Startup Lessons From Zen and The Art of Motorcycle Maintenance

Over the last few weeks, I was inspired to re-read Zen and The Art of Motorcycle Maintenance, a book I read in my late teens and remember enjoying.

At the time, I embraced its emphasis on Quality (hard to define, easy to discern) as an organizing mantra for living a purposeful life. The book was published in the 1970s at a time when many young people were waking up from the hangover of the 1960s and feeling aimless and unfocused. What was the meaning of life and how should a good life be lived? Author Robert Pirsig does a brilliant job trying to address these big questions with a ranging review of Buddhism, Socrates, Plato, Kant, and other philosophers all told through the prism of an autobiographical journey on a motorcycle through the great expanse of the West with his young son. It has since become the best-selling philosophy book of all time.

Reading Zen decades later, though, gave me a new perspective on the book and its lessons for the art of startup building. Pirsig spends time highlighting the limitations of the scientific method and those limitations are ones that I’ve been thinking about recently in the context of startups.

The scientific method is a process for experimentation that rests on the belief that hypotheses should be sharply defined and then rigorously tested through well-constructed experiments. Eric Ries popularized applying the scientific method to startups through his book, The Lean Startup.

At Harvard Business School (HBS), in both my class and the entrepreneurship department more broadly, we teach the importance of treating startups like experimentation machines. But HBS and other entrepreneurship programs and startup accelerators typically fall short when giving guidance as to how to determine which experiments to run. I have written in the past that for founders, test selection is all about strategic choices.

Entrepreneurs need to be thoughtful and disciplined regarding test selection, design, and prioritization. In short, entrepreneurs need to analyze their business model critically and select the tests that matter the most at each phase of their journey.

What is less well-understood is the process for determining which experiments to run and how to sequence those experiments. Given that founders have a discrete envelope of time and money before they need to produce positive results, experimentation selection is one of the most critical factors in startup success.

Pirsig speaks to this point in a more general sense. Written in the 1970s, Zen could not have anticipated the impact of his words on tech startups. But his argument applies beautifully to tech startups when he states that test selection must come from intuition. At one point late in the book he observes:

“You need some ideas, some hypotheses. Traditional scientific method, unfortunately, has never quite gotten around to say exactly where to pick up more of these hypotheses…Creativity, originality, inventiveness, intuition, imagination…are completely outside its domain.”

The problem for entrepreneurs, then, is how to build up this intuition. How should an entrepreneur best navigate the Idea Maze (a lovely metaphor from Chris Dixon)?

As with the elusive definition of Quality in Zen, the answer is subtle and not easy to deliver or communicate. We think we know how to identify a quality entrepreneur, a quality business plan, a quality consumer value proposition, a quality initial product, and a quality go to market plan. But is there a rule set that entrepreneurs can follow to know that they are on the right path? The sad answer is no. But founders can do a few things to help in developing that critical entrepreneurial intuition required to navigate the Idea Maze:

  1. Customer Development. Immerse yourself in the customer problem set (i.e., perform deep customer development in the classically defined manner of Steve Blank and Four Steps to the Epiphany) to develop an intuition on the customer’s pain points to hypothesize compelling value propositions.
  2. Domain Knowledge. Study the domain through both direct research (i.e., be an autodidact founder, defying the myth of Founder-Market Fit) and by surrounding yourself with domain experts to develop an intuition on the market dynamics and trends.
  3. Strategy 101. Apply rigorous strategic thinking to the market dynamics, the value chain, substitutes and complements to develop an intuition on where profit pools may lie.
  4. Built a Test Machine. Build an organization that is able to run tests rapidly and efficiently so that your testing throughput is faster than your competitors, building a startup organization that is a learning machine as well as an execution machine.

Follow these steps and, hopefully, you will find your path to Quality experiments, execution, and company-building.

HBS Class – Launching Tech Ventures (LTV)

My Harvard Business School class, Launching Tech Ventures (LTV), begins in just a few weeks. I have taught the class for ten years across over 1000 students and it remains a joy and privilege to engage with our brilliant, ambitious students in the study of startups as they seek to achieve product-market fit.

