A Few of my Favorite Quotes from Peter Thiel’s Zero to One

We are going to discuss the book at Chennai Open Coffee Club today. So I decided to take all the fragments I marked up from the the book and share it in this post. Hopefully, we will get to most of them in our book club discussion. I apologize that these quotes do not have much of a context or selected based on some criteria. I resonate with most of them and puzzled by a few. Here it goes:

This book is about the questions you must ask and answer to succeed in the business of doing new things: what follows is not a manual or a record of knowledge but an exercise in thinking. Because that is what a startup has to do: question received ideas and rethink business from scratch.

  • E VERY MOMENT IN BUSINESS happens only once. The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. And the next Mark Zuckerberg won’t create a social network. If you are copying these guys, you aren’t learning from them.
  • Unless they invest in the difficult task of creating new things, American companies will fail in the future no matter how big their profits remain today.
  • The paradox of teaching entrepreneurship is that such a formula necessarily cannot exist; because every innovation is new and unique, no authority can prescribe in concrete terms how to be innovative.
  • In a world of scarce resources, globalization without new technology is unsustainable.
  •  it’s hard to develop new things in big organizations,
  • Positively defined, a startup is the largest group of people you can convince of a plan to build a different future.
  • If you lose sight of competitive reality and focus on trivial differentiating factors— maybe you think your naan is superior because of your great-grandmother’s recipe—your business is unlikely to survive.
  • In the real world outside economic theory, every business is successful exactly to the extent that it does something others cannot.
  • a great business is defined by its ability to generate cash flows in the future.
  • Simply stated, the value of a business today is the sum of all the money it will make in the future .
  • Most of a tech company’s value will come at least 10 to 15 years in the future.
  • As a good rule of thumb, proprietary technology must be at least 10 times better than its closest substitute in some important dimension to lead to a real monopolistic advantage.
  • network effects businesses must start with especially small markets.
  • successful network businesses rarely get started by MBA types: the initial markets are so small that they often don’t even appear to be business opportunities at all.
  • Beginning with brand rather than substance is dangerous.
  • Every startup is small at the start. Every monopoly dominates a large share of its market. Therefore, every startup should start with a very small market. Always err on the side of starting too small. The reason is simple: it’s easier to dominate a small market than a large one . If you think your initial market might be too big, it almost certainly is.
  • The perfect target market for a startup is a small group of particular people concentrated together and served by few or no competitors.
  • future: is it a matter of chance or design?
  • Instead of pursuing many-sided mediocrity and calling it “well-roundedness,” a definite person determines the one best thing to do and then does it.
  • Boom produced a generation of indefinite optimists so used to effortless progress that they feel entitled to it.
  • fascination with statistical predictions – statistical predictions of what the country will be thinking in a few weeks ’ time than by visionary predictions of what the country will look like 10 or 20 years from now.
  • A startup is the largest endeavor over which you can have definite mastery. You can have agency not just over your own life, but over a small and important part of the world. It begins by rejecting the unjust tyranny of Chance. You are not a lottery ticket.
  • never underestimate exponential growth.
  • After all, less than 1% of new businesses started each year in the U.S. receive venture funding, and total VC investment accounts for less than 0.2% of GDP. But the results of those investments disproportionately propel the entire economy. Venture-backed companies create 11% of all private sector jobs. They generate annual revenues equivalent to an astounding 21% of GDP. Indeed, the dozen largest tech companies were all venture-backed. Together those 12 companies are worth more than $ 2 trillion, more than all other tech companies combined.
  • EVERY ONE OF TODAY ’S most famous and familiar ideas was once unknown and unsuspected.
  • Companies are like countries in this way. Bad decisions made early on— if you choose the wrong partners or hire the wrong people , for example— are very hard to correct after they are made. It may take a crisis on the order of bankruptcy before anybody will even try to correct them.
  • advertising doesn’t exist to make you buy a product right away; it exists to embed subtle impressions that will drive sales later.
  • It’s better to think of distribution as something essential to the design of your product. If you’ve invented something new but you haven’t invented an effective way to sell it, you have a bad business— no matter how good the product.
  • Superior sales and distribution by itself can create a monopoly, even with no product differentiation.
  • The most valuable businesses of coming decades will be built by entrepreneurs who seek to empower people rather than try to make them obsolete.
  • But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?
  • The Engineering Question Can you create breakthrough technology instead of incremental improvements? 2. The Timing Question Is now the right time to start your particular business? 3. The Monopoly Question Are you starting with a big share of a small market? 4. The People Question Do you have the right team? 5. The Distribution Question Do you have a way to not just create but deliver your product? 6. The Durability Question Will your market position be defensible 10 and 20 years into the future? 7. The Secret Question Have you identified a unique opportunity that others don’t see?
  • Customers won’t care about any particular technology unless it solves a particular problem in a superior way.
  • cleantech executives were running around wearing suits and ties. This was a huge red flag, because real technologists wear T-shirts and jeans.
  • never invest in a tech CEO that wears a suit
  • An entrepreneur can’t benefit from macro-scale insight unless his own plans begin at the micro-scale.
  • No sector will ever be so important that merely participating in it will be enough to build a great company.
  • But a valuable business must start by finding a niche and dominating a small market.
  • O F THE SIX PEOPLE who started PayPal, four had built bombs in high school. Five were just 23 years old— or younger. Four of us had been born outside the United States.
  • IF EVEN THE MOST FARSIGHTED founders cannot plan beyond the next 20 to 30 years, is there anything to say about the very distant future?

