Paul Graham: Black Swan Farming
2012 Sep 9
See all posts
Paul Graham: Black Swan Farming @ Satoshi Nakamoto
- Author
-
Paul Graham
- Email
-
satoshinakamotonetwork@proton.me
- Site
-
https://satoshinakamoto.network
I've done several types of work over the years but I don't know
another as counterintuitive as startup investing.
The two most important things to understand about startup investing,
as a business, are (1) that effectively all the returns are concentrated
in a few big winners, and (2) that the best ideas look initially like
bad ideas.
The first rule I knew intellectually, but didn't really grasp till it
happened to us. The total value of the companies we've funded is around
10 billion, give or take a few. But just two companies, Dropbox and
Airbnb, account for about three quarters of it.
In startups, the big winners are big to a degree that violates our
expectations about variation. I don't know whether these expectations
are innate or learned, but whatever the cause, we are just not prepared
for the 1000x variation in outcomes that one finds in startup
investing.
That yields all sorts of strange consequences. For example, in purely
financial terms, there is probably at most one company in each YC batch
that will have a significant effect on our returns, and the rest are
just a cost of doing business. I haven't really
assimilated that fact, partly because it's so counterintuitive, and
partly because we're not doing this just for financial reasons; YC would
be a pretty lonely place if we only had one company per batch. And yet
it's true.
To succeed in a domain that violates your intuitions, you need to be
able to turn them off the way a pilot does when flying through
clouds. You need to do what you know
intellectually to be right, even though it feels wrong.
It's a constant battle for us. It's hard to make ourselves take
enough risks. When you interview a startup and think "they seem likely
to succeed," it's hard not to fund them. And yet, financially at least,
there is only one kind of success: they're either going to be one of the
really big winners or not, and if not it doesn't matter whether you fund
them, because even if they succeed the effect on your returns will be
insignificant. In the same day of interviews you might meet some smart
19 year olds who aren't even sure what they want to work on. Their
chances of succeeding seem small. But again, it's not their chances of
succeeding that matter but their chances of succeeding really big. The
probability that any group will succeed really big is microscopically
small, but the probability that those 19 year olds will might be higher
than that of the other, safer group.
The probability that a startup will make it big is not simply a
constant fraction of the probability that they will succeed at all. If
it were, you could fund everyone who seemed likely to succeed at all,
and you'd get that fraction of big hits. Unfortunately picking winners
is harder than that. You have to ignore the elephant in front of you,
the likelihood they'll succeed, and focus instead on the separate and
almost invisibly intangible question of whether they'll succeed really
big.
Harder
That's made harder by the fact that the best startup ideas seem at
first like bad ideas. I've written about this before: if a good idea
were obviously good, someone else would already have done it. So the
most successful founders tend to work on ideas that few beside them
realize are good. Which is not that far from a description of insanity,
till you reach the point where you see results.
The first time Peter Thiel spoke at YC he drew a Venn diagram that
illustrates the situation perfectly. He drew two intersecting circles,
one labelled "seems like a bad idea" and the other "is a good idea." The
intersection is the sweet spot for startups.
This concept is a simple one and yet seeing it as a Venn diagram is
illuminating. It reminds you that there is an intersection—that there
are good ideas that seem bad. It also reminds you that the vast majority
of ideas that seem bad are bad.
The fact that the best ideas seem like bad ideas makes it even harder
to recognize the big winners. It means the probability of a startup
making it really big is not merely not a constant fraction of the
probability that it will succeed, but that the startups with a high
probability of the former will seem to have a disproportionately low
probability of the latter.
History tends to get rewritten by big successes, so that in
retrospect it seems obvious they were going to make it big. For that
reason one of my most valuable memories is how lame Facebook sounded to
me when I first heard about it. A site for college students to waste
time? It seemed the perfect bad idea: a site (1) for a niche market (2)
with no money (3) to do something that didn't matter.
One could have described Microsoft and Apple in exactly the same
terms.
Harder Still
Wait, it gets worse. You not only have to solve this hard problem,
but you have to do it with no indication of whether you're succeeding.
When you pick a big winner, you won't know it for two years.