This year, there are a number of changes happening at the school and in the course. The pandemic has forced us to deliver the course online for the first time and so my colleagues and I have tried to reimagine the course to optimize it for the new medium. I created a few videos to describe that reinvention, and give everyone a “behind the scenes” look at what the course will look like. My faculty colleagues this year are Sam Clemens, Donna Levin, and Reza Satchu. Each of them are accomplished entrepreneurs and investors who bring a wealth of practical experience into the classroom.

Below are the two videos and here is the class website. Enjoy!

Underrepresented Founders and Flybridge’s New Website

I have written about the cultural dysfunction in the venture capital industry before and, six years later, there have been some encouraging steps in the right direction. That said, we as an industry have a long way to go still. A few years ago, we decided to focus more intentionally on diversity and inclusion in our investment practice. Like many, we are appalled by the lack of capital and support for underrepresented founders and are eager to contribute as best we can to address this critical issue.

Over the last few years, we have co-founded and supported three major initiatives to improve diversity and inclusion in our innovation ecosystem:

  • XFactor Ventures:  a pre-seed fund focused on investing in female founders with billion-dollar ideas. My partner, Chip, co-founded the fund with our friend Anna Palmer (founder/CEO of Dough) and they recruited a team of twenty spectacular female CEOs to serve as the investment partners. One of the XFactor Ventures partners refers to her experience in XFactor as a master class in venture capital. Since inception a number of years ago, the fund has invested in over 40 female-founded startups.
  • Hack.Diversity:  a non-profit program to identify, support and train young engineers of color and help them secure jobs at the top innovation economy companies. Since inception a number of years ago, Hack.Diversity has helped nearly 200 fellows find jobs at 20 participating companies, resulting in an average increase in salary of $65,000. It is not unusual for the fellows to go from a minimum wage job as a dishwasher to a software engineering job paying $100,000 at firms like Drift, Hubspot, Rapid7 or Wayfair. Jody Rose of the NEVCA has been a terrific partner and leader in this work. Our recent impact report is here.
  • Global EIR:  a non-profit dedicated to helping immigrant entrepreneurs secure visas. Since inception a number of years ago, the program has had a 100% success rate in securing H1B visas across over 70 entrepreneurs. Those entrepreneurs have founded companies that now employ 1000 people and have raised over $500 million in venture capital. Brad Feld of Foundry Group has been a terrific partner in this endeavor as has Bill Brah at UMass Boston and Craig Montuori.

We are proud of these initiatives and continue to work hard to further their missions. At the same time, we know our core business of investing needs to evolve. We believe that diversity in thought and background is key to the success of our ecosystem as a whole as well as individual companies. In other words, we aspire to develop even more of an edge and reputation in backing diverse founders because we think it will yield better investments.

In the spirit of holding ourselves more accountable to this goal, we decided to highlight the underrepresented founders in our portfolio as part of the redesign of our website. Many firms allow you to sort their portfolio by sector or geography. We have added a category for “Underrepresented founders”, using the standard industry definition of those founders that belong to a group that the venture industry as a whole underinvests in relative to the percent of the overall US population.  This definition includes founders that are women as well as people of color, including those of African, Latin American, or Native American descent.

We have a lot more work to do as individual partners, as a firm, and as an industry as a whole in this area. Hopefully, by providing a bit more visibility towards this work, we can continue to make steady progress. And we welcome any support or ideas to get engaged and help advance these initiatives.

Finally, if you know great founders we should be talking to, particularly underrepresented founders, please let us know!

Cloudflare, Sprung from HBS, is Quietly Worth $5 Billion

Disclosure: I am not an investor in Cloudflare. I have no ties to the company other than a friendship with the two founders.cloudflare-co-founders-matthew-prince-and-michelle-zatlyn_750xx4522-2544-0-236

Amidst all the WeWork IPO hoopla, Cloudflare’s incredibly successful IPO was lost in the shuffle. That’s a shame because the amazing journey that these two founders have undertaken to build a business now worth $5 billion is worth studying.

As depicted in what has become a classic HBS case written by my colleague Professor Tom Eisenmann, Cloudflare founders Michelle Zatlyn and Matthew Prince met as students at Harvard Business School in 2008 and started the company as they were graduating. The two were a powerful combination:  Matthew was a hard-charging, technical visionary while Michelle was a skilled operator with an off-the-charts emotional IQ.

