I picked up The Founder’s Dilemmas: Anticipating and Avoiding the Pitfalls That Can Sink a Startup by Noam Wasserman after he spoke about it in the ETL speaker series at Stanford back in November (see my study notes of that week here). With all the regular school reading in parallel it took me 2 months and 2 vacation trips to dig through the material, but coming out on the other side it is a highly recommended read for anyone in the tech startup scene. Doesn’t matter if you’re just contemplating bootstrapping your first company or want to take a step-back look at the impact your term sheet demands as an investor can have on entrepreneurs on the receiving end.
Basically, what Noam has done is to take a whole sequence of inevitable dilemmas every founder has no way of escaping while building their startup, gone back to about 3k companies and 10k people and surveyed the hell out of them to quantify both the triggers as well as the indirect results down the road after they have made their choices on these issues so often just considered a “gut feeling thing”. Should I found a company now? Should I do it alone, with friends or strangers? What happens to my likely CEO tenure time if I take external money? What kind of side effects can different outcomes of horse trading over boards seats practically have? What are the hidden costs (and benefits) of hiring youth over experience, and vice versa?
Building a successful company out of nothing can be viewed as a sequence of this sort of daily decisions. Many of them can be hard, unclear, controversial, unfair (at least to some party) and scary. Making these decisions is often a lonely job. Noam’s research does not give you the “right” answers, but does provide helpful support based on quantified data. “If you to A, the likelihood of future B will go up 40%” or “measurable effect of dynamic equity distribution agreements to the core founder’s wealth is X” are useful data points that can be priceless for confirming or augmenting your gut-based choices, the least.
As a small warning, the book is quite academically structured, with every single chapter forming a self-contained entity a professor could distribute to students, I guess. This can make linear reading a bit repetitive at times, but interview tidbits with Ev Williams (Blogger, Odeo/Twitter), Dick Costolo (Feedburner, Twitter), Tim Westergren (Pandora) and several others fortunately bring a little more human spark back between the drier analysis. So, not sure if you’ll end up using the book for a straight read-through or a standing reference source you turn back in different stages of building your business. But it will be useful anyway.
Buy the book here, or see Noam’s site for further background. And as a teaser, below are a few notes I took while reading:
- Wasserman’s research: 2000-2009, 9900 founders, 19000 executives, 3600 startups
- Early research on entrepreneurship: Schumpeter 1934(!)
- Desires for wealth and control seem complementary, yet are in perpetual tension as entrepreneurial motivations
- On average, entrepreneurs earn 35% less over 10 year period than they would having a paid job
- Tech & life sciences startups:
- 74% of angel capital
- 71% of VC capital
- 48% of IPOs
- No sweet spot for founding ages: 14 years prior work experience average, but with almost 10y std deviation
- Only 18% with prior management experience
- those who had, 26% more likely to become solo founders
- Over 55% start companies in industries different from their past experience
- … and raise less capital, have lower staff growth, higher rate of failure than those with past industry exp
- 71% of founders had the idea while working at their regular job
- Only 18% with prior management experience
- 70% of founders married, 60% with at least one kid
- Passion is the almost only common entrepreneurial motivation
- Most powerful influences toward entrepreneurial career come from early messages sent by older relatives or by the culture (Butler, CareerLeader)
- Control motivations dominate male & female entrepreneurs’ lists alike, but financial gain ranks much lower for females
- Experienced entrepreneurs more likely to use “effectual reasoning” (start with means, personal strengths and resources, rather than predetermined goal, and then allow opportunities to emerge to which they can react). Non-entrepreneurial executives tend to use causal reasoning (set goal, seek the best way to achieve it). (Sarasvathy 2008)
- Founders come from smaller companies
- half from <25 employees, 64% from <100 employees
- VC backed companies 20% more likely to spawn next startups
- Common handcuffs blocking founding: social status, impressive title, well-known employer, high salary, vesting schedule, noncompete agreement, claims on IP, non hire agreements
- employees in the middle of the performance distribution least likely to leave
- Self-confidence: 95% of founders believe their startup has >50% chance of success
- 78% believe a similar startup has the same chance
- 33% believe they have 100% chance of success!
- Stinchcombe 1965: what makes new organisations especially prone to failure
- Larger founding teams have higher org growth and survival rates
- Cofounders from diverse prior companies more likely to adopt explorer strategies, shared work experience leads more likely to exploiter models
- Core founders should seek co-founders with social networks that differ from their own and are not overweighted with one type of social contact (“Valley engineers”)
- On egalitarism: In 26 large innovation intensive firms, autocratic CEO dominance was associated with 19% decrease of firm’s performance
- Dynamic equity agreements, two approaches: buyout terms and vesting schedules
- Different kinds of uncertainty by Deepak Malhotra:
- knowns (outcomes are known/ensured) – address by terms in contract
- known-unknowns (one can anticipate occurrence, but not the outcome) – address with contingencies
- unknown-unknowns (complete surprises) – address with trust
- Stanford Project on Emerging Companies (SPEC)
- Three dimensions of founder preferences: recruitment, rewards, control
- 36 permutations (3x3x4), yet 5 main “blueprints” prevail
- star, engineering, commitment, bureaucracy, autocracy