going further than the previous topics addressed here.


Not much more to add except maybe the short description of the approach by 3 start-ups:
Vicarious is trying to build AI by develop algorithms that use the underlying principles of the human brain. They believe that higher-level concepts are derived from grounded experiences in the world, and thus creating AI requires first solving a human sensory modality.
Prior Knowledge (acquired by Salesforce since Thiel’s class) is taking a different approach to building AI. Their goal is less to emulate brain function and more to try to come up with different ways to process large amounts of data. They apply a variety of Bayesian probabilistic techniques to identifying patterns and ascertaining causation in large data sets. In a sense, it’s the opposite of simulating human brains.
– The big insight at Palantir (…) isn’t regression analysis, where you look at what was done in the past to try to predict what’s going to be next. A better approach is more game theoretic. Palantir’s framework is not fundamentally about AI, but rather about intelligence augmentation.

And one more comment: “For the most part, academics aren’t (working on strong AI or crazy things) because their incentive structure is so weird. They have perverse incentive to make only marginally better things. And most private companies aren’t working on it because they’re trying to make money now.(…) Bold claims also require extraordinary proof. If you’re pitching a time machine, you’d need to be able to show incremental progress before anyone would believe you. Maybe your investor demo is sending a shoe back in time. That’d be great. You can show that prototype, and explain to investors what will be required to make the machine work on more valuable problems. It’s worth noting that, if you’re pitching a revolutionary technology as opposed to an incremental one, it is much better to find VCs who can think through the tech themselves. When Trilogy was trying to raise their first round, the VCs had professors evaluate their approach to the configurator problem. Trilogy’s strategy was too different from the status quo, and the professors told the VCs that it would never work. That was an expensive mistake for those VCs. When there’s contrarian knowledge involved, you want investors who have the ability to think through these things on their own.”

End of part 5!

When Peter Thiel talks about Start-ups – part 4: it’s customer, stupid!

Thiel’s classes 9 to 12 leave the pure field of start-ups to the higher levels of economy, business and innovation. Thiel gives general advice such that customers are important and more important than competitors with the recurring “obsession” that peace and correlated monopoly  are better than war and deadly competition.


Class 9 is about customers and more specifically how to find them. “People say it all the time: this product is so good that it sells itself. This is almost never true. […] The truth is that selling things is not a purely rational enterprise. There is much stranger stuff at work here. […] Most engineers underestimate the sales side of things because they are very truth-oriented people. In engineering, something either works or it doesn’t. […] Engineering is transparent. […] Sales isn’t very transparent at all. (As a side comment, I advise again to read Packer about transparency and politics in SV, in fact look at what follows!) […] A good analogy to the engineer vs. sales dynamic is experts vs. politicians. If you work at a big company, you have two choices. You can become expert in something. The other choice is to be a politician. […] The really good politicians are much better than you think. Great salespeople are much better than you think. But it’s always deeply hidden. In a sense, probably every President of the United States was first and foremost a salesman in disguise.

Thiel loves quadrants but does not draw one here, he just explains it:
– Product sells itself, no sales effort. Does not exist.
– Product needs selling, no sales effort. You have no revenue.
– Product needs selling, strong sales piece. This is a sales-driven company.
– Product sells itself, strong sales piece. This is ideal.

Thiel has similar views on marketing “Advertising is tricky in the same way that sales is” and he uses the famous quote: “Half the money I spend on advertising is wasted: the trouble is I don’t know which half.” Sales follow a power law similar to the one existing in value creation. Viral marketing rarely works… and viral marketing requires that the product’s core use case must be inherently viral.

In his class 10, Thiel begins to explore the future and shows how difficult it is to identify opportunities. He even mentions the nice quote (but never said in reality) “everything that can be invented has been invented” (falsely) attributed to U.S. Patent Commissioner Charles H. Duell in 1899. Again both in terms of technology innovation (vs. computers) or globalization (vs. China), he advises not to compete but to collaborate.