And then, as it usually happens, I discovered this one

Focusing Illusion

“Nothing In Life Is As Important As You Think It Is, While You Are Thinking About It”


Education is an important determinant of income — one of the most important — but it is less important than most people think. If everyone had the same education, the inequality of income would be reduced by less than 10%. When you focus on education you neglect the myriad other factors that determine income. The differences of income among people who have the same education are huge.


Recipient, Nobel Prize in Economics, 2002; Eugene Higgins Professor of Psychology; Author, Thinking Fast and Slow

Your Brain and The Concept of Desirable Difficulty

The struggle and frustration you feel at the edges of your abilities— that uncomfortable burn of “almost, almost”— is the sensation of constructing new neural connections, a phenomenon that the UCLA psychologist Robert Bjork calls “desirable difficulty.” Your brain works just like your muscles: no pain, no gain.

The concept is not new. In several articles about “being in the zone” and “being in the flow” you are at the edges of your capacity – not too hard, not too easy, just  stretched a bit. There is no need for intensity. A regular daily practice snacks  help.

With deep practice, small daily practice “snacks” are more effective than once-a-week practice binges. The reason has to do with the way our brains grow—incrementally, a little each day, even as we sleep. Daily practice, even for five minutes, nourishes this process, while more occasional practice forces your brain to play catch-up.
As a teacher, I am experimenting with these right chunks for each student. They are different for each student. The biggest challenge is to motivate them to make it a daily habit.

The Little Book of Talent by Daniel Coyle  is an amazing read.  You can view the 52 tips from the book in this Slideshare presentation. I would urge you to get the book and read it. It is a keeper and will teach you a lot about developing talent whether you are a parent or a teacher.

Data Jujitsu – A Pragmatic Approach To Applying Data Science

Notes from Data Jujitsu – The Art of Turning Data into Product by Dr. DJ Patil. It is free and a PDF version can be found here. dj This is a great resource, if you are just starting to think about applying Data Science in your organization.  I like DJ’s incremental and very pragmatic approach to applying data science to your business and building a product. He has done it at LinkedIn (in fact the first time I heard the term “Data Science”, was from his LinkedIn post). Here are a few excerpts, a selection of fragments I marked up while reading the book. It is only about 16 pages so you should certainly give it a try if you are interested in the topic.

On Data Scientists:

Smart Data Scientists don’t just solve big, hard problems; they also have an instinct for making big problems small.

He proposes a very practical approach to solving problems.

Solve a simple piece that shows you whether there’s an interest.

In this lean startup world, there is a similar approach, known as building a minimum viable product (MVP), to assess whether there is interest and validate the assumptions we make about user needs.

On learning more about problems:

By using humans to solve problems initially, we can understand a great deal about the problem at a very low cost. The collaborative filter is a great example of starting with a simple product that becomes a more complex system later, once you know that it works.

How do you create engagement and revenue with your data product?

Some examples from his LinkedIn experience:

Giving data back to user creates additional value. By giving data back to the user, you can create both engagement and revenue.

