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:
- The number of shares they are offering to grant you
- The total number of fully diluted shares of the company
- The common stock strike price of your shares
- 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!
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:
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!
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.
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!
In my blockchain investment work (we have invested in six early-stage projects, including bloXroute, Enigma, FalconX, NEX and two stealth projects), I have been struck by the fact that decades of progress on applying the scientific method to entrepreneurship (e.g., experimental design, lean startup, design thinking), as well as decades of established governance modeled, are being effectively blown up by Initial Coin Offerings (ICOs).
Steve Blank and Eric Ries popularized applying the scientific method to startups in an incisive fashion with the publishing of their books, Four Steps to the Epiphany and The Lean Startup, respectively. These became canons for entrepreneurs around the world as they embarked on the journey for product-market fit.
With blockchain startups raising over $5 billion in 2017 and over $12 billion through the first three quarters of 2018, it appears that this discipline of staged experimentation and fundraising is being discarded.
Harvard Business School professor Ramana Nanda and I spent some time on this issue in an article we published last week in Harvard Business Review called “The Hidden Cost of Initial Coin Offerings”. In it, we outline 3 defenses of large ICOs, some of the downsides they present and how they constrain the team from executing successfully on their mission. We hope it adds to an important debate on startup staging and experimentation in the context of this exciting, emerging funding mechanism.