Meanwhile, the one thing you can measure is dangerously
misleading. The one thing we can track precisely is how well the
startups in each batch do at fundraising after Demo Day. But we know
that's the wrong metric. There's no correlation between the percentage
of startups that raise money and the metric that does matter
financially, whether that batch of startups contains a big winner or
not.
Except an inverse one. That's the scary thing: fundraising is not
merely a useless metric, but positively misleading. We're in a business
where we need to pick unpromising-looking outliers, and the huge scale
of the successes means we can afford to spread our net very widely. The
big winners could generate 10,000x returns. That means for each big
winner we could pick a thousand companies that returned nothing and
still end up 10x ahead.
If we ever got to the point where 100% of the startups we funded were
able to raise money after Demo Day, it would almost certainly mean we
were being too conservative.
It takes a conscious effort not to do that too. After 15 cycles of
preparing startups for investors and then watching how they do, I can
now look at a group we're interviewing through Demo Day investors' eyes.
But those are the wrong eyes to look through!
We can afford to take at least 10x as much risk as Demo Day
investors. And since risk is usually proportionate to reward, if you can
afford to take more risk you should. What would it mean to take 10x more
risk than Demo Day investors? We'd have to be willing to fund 10x more
startups than they would. Which means that even if we're generous to
ourselves and assume that YC can on average triple a startup's expected
value, we'd be taking the right amount of risk if only 30% of the
startups were able to raise significant funding after Demo Day.
I don't know what fraction of them currently raise more after Demo
Day. I deliberately avoid calculating that number, because if you start
measuring something you start optimizing it, and I know it's the wrong
thing to optimize. But the percentage is certainly way
over 30%. And frankly the thought of a 30% success rate at fundraising
makes my stomach clench. A Demo Day where only 30% of the startups were
fundable would be a shambles. Everyone would agree that YC had jumped
the shark. We ourselves would feel that YC had jumped the shark. And yet
we'd all be wrong.
For better or worse that's never going to be more than a thought
experiment. We could never stand it. How about that for
counterintuitive? I can lay out what I know to be the right thing to do,
and still not do it. I can make up all sorts of plausible
justifications. It would hurt YC's brand (at least among the innumerate)
if we invested in huge numbers of risky startups that flamed out. It
might dilute the value of the alumni network. Perhaps most convincingly,
it would be demoralizing for us to be up to our chins in failure all the
time. But I know the real reason we're so conservative is that we just
haven't assimilated the fact of 1000x variation in returns.
We'll probably never be able to bring ourselves to take risks
proportionate to the returns in this business. The best we can hope for
is that when we interview a group and find ourselves thinking "they seem
like good founders, but what are investors going to think of this crazy
idea?" we'll continue to be able to say "who cares what investors
think?" That's what we thought about Airbnb, and if we want to fund more
Airbnbs we have to stay good at thinking it.
Thanks to Sam Altman, Paul Buchheit, Patrick
Collison, Jessica Livingston, Geoff Ralston, and Harj Taggar for reading
drafts of this.
Notes
Paul Graham: Black Swan Farming
2012 Sep 9 See all postsPaul Graham
satoshinakamotonetwork@proton.me
https://satoshinakamoto.network
I've done several types of work over the years but I don't know another as counterintuitive as startup investing.
The two most important things to understand about startup investing, as a business, are (1) that effectively all the returns are concentrated in a few big winners, and (2) that the best ideas look initially like bad ideas.
The first rule I knew intellectually, but didn't really grasp till it happened to us. The total value of the companies we've funded is around 10 billion, give or take a few. But just two companies, Dropbox and Airbnb, account for about three quarters of it.
In startups, the big winners are big to a degree that violates our expectations about variation. I don't know whether these expectations are innate or learned, but whatever the cause, we are just not prepared for the 1000x variation in outcomes that one finds in startup investing.
That yields all sorts of strange consequences. For example, in purely financial terms, there is probably at most one company in each YC batch that will have a significant effect on our returns, and the rest are just a cost of doing business. 1 I haven't really assimilated that fact, partly because it's so counterintuitive, and partly because we're not doing this just for financial reasons; YC would be a pretty lonely place if we only had one company per batch. And yet it's true.