But the worst competitor is time… “More interesting are cases where people are right about the future and just wrong on timing. […]And being too early is a bigger problem for entrepreneurs than not being correct. It’s very hard to sit and just wait for things to arrive. It almost never works.” Andreessen who was Thiel’s guest approved: “For entrepreneurs, timing is a huge risk. You have to innovate at the right time. You can’t be too early. This is really dangerous because you essentially make a one-time bet. It’s rare are to start the same company five years later if you try it once and were wrong on timing. Jonathan Abrams did Friendster but not Facebook.

And Andreessen also agrees about sales: “The number one reason that we pass on entrepreneurs we’d otherwise like to back is focusing on product to the exclusion of everything else. We tend to cultivate and glorify this mentality in the Valley. We’re all enamored with lean startup mode. Engineering and product are key. There is a lot of genius to this, and it has helped create higher quality companies. But the dark side is that it seems to give entrepreneurs excuses not to do the hard stuff of sales and marketing. Many entrepreneurs who build great products simply don’t have a good distribution strategy. Even worse is when they insist that they don’t need one, or call no distribution strategy a viral marketing strategy.”

Again about timing: “You can go wrong in a few ways. One is that the future is too far away […] It’s like surfing. The goal is to catch a big wave. If you think a big wave is coming, you paddle really hard. Sometimes there’s actually no wave, and that sucks. But you can’t just wait to be sure there’s a wave before you start paddling. You’ll miss it entirely. You have to paddle early, and then let the wave catch you. The question is, how do you figure out when the next big wave is likely to come?”

A few not related topics:
– You need to find the balance that lets you think about patents least. It’s basically a distracting regulatory tax.
– What’s ideal is to have a founder/CEO who is a product person. Sales operators handle the sales force. Larry Ellison [is no exception and] is a product guy.
– Being CEO is a learnable skill. With the “world class” CEO model, you miss out on Microsoft, Google, and Facebook. The CEOs of those companies, of course, turned out to be excellent. But they were also the product people who built the companies. [Do not misunderstand] everybody thinks management is a bunch of idiots, and that engineers must save the day by doing the right things on the side. That’s not right. Management is extremely important. Great management and a great product person running the company is characteristic of the very best companies.

Class 11 is also about the future, but in terms of “secrets” which may be important to know how to identify real opportunities: “Some secrets are small and incremental. Others are very big. The focus should be on the secrets that matter: the big secrets that are true. The big ones so far have involved monopoly vs. competition, the power law, and the importance of distribution. “Capitalism and competition are antonyms.” That is a secret; it is an important truth, and most people disagree with it.”

Thiel explains why secrets are important: “Four primary things have been driving people’s disbelief in secrets.
– First is the pervasive incrementalism in our society. People seem to think that the right way to go about doing things is to proceed one very small step at a time. […] Academics are incented by volume, not importance. The goal is to publish lots of papers, each of which is, in practice at least, new only in some small incremental way. […]
– Second, people are becoming more risk-averse. People today tend to be scared of secrets. They are scared of being wrong. Of course, secrets are supposed to be true. But in practice, what’s true of all secrets is that there is good chance they’re wrong. If your goal is to never make mistake in your life, you should definitely never think about secrets. Thinking outside the mainstream will be dangerous for you. […]
– Third is complacency. There’s really no need to believe in secrets today. Law school deans at Harvard and Yale give the same speech to incoming first year students every fall: “You’re set. You got into this elite school. […]
– Finally, some pull towards egalitarianism is driving us away from secrets. We find it increasingly hard to believe that some people have important insight into reality that other people do not. Prophets have fallen out of fashion. Having visions of the future is seen as crazy. In 1939 Einstein sent a letter to President Roosevelt urging him to get serious about nuclear power and atomic weaponry. Roosevelt read it and got serious. Today, such a letter would get lost in the White House mailroom.”

But… “There is no straightforward formula that can be used to find secrets.”

