People starting their own startups are not likely to get anything useful out of this article. There is little to "learn", as the link-bait title suggests, from a statistical analysis of billion-dollar companies.
There were, however, a few somewhat interesting takeaways amid the difficult-to-read mess:
The average founding age is 34.
80% of founders have started a company before.
Only 2 out of 39 have any female cofounders.
The billion+ startups are pretty evenly split into free, freemium, paid, and enterprise only pay structures.
Someone correct me if I'm wrong, but everyone I recognize on the list is a software-only company.
"We found 39 companies belong to what we call the “Unicorn Club” (by our definition, U.S.-based software companies started since 2003 and valued at over $1 billion by public or private market investors)."
So yes, they are talking only about software companies.
I have to kindly disagree with your opinion that there's nothing to learn here for aspiring entrepreneurs: one possibly actionable take-away seems to be "You haven't missed the train if you're 25, not even close. You still have time to learn, start a first company that probably won't take over the world, then try a second company."
39 is a tiny sample, to be quite honest, especially since the selection doesn't include erstwhile competitors who failed.
I've found studies of failures to be more illuminating. There are far more of them and you can draw more reliable conclusions as a result.
Consider for example Billion Dollar Lessons by Carroll and Mui. They are able to demonstrate, from a large, methodologically-selected sample, that most of the B-school grand strategies of the 1980s were disastrous.
Exactly. The problem with this sample is the same as In Search of Excellence - you can see what the good companies do, but what makes them unique? You can correct for it somewhat by adding comparison companies that failed, but the real thing to look for is very similar companies that had divergent choices at the same point in time, with everything being equal up until then. This is why VC and management is an art, not a science. :-)
> This is why VC and management is an art, not a science. :-)
Granted, but that said, Tetlock showed that in at least one very messy problem domain (international geopolitics), embarrassingly simplistic linear models crush expert judgement in terms of predictive power.
The problem is that the world is still sufficiently unpredictable that you can't use this insight to turn a buck, since events get "better" or "worse" with about the same frequency.
embarrassingly simplistic linear models crush expert judgement in terms of predictive power.
This is true in the vas majority of cases (andecdoctaly). But the inflection points are the subject of this essay. The analogue in internatinaly geopolitics, would be 9/11 and the ruthless pursuit of anti-civillian warfare. Which nobody predicted, and which <gamed> the theory of linearity[1] to maximize its damage.
[1] eg, the "expert" advice: 'go along with the hijackers', this has been empirically shown to be the safest/smartest course of action, etc
You'd find that book interesting. One of Tetlock's (many) findings is that almost everyone overpredicts outlier events. That is, we expect large deviations to occur far more often than they usually do. This is especially true of the "Hedgehogs", but "Foxes" do it also.
This is interesting, but their are two possible ways to read this: (1) Overdiagonoses; (2) Actual Bias. They are quite different in character.
An actual bias would be (for example) poor people taking unfair bets. We know they will dis-regard odds and buy lottery tickets that are negative NPV.
An overdiagnoses migh be something else altogether. If i know dis-proportionate rewards accrue to being "first" or early, I will overdiagnose. This is a variant of strategic precedence (to infer seniority, status, or special priveledge). This goes buy other monikers, like offensive "land grab" or the more defensive "lick the cookie" tactic.
In any event, the con is just a trick to manipulate someone who has linearly locked on a path (biased or not). However you feed them the bait, your goal is to get them to believe they do not need to revise their priors.
Yes - expertise is highly overrated too. Ask experts to give 90% confidence intervals on topics in their area of interest, and see how many they get wrong. This spans industries.
Ok, I don't read TechCrunch that often, but leading a story with a giant unicorn picture that literally takes up the entire screen on my 13" MacBook Air, such that all of the meaningful content is not even on the screen is really terrible design. I wish that design trend would go away like yesterday.
"Few companies are the result of a successful pivot."
I balance this statement against the notion of many incubators, whom I know/understand to focus heavily on the founding team more so than the idea. While the statement isn't damning in that respect, it does suggest that there's quite a bit more to success than just the people involved.
Incubators aren't necessarily trying to fund the next billion-dollar idea, but I'm not sure the statement doesn't apply to all companies, regardless of exit valuation.
Pivots are a redefinition of a failed attempt. The part about how most of the companies are made by 30+ people who have worked together and most of them having had founder experience seems to indicate that they've had had failures in their past. It seems mostly a semantical issue to me.
The impression I get is that pivots are neither a positive nor a negative indicator. It is neutral.
Pivots are an attempt to internalise the market-fitting function inside the firm.
Instead of failing at a company-bankruptcy level, the idea is to fail at a business-model level.
However, company-bankruptcy is a much stronger signal, including to other companies and investors.
Either way, the overall market-fitting function still resembles a kind of optimisation by simulated annealing -- local hill-climbing with a moderate degree of randomisation.
It seems obvious that some types of companies (like entreprise) that have been started in 2010 are not going to be valued at >$1b yet. It takes time.
This information would probably be more valuable if you cut it up by area (consumer v entreprise etc) and did comparables inside those areas.
"Started since 2003 and valued at $1b or over by public OR private markets"
To me, those are completely separate ways of valuing a company - one is realized (in that anyone who owns shares outright can sell them for the value stated) and the other is purely speculative.
It doesn't matter that Fab is "worth $1b" especially as it heads towards the toilet bowl over the last 10 months.
Again, companies valued at >$1b on private is an interesting group to look at, but trying to compare it to public doesn't strike me as very useful.
The one thing this article does is tell me that it's hard to hit $1b realized or unrealized. But I don't think anyone thought otherwise, no?
There were, however, a few somewhat interesting takeaways amid the difficult-to-read mess:
The average founding age is 34.
80% of founders have started a company before.
Only 2 out of 39 have any female cofounders.
The billion+ startups are pretty evenly split into free, freemium, paid, and enterprise only pay structures.
Someone correct me if I'm wrong, but everyone I recognize on the list is a software-only company.