The company’s intense culture resulted in a rocky start. Attrition was high and morale low at the time of the case, despite the company’s early success. What happened next is a great lesson in leadership. The founders doubled down on culture and created a more welcoming, nurturing environment while retaining accountability and ambition. Prince and Zatlyn matured as founders and executives alongside the company’s maturing business model. Importantly, they stayed together as co-founders, even as Zatyln’s role evolved with the company’s meteoric growth and despite her taking time off for maternity leave. This summer, in the midst of the intense IPO process, the NY Times portrayed the company’s decision to ban 8chan in the wake of the El Paso massacre. Read the NY Times interview with Prince and you get a glimpse of a leader that isn’t too proud to admit when he’s wrong and willing to tackle tough decisions with a values-based compass.

Ten years after its founding, Cloudflare is a fast-growing, $300 million revenue company worth $5 billion. The company has quietly become a fundamental part of the Internet’s infrastructure and its leaders have become role models for other entrepreneurs for years to come.

Valuing Those Pesky Stock Options

Image result for stock options image

I receive many questions from my students and other startup joiners regarding how to evaluate the value of the stock options they are being offered. There is surprisingly little written about this topic, so this post will hopefully be useful to folks interested in answering this question.

In order to properly assess the value of your stock options, you need to know four pieces of information from the company:

  1. The number of shares they are offering to grant you
  2. The total number of fully diluted shares of the company
  3. The common stock strike price of your shares
  4. The preferred post-money valuation of the last round of financing

Many HR departments don’t know the answer to these four simple questions and get very defensive when asked by candidates, perhaps out of embarrassment or a false sense of confidentiality. Don’t be afraid to escalate the conversation to a more senior hiring manager or financial executive to get the answer. After all, it’s impossible to understand the value of the options package unless you have the data you need to evaluate it.

From these four data points, you should perform the following calculation using your best judgment:  what might be the dilution that I will face in the coming years as a result of future financings and what might be the range of valuation increases that the company might be able to achieve.

With this information in mind, you can derive a range of possible values of your stock options and evaluate whether the scenarios make sense to you and what range of value is possible under the different scenarios. The spreadsheet template below provides an example that you can play with or download here:

Hopefully, this template and post are helpful! I welcome any feedback or stories you might want to share on your own stock options negotiation process.

Many thanks to Matt Wozny for contributing to this post!

Experiments Lead to Product-Market Fit

The central theme of my Harvard Business School class, Launching Technology Ventures (LTV), is that startups are experimentation machines and the choice and design of experiments during a finite envelope of time and money is the central strategic decision that founders make. In other words, founders should test the experiments that matter most.

If done correctly, these early experiments eventually lead to finding product-market fit. But finding product-market fit in the context of a dynamic system that makes up the startup business model is complex and nuanced. Each component of the business model is linked to the other. Thus, experiments should be run that hold certain elements constant and focus on testing the most important, critical path business model elements first.

To help frame those decisions, I have developed a simple framework that builds off Professor Tom Eisenmann’s work on business model analysis for entrepreneurs to communicate the early strategic choices in experiment design. Founders need to answer two simple questions:

  • Which experiments should I run between testing the Consumer Value Proposition, the Go To Market and the Cash Flow formula (sometimes also referred to as the business model)?
  • What organization should I build to execute each of these experiments in the most efficient fashion?

The following two slides summarize these two questions visually:

Step One in my HBS LTV Course: figure out which are the most critical experiments to run

Step Two: figure out what organization to stand up to run those experiments in the most efficient fashion

The other day, my friend Ed Zimmerman of Lowenstein asked me to “speed present” my entire course in 5 minutes in advance of a panel that he hosted as part of his VentureCrush series. Here is that presentation, where I cover the experiments as well as the metrics that help determine where you are in your quest for product-market fit:

I welcome hearing about feedback from your own experiments!

The Rocket Ship List of Startups – 2019

New graduates should jump on board one of these high flying companies and go along for the ride

Graduating students hungry to dive into the startup community around the world (aka StartUpLand) often struggle to select the right, specific opportunity where they can productively start their career.

Each spring, I provide a comprehensive list of exciting, growing, hiring startups that are worthy of consideration as places to start or continue a career in StartUpLand. The criteria for being on the list is subjective but is a mix of fundraising (typically > $20m in the most recent round), scale (typically > 100 employees), momentum (typically growing users or revenue > 50%) and hiring (typically growing headcount > 20%, including a number of entry-level positions that would be a fit for recent college or business school graduates).