Class 12 is about war and peace again, but I do not have much to comment here except a quote from Reid Hoffman: “A side note on invention and innovation: when you have an idea for a startup„ consult your network. Ask people what they think. Don’t look for flattery. If most people get it right away and call you a genius, you’re probably screwed; it likely means your idea is obvious and won’t work. What you’re looking for is a genuinely thoughtful response. Fully two thirds of people in my network thought LinkedIn was stupid idea. These are very smart people. They understood that there is zero value in a social network until you have a million users on it. But they didn’t know the secret plans that led us to believe we could pull it off. And getting to the first million users took us about 460 days. Now we grow at over 2 users per second.” Peaceful secrets are safer than competing for known things.

When Peter Thiel & Friends talk about Start-ups – part 3: company culture, founders, team, investors

Part 3 of my series of comments about Thiel’s class notes at Stanford mainly cover his Class 5-8. But first I should add that Thiel invited a “honor class” of innovators during his 19 classes. Quite fascinating!

Thiel-Friends-CS1st row: Stephen Cohen, co-founder and Executive VP of Palantir Technologies,
Max Levchin, co-founder PayPal and Slide,
Roelof Botha, partner at Sequoia Capital and former CFO of PayPal,
2nd row: Paul Graham, partner and co-founder of Y Combinator,
Bruce Gibney, partner at Founders Fund,
Marc Andreessen, general partner Andreessen Horowitz,
3rd row: Reid Hoffman, co-founder of LinkedIn,
Danielle Fong, Co-founder and Chief Scientist of LightSail Energy,
Jon Hollander, Business Development at RoboteX,
4th row: Greg Smirin, COO of The Climate Corporation,
Scott Nolan, Principal at Founders Fund and former aerospace engineer at SpaceX,
(Elon Musk was going to come, but he was busy launching rockets),
5th row: Brian Slingerland. Co-Founder, President & COO at Stem CentRx,
Balaji S. Srinivasan, CTO of Counsyl,
Brian Frezza, Co-founder, Emerald Therapeutics,
6th row: D. Scott Brown, co-founder of Vicarious,
Eric Jonas, CEO of Prior Knowledge,
Bob McGrew, Director of Eng, Palantir,
7th row: Sonia Arrison, Associate Founder of Singularity University,
Michael Vassar, the Singularity Institute for the study of Artificial Intelligence (SIAI),
Aubrey de Grey, Chief Science Officer at the SENS Foundation.

Thiel covered how to build a company from the ideas and vision of founders, through hiring and sometimes funding from investors. But he began with a critical though fuzzy concept, the company culture: “A robust company culture is one in which people have something in common that distinguishes them quite sharply from rest of the world.”

He mentions also some important dimensions of the culture:
– Consultant-nihilism or Cultish Dogmatism: “You want to be somewhere in the middle of that spectrum. To the extent you gravitate towards an extreme, you probably want to be closer to being a cult than being an army of consultants.” which could be why Thiel said earlier,
pre-money valuation = ($1M*n_engineers) – ($500k*n_MBAs).
– To Fight or Not To Fight (i.e. Nerds or Athletes or again Zero-sum and Non zero-sum). “So you have to strike the right balance between nerds and athletes. Neither extreme is optimal. Consider a 2 x 2 matrix. On the y-axis you have zero-sum people and non zero-sum people. On the x-axis you have warring, competitive environments and then you have peaceful, monopoly/capitalist environments. The optimal spot on the matrix is monopoly capitalism with some tailored combination of zero-sum and non zero-sum oriented people. You want to pick an environment where you don’t have to fight. But you should bring along some good fighters to protect your non zero-sum people and mission, just in case.”
I was just told this is crytic… I agree… another reason to read Thiel directly!