Focus on actionability of data.

“Inverse interaction law” applies to most users: The more data you present, the less interaction. The best way to avoid data vomit is to focus on actionability of data. That is, what action do you want the user to take?

Putting Data Jujitsu into practice.

Data Jujitsu embraces the notion of minimum viable product and the simplest thing that could possibly work.

My favorite part is the advice to product builders.

With all products, you should ask yourself three questions:

1. What do you want the user to take away from this product?

2. What action do you want the user to take because of the product?

3. How should the user feel during and after using your product?

If your product is successful you will have plenty of time to play with complex machine learning algorithms, large computing clusters in the cloud…

It is 16 pages of practical wisdom that comes out of running data science teams and building products at LinkedIn. The wonderful gesture of  sharing his experience with us in a free eminently readable report,  deserves a salute.

Book: What Is Data Science?

I just finished reading the book What is Data Science?.


It is a small book (25 pages) and one of the many good starting points to learn about Data Science. This not a review but a few quotes from the book:

  • According to Mike Driscoll(@dataspora), statistics is the “grammar of data science.”
  • According to Martin Wattenberg (@wattenberg, founder of Flowing Media), visualization is key to data conditioning: if you want to find out just how bad your data is, try plotting it.
  • Making data tell its story isn’t just a matter of presenting results; it involves making connections, then going back to other data sources to verify them.
  • Data science requires skills ranging from traditional computer science to mathematics to art.
  • According to DJ Patil,  (@dpatil), the best data scientists tend to be “hard scientists,” particularly physicists, rather than computer science majors. Physicists have a strong mathematical background, computing skills, and come from a discipline in which survival depends on getting the most from the data. They have to think about the big picture, the big problem. When you’ve just spent a lot of grant money generating data, you can’t just throw the data out if it isn’t as clean as you’d like. You have to make it tell its story. You need some creativity for when the story the data is telling isn’t what you think it’s telling.
  • What Patil calls “data jiujitsu”—using smaller auxiliary problems to solve a large, difficult problem that appears intractable (he has a book on Data Jujitsu)
  • Patil’s first flippant answer to “what kind of person are you looking for when you hire a data scientist?” was “someone you would start a company with.”
  • Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdiscplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
  • The future belongs to the companies who figure out how to collect and use data successfully. Google, Amazon, Facebook, and LinkedIn have all tapped into their datastreams and made that the core of their success. They were the vanguard, but newer companies like bit.ly are following their path.
  • The part of Hal Varian’s quote that nobody remembers says it all: “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”

This graphic from BigData Startups shows that lots of organizations still do not understand Big Data and predicts a shortage f 140k-190k big data scientists and 1.5M big data managers in USA alone by 2018.


I am reading a bunch of books and will probably do more of these posts. BTW, big data is not always about big data. It is an umbrella term to cover different areas that deal with deriving value out of data.


Love The Motivation Behind This Book

I really like this para – The Motivation Behind the Book Doing Data Science:

The world is opening up with possibilities for people who are quantitatively minded and interested in putting their brains to work to solve the world’s problems. I consider it my goal to help these students to become critical thinkers, creative solvers of problems (even those that have not yet been identified), and curious question askers. While I myself may never build a mathematical model that is a piece of the cure for cancer, or identifies the underlying mystery of autism, or that prevents terrorist attacks, I like to think that I’m doing my part by teaching students who might one day do these things. And by writing this book, I’m expanding my reach to an even wider audience of data scientists who I hope will be inspired by this book, or learn tools in it, to make the world better and not worse.

The solutions to all the world’s problems may not lie in data and technology—and in fact, the mark of a good data scientist is someone who can identify problems that can be solved with data and is well-versed in the tools of modeling and code. But I do believe that interdisciplinary teams of people that include a data-savvy, quantitatively minded, coding-literate problem-solver (let’s call that person a “data scientist”) could go a long way.

BookLog: If Bayesian Inference is the Destination…

From this free book – Bayesian Methods for Hackers Using Python and PyMC

If Bayesian inference is the destination, then mathematical analysis is a particular path to towards it

After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

Path to Discovery.