To succeed in a domain that violates your intuitions, you need to be able to turn them off the way a pilot does when flying through clouds. 2 You need to do what you know intellectually to be right, even though it feels wrong.
It's a constant battle for us. It's hard to make ourselves take enough risks. When you interview a startup and think "they seem likely to succeed," it's hard not to fund them. And yet, financially at least, there is only one kind of success: they're either going to be one of the really big winners or not, and if not it doesn't matter whether you fund them, because even if they succeed the effect on your returns will be insignificant. In the same day of interviews you might meet some smart 19 year olds who aren't even sure what they want to work on. Their chances of succeeding seem small. But again, it's not their chances of succeeding that matter but their chances of succeeding really big. The probability that any group will succeed really big is microscopically small, but the probability that those 19 year olds will might be higher than that of the other, safer group.
The probability that a startup will make it big is not simply a constant fraction of the probability that they will succeed at all. If it were, you could fund everyone who seemed likely to succeed at all, and you'd get that fraction of big hits. Unfortunately picking winners is harder than that. You have to ignore the elephant in front of you, the likelihood they'll succeed, and focus instead on the separate and almost invisibly intangible question of whether they'll succeed really big.
Harder
That's made harder by the fact that the best startup ideas seem at first like bad ideas. I've written about this before: if a good idea were obviously good, someone else would already have done it. So the most successful founders tend to work on ideas that few beside them realize are good. Which is not that far from a description of insanity, till you reach the point where you see results.
The first time Peter Thiel spoke at YC he drew a Venn diagram that illustrates the situation perfectly. He drew two intersecting circles, one labelled "seems like a bad idea" and the other "is a good idea." The intersection is the sweet spot for startups.
This concept is a simple one and yet seeing it as a Venn diagram is illuminating. It reminds you that there is an intersection—that there are good ideas that seem bad. It also reminds you that the vast majority of ideas that seem bad are bad.
The fact that the best ideas seem like bad ideas makes it even harder to recognize the big winners. It means the probability of a startup making it really big is not merely not a constant fraction of the probability that it will succeed, but that the startups with a high probability of the former will seem to have a disproportionately low probability of the latter.
History tends to get rewritten by big successes, so that in retrospect it seems obvious they were going to make it big. For that reason one of my most valuable memories is how lame Facebook sounded to me when I first heard about it. A site for college students to waste time? It seemed the perfect bad idea: a site (1) for a niche market (2) with no money (3) to do something that didn't matter.
One could have described Microsoft and Apple in exactly the same terms. 3
Harder Still
Wait, it gets worse. You not only have to solve this hard problem, but you have to do it with no indication of whether you're succeeding. When you pick a big winner, you won't know it for two years.
Meanwhile, the one thing you can measure is dangerously misleading. The one thing we can track precisely is how well the startups in each batch do at fundraising after Demo Day. But we know that's the wrong metric. There's no correlation between the percentage of startups that raise money and the metric that does matter financially, whether that batch of startups contains a big winner or not.
Except an inverse one. That's the scary thing: fundraising is not merely a useless metric, but positively misleading. We're in a business where we need to pick unpromising-looking outliers, and the huge scale of the successes means we can afford to spread our net very widely. The big winners could generate 10,000x returns. That means for each big winner we could pick a thousand companies that returned nothing and still end up 10x ahead.
If we ever got to the point where 100% of the startups we funded were able to raise money after Demo Day, it would almost certainly mean we were being too conservative. 4
It takes a conscious effort not to do that too. After 15 cycles of preparing startups for investors and then watching how they do, I can now look at a group we're interviewing through Demo Day investors' eyes. But those are the wrong eyes to look through!
We can afford to take at least 10x as much risk as Demo Day investors. And since risk is usually proportionate to reward, if you can afford to take more risk you should. What would it mean to take 10x more risk than Demo Day investors? We'd have to be willing to fund 10x more startups than they would. Which means that even if we're generous to ourselves and assume that YC can on average triple a startup's expected value, we'd be taking the right amount of risk if only 30% of the startups were able to raise significant funding after Demo Day.