Foundings are obviously temporal. But how long they last can be a hard question. The typical narrative contemplates a founding, first hires, and a first capital raise. But there’s an argument that the founding lasts a lot longer than that. The idea of going from 0 to 1—the idea of technology—parallels founding moments. The 1 to n of globalization, by contrast, parallels post-founding execution. It may be that the founding lasts so long as a company’s technical innovation continues. Founders should arguably stay in charge as long as the paradigm remains 0 to 1. Once the paradigm shifts to 1 to n, the founding is over. At that point, executives should execute.”

Max Levchin: The notion that diversity in an early team is important or good is completely wrong. You should try to make the early team as non-diverse as possible. There are a few reasons for this. The most salient is that, as a startup, you’re underfunded and undermanned. It’s a big disadvantage; not only are you probably getting into trouble, but you don’t even know what trouble that may be. Speed is your only weapon. All you have is speed. […] How to hire? A specific application of this is the anti-fashion bias. You shouldn’t judge people by the stylishness of their clothing; quality people often do not have quality clothing. Which leads to a general observation: Great engineers don’t wear designer jeans. So if you’re interviewing an engineer, look at his jeans. There are always exceptions, of course. But it’s a surprisingly good heuristic. […] PayPal also had a hard time hiring women. An outsider might think that the PayPal guys bought into the stereotype that women don’t do CS. But that’s not true at all. The truth is that PayPal had trouble hiring women because PayPal was just a bunch of nerds! They never talked to women. So how were they supposed to interact with and hire them?

“No CEO should be paid more than $150k per year” (in Silicon Valley)
“Another important insight is that people must either be fully in the company or not in it at all.”

Dilution and funding
Building a valuable company is a long journey. A key question to keep your eye on as a founder is dilution. The Google founders had 15.6% of the company at IPO. Steve Jobs had 13.5% of Apple when it went public in the early ‘80s. Mark Pincus had 16% of Zynga at IPO. If you have north of 10% after many rounds of financing, that’s generally a very good outcome. Dilution is relentless. The alternative is that you don’t let anyone else in. It’s worth remembering that many successful businesses are built like this. Craigslist would be worth something like $5bn if it were run more like a company than a commune. GoDaddy never took funding. Trilogy in the late 1990s had no outside investors. Microsoft very nearly joined this club; it took one small venture investment just before its IPO. When Microsoft went public, Bill Gates still owned an astounding 49.2% of the company. So the question to think about with VCs isn’t all that different than questions about co-founders and employees. Who are the best people? Who do you want—or need—on board?

The VC model in a nutshell: a power law. “To a first approximation, a VC portfolio will only make money if your best company investment ends up being worth more than your whole fund. (And the investment in the second best company is about as valuable as number three through the rest.)”

I have not yet read the following classes…

The Black Swan and the danger of statistics

“Thought is only a flash in the middle of a long night. But this flash means everything.”
Henri Poincaré*

When I talked to friends and colleagues about The Black Swan (“BS”), they were surprised about my interest in the movie with Natalie Portman. I cannot say, I have not watched it. I was talking about Nassem Nicholas Taleb’s book and theory. Some other friends classified at it as American b… s…, these superficial books that give advice on anything and that seem to always become bestsellers; my colleagues would classify it as airport literature, not to be read in academic circles.

I read it and enjoyed it, but I have to admit Taleb is sometimes painful. Is it because he was so much frustrated by I do not know whom or what or is it because he is so proud of his certainties? I am not sure. But his ideas are certainly worth thinking about more than a minute. (Whereas you forget about airport American b… s… after 30 seconds). So back to the BS.

You’ll find great accounts of his book or of his theory, e.g.
– Nassim Taleb’s “The Black Swan” by Andrew Gelman,
– The Wikipedia page on the Black Swan theory
– or even another essay by Taleb, the Fourth Quadrant,
so I will not try to do the same.