  1. My tweetstream contained a couple of interesting tweets from the Strata Conference
  2. I went and starting looking at #strataconf
  3. Found this book (a compelling title with keywords like hacker, python, bayesian)

A Few Snippets From “Secrets of Analytical Leaders”

From the book Secrets of Analytical Leaders

Analytics have evolved a lot over the last 25 years.


On the business need for Analytics

A chief responsibility for any CEO in today’s business world is to build and to continually evolve the most effective strategy for mining and then leveraging the data that can make a difference within an organization.

Using information to make smarter decisions, develop better products and improve Business IQ

Through analytics, companies have the ability to use information to make smarter decisions, to develop better products, to improve overall customer satisfaction and to increase profitability. Analytics can become a sustainable resource that provides a competitive advantage because it helps improve business IQ.

Seeing connections and and imagining new ways to solve old problems…

people who live at the confluence of disparate approaches and opinions have a broader perspective. They see connections and possibilities that others miss. They speak multiple languages and gracefully move between different groups and norms. They continuously translate, synthesize, and unify. As a result, they imagine new ways to solve old problems, and they reinvent old ways to tackle new challenges. They are powerful change agents and value creators.

About Analytical Leaders – The Purple People…

They are not “blue” in the business or “red” in technology, but a blend of the two, hence purple. Purple people are true analytical leaders, and they are the central focus of this book.

The book is about Analytical Leaders and how they leverage information.


Terms like Big Data, Data Science, Data Engineering appear everywhere. Some people think it is the next frontier and others think it is all hype.

My company builds tools for discovering, tracking, gathering information. We were thinking of providing the next layer of tools (InfoAnalyzers) to make sense of all that information.

Before jumping in, I wanted to get some sense of the underlying need for analyzing data. Like you, I have some intuitive understanding but wanted to get a better sense of what the leaders in this space think. It is a constant quest and an exciting journey.

I accidentally came upon a page titled “A for Analytics” when I was searching for another book. The evolution graph was so fascinating that I paused my search and started reading. Fortunately, I have O’Reilly Safari subscription so jumping into this book took only a couple of clicks.

These snippets from the early portions of the book are just a sample. Hopefully I will have a lot to talk about after reading the book. If you want to get an over view this presentation by  Wayne Eckerson: Secrets of Analytical Leaders is a good starting point.

From The Book: Open Data Now

From the book – Open Data Now:

In a series of studies led by Harvey Lewis, a research director in Deloitte’s Insight Team, the firm has identified five Open Data business “archetypes”:

  1. Suppliers publish their data as Open Data that can be easily used. While they don’t charge for the data—if they did, it wouldn’t be Open Data—they increase customer loyalty and enhance their reputations by releasing it.
  2. Aggregators collect Open Data, analyze it, and charge for their insights or make money from the data in other ways.
  3. Developers “design, build, and sell web-based, tablet, or smart-phone applications” using Open Data as a free resource.
  4. Enrichers are “typically large, established businesses” that use Open Data to “enhance their existing products and services,” for example by using demographic data to understand their customers better.
  5. Enablers charge companies to make it easier for them to use Open Data.

You can download a sample chapter free.

Data Science Salaries in 2013

“Whether you call it Big Data, data science, or simply analytics, modern businesses see data as a gold mine.” This was in evidence in this salary survey results.

From O’Reilly Data Science Survey ( a free ebook on registration):

By a significant margin, more respondents used SQL than any other tool (71% of respondents, compared to 43% for the next highest ranked tool, R).

The open source tools R and Python, used by 43% and 40% of respondents, respectively, proved more widely used than Excel (used by 36% of respondents).

Salaries positively correlated with the number of tools used by respondents. The average respondent selected 10 tools and had a median income of $100k; those using 15 or more tools had a median salary of $130k.

Two clusters of correlating tool use: one consisting of open source tools (R, Python, Hadoop frameworks, and several scalable machine learning tools), the other consisting of commercial tools such as Excel, MSSQL, Tableau, Oracle RDB, and BusinessObjects.

Respondents who use more tools from the commercial cluster tend to use them in isolation, without many other tools.

Respondents selecting tools from the open source cluster had higher salaries than respondents selecting commercial tools. For example, respondents who selected 6 of the 19 open source tools had a median salary of $130k, while those using 5 of the 13 commercial cluster tools earned a median salary of $90k