I don't know what fraction of them currently raise more after Demo Day. I deliberately avoid calculating that number, because if you start measuring something you start optimizing it, and I know it's the wrong thing to optimize. 5 But the percentage is certainly way over 30%. And frankly the thought of a 30% success rate at fundraising makes my stomach clench. A Demo Day where only 30% of the startups were fundable would be a shambles. Everyone would agree that YC had jumped the shark. We ourselves would feel that YC had jumped the shark. And yet we'd all be wrong.
For better or worse that's never going to be more than a thought experiment. We could never stand it. How about that for counterintuitive? I can lay out what I know to be the right thing to do, and still not do it. I can make up all sorts of plausible justifications. It would hurt YC's brand (at least among the innumerate) if we invested in huge numbers of risky startups that flamed out. It might dilute the value of the alumni network. Perhaps most convincingly, it would be demoralizing for us to be up to our chins in failure all the time. But I know the real reason we're so conservative is that we just haven't assimilated the fact of 1000x variation in returns.
We'll probably never be able to bring ourselves to take risks proportionate to the returns in this business. The best we can hope for is that when we interview a group and find ourselves thinking "they seem like good founders, but what are investors going to think of this crazy idea?" we'll continue to be able to say "who cares what investors think?" That's what we thought about Airbnb, and if we want to fund more Airbnbs we have to stay good at thinking it.
Thanks to Sam Altman, Paul Buchheit, Patrick Collison, Jessica Livingston, Geoff Ralston, and Harj Taggar for reading drafts of this.
Notes
I'm not saying that the big winners are all that matters, just that they're all that matters financially for investors. Since we're not doing YC mainly for financial reasons, the big winners aren't all that matters to us. We're delighted to have funded Reddit, for example. Even though we made comparatively little from it, Reddit has had a big effect on the world, and it introduced us to Steve Huffman and Alexis Ohanian, both of whom have become good friends.
Nor do we push founders to try to become one of the big winners if they don't want to. We didn't "swing for the fences" in our own startup (Viaweb, which was acquired for $50 million), and it would feel pretty bogus to press founders to do something we didn't do. Our rule is that it's up to the founders. Some want to take over the world, and some just want that first few million. But we invest in so many companies that we don't have to sweat any one outcome. In fact, we don't have to sweat whether startups have exits at all. The biggest exits are the only ones that matter financially, and those are guaranteed in the sense that if a company becomes big enough, a market for its shares will inevitably arise. Since the remaining outcomes don't have a significant effect on returns, it's cool with us if the founders want to sell early for a small amount, or grow slowly and never sell (i.e. become a so-called lifestyle business), or even shut the company down. We're sometimes disappointed when a startup we had high hopes for doesn't do well, but this disappointment is mostly the ordinary variety that anyone feels when that happens.↩︎
Without visual cues (e.g. the horizon) you can't distinguish between gravity and acceleration. Which means if you're flying through clouds you can't tell what the attitude of the aircraft is. You could feel like you're flying straight and level while in fact you're descending in a spiral. The solution is to ignore what your body is telling you and listen only to your instruments. But it turns out to be very hard to ignore what your body is telling you. Every pilot knows about this problem and yet it is still a leading cause of accidents.↩︎
Not all big hits follow this pattern though. The reason Google seemed a bad idea was that there were already lots of search engines and there didn't seem to be room for another.↩︎
A startup's success at fundraising is a function of two things: what they're selling and how good they are at selling it. And while we can teach startups a lot about how to appeal to investors, even the most convincing pitch can't sell an idea that investors don't like. I was genuinely worried that Airbnb, for example, would not be able to raise money after Demo Day. I couldn't convince Fred Wilson to fund them. They might not have raised money at all but for the coincidence that Greg McAdoo, our contact at Sequoia, was one of a handful of VCs who understood the vacation rental business, having spent much of the previous two years investigating it.↩︎
I calculated it once for the last batch before a consortium of investors started offering investment automatically to every startup we funded, summer 2010. At the time it was 94% (33 of 35 companies that tried to raise money succeeded, and one didn't try because they were already profitable). Presumably it's lower now because of that investment; in the old days it was raise after Demo Day or die.↩︎