However defining the Black Swan might be useful! In the Fourth Quadrant, Taleb writes the following:

There are two classes of probability domains—very distinct qualitatively and quantitatively. The first, thin-tailed: Mediocristan”, the second, thick tailed Extremistan. Before I get into the details, take the literary distinction as follows: In Mediocristan, exceptions occur but don’t carry large consequences. Add the heaviest person on the planet to a sample of 1000. The total weight would barely change. In Extremistan, exceptions can be everything (they will eventually, in time, represent everything). Add Bill Gates to your sample: the wealth will jump by a factor of >100,000. So, in Mediocristan, large deviations occur but they are not consequential—unlike Extremistan. Mediocristan corresponds to “random walk” style randomness that you tend to find in regular textbooks (and in popular books on randomness). Extremistan corresponds to a “random jump” one. The first kind I can call “Gaussian-Poisson”, the second “fractal” or Mandelbrotian (after the works of the great Benoit Mandelbrot linking it to the geometry of nature). But note here an epistemological question: there is a category of “I don’t know” that I also bundle in Extremistan for the sake of decision making—simply because I don’t know much about the probabilistic structure or the role of large events. Black Swans are the unknown deviations in Extremistan.

Here are more notes taken while reading.

[Page xxii] The black swan is characterized by “rarity, extreme impact and retrospective (though not prospective) predictability” (with additional footnote: the occurrence of a highly improbably event is the equivalent of the nonoccurrence of a highly probably one.

[Page 8] The human mind suffers from 3 aliments:
-The illusions of understanding, or how everyone thinks he knows what is going on in a world that is more complicated (or random) than they realize;
-the retrospective distortion, or how we can assess matters only after the fact, as if they were in a rearview mirror; and
-the overvaluation of factual information and the handicap of authoritative and learned people – when they platonify.

[Page 15] While in the past a distinction had been between drawn Mediterranean and non- Mediterranean (i.e., between the olive oil and the butter), in the 1970s, the distinction suddenly became between Europe and non-Europe.

[Page 54] There is a major difference and often-made mistake between no evidence of something and the evidence of its non-occurence (mental bias.)

[Page 77] The answer is that there are two varieties of rare events: a) the narrated Black Swans, those that are present in the current discourse and that you are likely to hear about on television, and b) those nobody talks about, since they escape models – those that you would feel ashamed discussing in public because they do not seem plausible. I can safely say that it is entirely compatible with human nature that the incidences of Black Swans would be overestimated in the first case, but severely underestimated in the second one.

[Page 80] One death is a tragedy; a million is a statistic. […] We have two systems of thinking. System 1 is experiential, effortless, automatic, fast, and opaque. System 2 is thinking, reasoned, local, slow, serial, progressive. Most mistakes come from using system 1 when we think we use system 2.

[Page 140] We overestimate what we know and underestimate uncertainty. Another bias, ”think about how many people divorce. Almost all of them are acquainted with the statistic that between one-third and one-half of all marriages fail, something the parties involved did not forecast while tying the know. Of course, “not us” because “we get along so well” (as if others tying the know got along poorly.)”

[Page 174-179] Poincaré is a central personality of Taleb’s theory, in particular through the 3-body problem. According to Taleb, “Poincaré angrily disparages the use of the bell curve.” Now the next figure simply illustrates the concept of sensitivity to initial conditions.


Operation 1: imagine an ice cube and consider how it may melt.
Operation 2: consider a puddle of water. Try to reconstruct the shape of the ice-cube.
The forward process is generally used in physics and engineering, the backward process in nonrepeatable, nonexperimental historical approaches. And the backward is much more complex to analyze.

[Page 198] While in theory it is an intrinsic property. In practice, randomness is incomplete information. Nonpractitioners do not understand the subtlety. A true random process does not have predictable properties. A chaotic system has entirely predictable properties, but they are hard to know.
a) There are no functional differences in practice between the two since we will never get to make the distinction.
b) The mere fact that a person is talking about the difference implies he has never made a meaningful decision under uncertainty – which is why he does not realize that they are indistinguishable in practice.
Randomness in practice, in the end, is just unknowledge. The world is opaque and appearances fool us.