Before we get into the companies themselves, I suggest checking out two of my posts where I give some more detailed advice on how to select the right company for you and position yourself to secure a job:

Once you have reviewed this framework for deciding what you’re looking for, below you will find a list of 500 companies to research and approach.

As usual, the list is compiled and organized based on location. Like David Brooks, I believe in selecting a particular community to invest in and contribute to as a member of the ecosystem.

I received fantastic input from angels, entrepreneurs, lawyers and VCs across the world, helping me pressure test and compile this list (note: Flybridge portfolio companies are in blue). This year, I added companies from India and China with the help of a number of friends from those communities — a nod to the growing global importance of those two startup ecosystems. I’d also point folks to Stanford’s Andy Rachleff’s terrific list, which he publishes each fall with a similar theme.

I’m sure I made many mistakes and omissions, which are all my own. Special thanks to my Flybridge teammate and MIT Sloan intern Caroline Constable, who methodically crushed this year’s list and Harvard computer science intern Raymond Wang, who provided awesome analytical, programmatic firepower.

As always, feedback welcome!

East Coast

BOS 2019 v2

NYC 2019 v2

West Coast


Other Startup Hubs

  • ATL: Bitpay, CallRail, FullStory, Kabbage, MailChimp, Pindrop, SalesLoft, Terminus
  • CHI: AvantCredit, bloXroute, Civis Analytics, Fooda, FourKites, Kin Insurance, Project 44, ShopRunner, Sprout Social, Tempus
  • CO: Boom Supersonic, Cloud Elements, CyberGRX, FullContact, GoSpotCheck, Ibotta, JumpCloud, LogRhythm, Quantum Metric, Red Canary, TeamSnap, Welltok
  • DC: MapBox, Optoro, Sonatype, Vox Media, WeddingWire
  • SEA: Apptio, Convoy, DefinedCrowd, ExtraHop, Knock, OfferUp, Outreach, Porch,, Textio
  • UT: Avetta, BambooHR, Canopy Tax, Fortem Technologies, Health Catalyst, HireVue,, Lucid Software, ObservePoint, Podium, Qualtrics, Solutionreach, Via, Workfront

All About Applied AI


My partner, Chip Hazard, has been on a blogging tear lately on the topic of Applied AI.

We are pretty fired up about this theme here at Flybridge and Chip’s recent posts provide a nice outline as to why. His first post from a few weeks ago, Applied AI: Beyond The Algorithm, provides a description of how we think about next generation AI companies and the opportunities and challenges they face. Today’s post, the AI Paradox, gives a more detailed view on what we are internally referring to as “AAA grade” AI companies:  those that are focused on building Absorable, Applied AI. We are very bullish on this category of startups.

The kickoff last week of MIT’s billion-dollar new AI school, the Schwarzman College of Computing (pictured above), was a punctuation point in an ongoing arc of historical significance. We are entering an era where applied AI is on the cusp of impacting billions of lives and businesses. This wave will be a fun one to watch and participate in.

How To Raise Your First Round of Capital

Every year, I do a talk at Harvard Business School regarding how to raise your first round of capital. In the past, folks have found the slides to be helpful, and so I am sharing them here. The longer version of this material is covered in my book, Mastering the VC Game (first chapter is free) and this teaching note on Raising Startup Capital. I hope they’re helpful!



Applied AI: Beyond The Algorithms

Great, thoughful post from my partner, Chip, on Applied AI.

Hazard Lights

One of the primary areas of focus for Flybridge over the years has been to be the first institutional investor behind companies looking to transform the enterprise technology landscape with modern software.  Given the explosion in the volume of data being generated globally, this theme has led to investments in companies such as MongoDB (databases) and Nasuni (storage) that operate at the data infrastructure layer of the enterprise tech stack.

More recently, we have been investing in further advances in data management, analytics, machine learning, and artificial intelligence.  While the potential for artificial intelligence has been written about extensively, what is less well understood is that the algorithms and underlying tools are only a fraction of the value and are unlikely to be a source of long-term differentiation.  Fully realizing the power of AI requires a deep understanding of the domain and the specific workflows that AI will seek to…

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