[Page 204] Trial and error means trying a lot. In the Blind Watchmaker, Richard Dawkins brilliantly illustrates this notion of the world without grand design, moving by small incremental random changes. Note a slight disagreement on my part that does not change the story by much: the world, rather moves by large incremental random changes. Indeed, we have psychological and intellectual difficulties with trial and error and with accepting that series of small failures are necessary in life. “You need to love to lose”. In fact the reason I felt immediately at home in America is precisely because American culture encourages the process of failure, unlike the cultures of Europe and Asia where failure is met with stigma and embarrassment.
[It’s really Taleb writing and not the blog’s author, but I fully agree !]

[Page 207] When you have a very limited loss, you need to be as aggressive as speculative and sometimes as unreasonable as you can be. Middlebrow thinkers sometimes make the analogy with lottery tickets. It is plain wrong. First lottery tickets do not have a scalable payoff. Second, lottery tickets have known rules.

The economics of superstars

[Page 24] Who is this book written for? You need to understand who your audience is and amateurs write for themselves, professionals write for others. [This irony of the author’s is stimulating. I experienced it, I’m an amateur. But are the masterpieces not then written by amateurs? The Black Swans (The Lord of the Rings, Harry Potter) look often like a work of amateurs. The Yevgenia Krasnova example provided by Taleb is also stimulating]

[Page 214] Someone who is marginally better can easily win the entire pot. The problem is the notion of “better.” People take from the poor to give to the rich. An initial advantage follows someone through life and keep getting cumulative advantages. Failure is also cumulative. The advent of modern media has accelerated these cumulative advantages. The sociologist Pierre Bourdieu noted a link between the increased concentration of success and the globalization of culture and economic life.

[Page 221] Taleb claims new comers mitigate the cumulative advantages. “of the five hundred largest US companies in 1957, only seventy-four were still part of that select group, the S&P 500, forty year later. Only a few hundred had disappeared in mergers; the rest either shrank or went bust.

Actors who win an Oscar tend to live on average five years longer than their peers who don’t. People live longer in societies that have flatter social gradients.

[Page 277] What is poorly understood is the absence of a role for the average in intellectual production. The disproportionate share of the very few in intellectual influence is even more unsettling than the unequal distribution of wealth- unsettling because, unlike the income gap, no social policy can eliminate it. Communism could conceal or compress income discrepancies, but it could not eliminate the superstar system in intellectual life. [I am not sure]


Taleb defines himself as a skeptic and his mentor are Hayek and Popper. He links it with humility in the following: [Page 190] Someone with a low degree of epistemic arrogance is not too visible, like a shy person at a cocktail party. We are not predisposed to respect humble people, those who try to suspend judgment. Now contemplate epistemic humility. Think of someone heavily introspective, tortured by the awareness of his own ignorance. He lacks the courage of the idiot, yet has the rare gust to say “I don’t know”. He does not mind looking like a fool or, worse, an ignoramus. He hesitates, he will not commit, and he agonizes over the consequences of being wrong. He introspects, introspects, and introspects until he reaches physical and nervous exhaustion.


[Page 146] We know the difference between know-how and know-what. The Greeks made a distinction between techne and episteme, craft and knowledge. We have experts who tend to be experts: astronomers, pilots, physicists, mathematicians, accountants and experts who tend to be… note experts: stockbrokers, psychologists, councilors… Simply things that move and therefore require knowledge do not usually have experts and are often Black-Swan-prone. The negative effect of prediction is that those who have a big reputation are worse predictors than those who had none.

[Page 166] The classical model of discovery is as follows: you search for what you know (say, a new way to reach India) and find something you didn’t know was there (America). It’s called serendipity. A term coined in a letter by the writer Hugh Walpole who derived it form a fairy tale, “The Three Princes of Serendip” who “were always making discoveries by accident or sagacity, of things they were not in quest of.“ […] Sir Francis Bacon commented that the most important advances are the least predictable ones.

[Page 169] Engineers tend to develop tools for the pleasure of developing tools. Tools lead to unexpected discoveries. So I disagree with Taleb’s definition: A nerd is simply someone who thinks exceedingly inside the box. It may not be contradictory but I prefer the engineer-like one: “I think a nerd is a person who uses the telephone to talk to other people about telephones. And a computer nerd therefore is somebody who uses a computer in order to use a computer. [https://www.startup-book.com/2012/02/03/triumph-of-the-nerds/]
And [Page 170] Pasteur claims “Luck favors the prepared”

[Page 170] On the difficulty of predicting, just look at the failure of the Segway which “it was prophesized, would change the morphology of cities.”

[Page 184] Another example of Taleb’s target: optimization… Optimization consists in finding the mathematically optimal policy that an economic agent could pursue. Optimization is a case of sterile modeling [discussed also in Chpater 17].


[Page 16] Categorization always produces a reduction in true complexity. Try to explain why those who favor allowing the elimination of a fetus in the mother’s womb also oppose capital punishment. [Which reminds me of André Frossard : “The unfortunate thing is that the left does not believe much in original sin and that the right has not much faith in redemption.”]

[Page 52] “I never meant that the Conservatives are generally stupid. I meant to say that stupid people are generally conservative” John Stuart Mill once complained. The problem is chronic: if you tell people that the key to success is not always skills, they think that you are telling them that it is never skills always luck.”

[Page 227] Which may explain “we live in a society of one person, one vote, where progressive taxes have been enacted precisely to weaken the winners”. I am not sure if Taleb does not prefer the aristocratic world. At least he seems to favor his friends from that world.

[Page 255] True, intellectually sophisticated characters were exactly what I looked for in life. My erudite and polymathic father – who, were he still alive, would have only been two weeks older than Benoît Mandelbrot [his mentor on non-linear fractals] – liked the company of extremely cultured Jesuit priests. I remember these Jesuit visitors […] I recall that one has a medical degree and a PhD in physics, yet taught Aramaic to locals in Beirut’s Institute of Eastern Languages. […] This kind of erudition impressed my father far more than scientific assembly-line work. I may have something in my genes dirving me away from bildungsphilisters.


[Page 28] a scalable profession is good only if you are successful; they are more competitive, produce monstrous inequalities and are far more random. Consider the example of the first music recording, of the alphabet, of the printing press. Today a few take almost everything; the rest, next to nothing [page 30].

[Page 32] In Mediocristan,” when your sample is large, no single instance will significantly change the aggregate or the total”. In Extremistan, Bill Gates in wealth or J. K. Rowling in book selling totally change the average of a crowd. “Almost all social matters are from Extremistan.” [When giving a talk on high-tech serial entrepreneurs at BCERC last month, I was slightly criticized with a “but you are only looking at 2% of the entrepreneurs! And I replied, yes but look at the impact…”]

[Page 85] Intellectual, scientific, and artistic activities belong to the province of Extremistan. I am still looking for a single counter-example, a non-dull activity that belongs to Mediocristan.

[Page 90] You not only see that venture capitalists do better than entrepreneurs, but publishers do better than authors, dealers do better than artists, and science does better than scientists.” (I can add that gold seekers made less money than the people who sold them picks and shovels.)

[Page 102] The consequence of the superstar dynamic is that what we call “literary heritage” or “literary treasures” is a minute proportion of what has been produced cumulatively. Balzac was just the beneficiary of disproportionate luck compared to his peers.

[Page 118] The problem here with the universe and the human race is that we are the surviving Casanovas (who should not have survived and had his life without luck – no destiny].


Taleb is not against statistics, but against Gaussian law, averages, etc. [Page 37] “The near-Black Swan are somewhat tractable. These are phenomena commonly known by terms such as scalable, scale-invariant, power laws, Pareto-Zipf laws, Yule’s law, Paretian-stable processes, Levy-stable and fractal laws.”

One thousand and one days or the story of the turkey confirms to me that an individual may not owe to the society that fed them initially!

[Page 239] Standard deviations do not exist outside the Gaussian, or if they do exist, they do not matter and do not explain much. But it gets worse. The Gaussian family (which includes various friends and relatives, such as the Poisson law) are the only class of distributions that the standard deviation (and the average) is sufficient to describe. You need nothing else. The bell curve satisfies the reductionism of the deluded. There are other notions that have little or no significance outside of the Gaussian: correlation and worse, regression. Yet they are deeply ingrained in our methods: it is hard to have a business conversation without hearing the word correlation.

[Page 240] Taleb has nothing against mathematicians, but he refers to Hardy’s views: The “real” mathematics of the “real” mathematicians, the mathematics of Fermat end Euler and Gauss and Abel and Riemann, is almost wholly “useless” (and this is as true of “applied” as of “pure” mathematics).

[Page 252] A critical feature of Gaussian statistics is the inclusion of two assumptions: First central assumption: the flips are independent of one another. The coin has no memory. The fact that you got heads or tails on the previous flip does not change the odds of your getting heads or tails on the next one. You do not become a “better” coin flipper over time. If you introduce memory, or skills in flipping, the entire Gaussian business becomes shaky. (Whereas there is preferential attachment and cumulative advantage in non-Gaussian events.) Second central assumption: no “wild” jump. The step size in the building block of the basic random walk is always known, namely one step. There is no uncertainty as to the size of the step.
[…] I have not for the life of me been able to find anyone around me in the business and statistical world who was intellectually consistent in that he both accepted the Black Swan and rejected the Gaussian and Gaussian tools. Many people accepted my Black Swan idea but could not take its logical conclusion, which is that you cannot use one single measure for randomness called standard deviation (and call it “risk”), you cannot expect a simple answer to characterize uncertainty.

But Taleb goes one step further. [Page 272] “But fractal randomness does not yield precise answer. […] Mandelbrot’s fractals allow us to account for a few Black Swans but not all. […] A gray swan concerns modelable extreme events, a black swan is about unknown unknowns. […] I repeat: Mandelbrot deals with gray swans; I deal with the Black Swan. So Mandelbrot domesticated many of my Black Swans, but not all of them, not completely.


Taleb shows that the stock crashes are sometimes linked to bad modeling and is particularly critical of the Black-Scholes options. He is very much critical of the stock portfolio theories and related Nobel prizes (Markowitz, Samuelson, Hicks or Debreu, “wrecking the ideas of Keynes”. The story of the LTCM hedge fund is an illustration of Taleb’s points.

Business and technology

[Page xxv] Almost no discovery, no technologies of note came from design and planning – they were just Black swans. […] So I disagree with the followers of Marx and those of Adam Smith: the reason free markets work is because they allow people to be lucky thanks to aggressive trial and error, not by giving rewards or “incentives” for skill.

[Page 17] The business world – inelegant, dull, pompous, greedy, unintellectual, selfish and boring.
[…] What I saw was that in some of the most prestigious business schools in the world, the executives of the most powerful corporations were coming to describe what they did for a living and it was possible that they too did not know what was going on.

[Page 135] When I ask people to name three recently implemented technologies that most impact our world today, they usually propose the computer, the Internet and the laser. All three were unplanned, unpredicted and unappreciated upon their discovery, and remained unappreciated well after their initial use. They were consequential. They were Black Swans.

Against averages

[Page 295] Half of the time I am a hyperskeptic; the other half I hold certainties. […] Half of the time I hate Black Swans, the other half I love them. […] Half of the time I am hyperconservative; the other half I am hyperaggressive”. I could delete the quotes!

I am not fully finished with the Black Swan, I am now reading the 70-page postcript essay which Taleb added to the latest paperback edition. There might be more to say (and read if you followed me until now…)

* Poincaré is quoted in Le Monde on July 7, 2012, by Cedric Villani, who by the way also mentions Black Swans in Dans les entrailles des cygnes noirs