|  
  
October 2023  
  
One of the most important things I didn't understand about the world when I
was a child is the degree to which the returns for performance are
superlinear.  
  
Teachers and coaches implicitly told us the returns were linear. "You get
out," I heard a thousand times, "what you put in." They meant well, but this
is rarely true. If your product is only half as good as your competitor's, you
don't get half as many customers. You get no customers, and you go out of
business.  
  
It's obviously true that the returns for performance are superlinear in
business. Some think this is a flaw of capitalism, and that if we changed the
rules it would stop being true. But superlinear returns for performance are a
feature of the world, not an artifact of rules we've invented. We see the same
pattern in fame, power, military victories, knowledge, and even benefit to
humanity. In all of these, the rich get richer.   
  
You can't understand the world without understanding the concept of
superlinear returns. And if you're ambitious you definitely should, because
this will be the wave you surf on.  
  
  
  
  
  
It may seem as if there are a lot of different situations with superlinear
returns, but as far as I can tell they reduce to two fundamental causes:
exponential growth and thresholds.  
  
The most obvious case of superlinear returns is when you're working on
something that grows exponentially. For example, growing bacterial cultures.
When they grow at all, they grow exponentially. But they're tricky to grow.
Which means the difference in outcome between someone who's adept at it and
someone who's not is very great.  
  
Startups can also grow exponentially, and we see the same pattern there. Some
manage to achieve high growth rates. Most don't. And as a result you get
qualitatively different outcomes: the companies with high growth rates tend to
become immensely valuable, while the ones with lower growth rates may not even
survive.  
  
Y Combinator encourages founders to focus on growth rate rather than absolute
numbers. It prevents them from being discouraged early on, when the absolute
numbers are still low. It also helps them decide what to focus on: you can use
growth rate as a compass to tell you how to evolve the company. But the main
advantage is that by focusing on growth rate you tend to get something that
grows exponentially.  
  
YC doesn't explicitly tell founders that with growth rate "you get out what
you put in," but it's not far from the truth. And if growth rate were
proportional to performance, then the reward for performance _p_ over time _t_
would be proportional to _p t_.  
  
Even after decades of thinking about this, I find that sentence startling.  
  
Whenever how well you do depends on how well you've done, you'll get
exponential growth. But neither our DNA nor our customs prepare us for it. No
one finds exponential growth natural; every child is surprised, the first time
they hear it, by the story of the man who asks the king for a single grain of
rice the first day and double the amount each successive day.  
  
What we don't understand naturally we develop customs to deal with, but we
don't have many customs about exponential growth either, because there have
been so few instances of it in human history. In principle herding should have
been one: the more animals you had, the more offspring they'd have. But in
practice grazing land was the limiting factor, and there was no plan for
growing that exponentially.  
  
Or more precisely, no generally applicable plan. There _was_ a way to grow
one's territory exponentially: by conquest. The more territory you control,
the more powerful your army becomes, and the easier it is to conquer new
territory. This is why history is full of empires. But so few people created
or ran empires that their experiences didn't affect customs very much. The
emperor was a remote and terrifying figure, not a source of lessons one could
use in one's own life.  
  
The most common case of exponential growth in preindustrial times was probably
scholarship. The more you know, the easier it is to learn new things. The
result, then as now, was that some people were startlingly more knowledgeable
than the rest about certain topics. But this didn't affect customs much
either. Although empires of ideas can overlap and there can thus be far more
emperors, in preindustrial times this type of empire had little practical
effect.   
  
That has changed in the last few centuries. Now the emperors of ideas can
design bombs that defeat the emperors of territory. But this phenomenon is
still so new that we haven't fully assimilated it. Few even of the
participants realize they're benefitting from exponential growth or ask what
they can learn from other instances of it.  
  
The other source of superlinear returns is embodied in the expression "winner
take all." In a sports match the relationship between performance and return
is a step function: the winning team gets one win whether they do much better
or just slightly better.   
  
The source of the step function is not competition per se, however. It's that
there are thresholds in the outcome. You don't need competition to get those.
There can be thresholds in situations where you're the only participant, like
proving a theorem or hitting a target.  
  
It's remarkable how often a situation with one source of superlinear returns
also has the other. Crossing thresholds leads to exponential growth: the
winning side in a battle usually suffers less damage, which makes them more
likely to win in the future. And exponential growth helps you cross
thresholds: in a market with network effects, a company that grows fast enough
can shut out potential competitors.  
  
Fame is an interesting example of a phenomenon that combines both sources of
superlinear returns. Fame grows exponentially because existing fans bring you
new ones. But the fundamental reason it's so concentrated is thresholds:
there's only so much room on the A-list in the average person's head.  
  
The most important case combining both sources of superlinear returns may be
learning. Knowledge grows exponentially, but there are also thresholds in it.
Learning to ride a bicycle, for example. Some of these thresholds are akin to
machine tools: once you learn to read, you're able to learn anything else much
faster. But the most important thresholds of all are those representing new
discoveries. Knowledge seems to be fractal in the sense that if you push hard
at the boundary of one area of knowledge, you sometimes discover a whole new
field. And if you do, you get first crack at all the new discoveries to be
made in it. Newton did this, and so did Durer and Darwin.  
  
  
  
  
  
Are there general rules for finding situations with superlinear returns? The
most obvious one is to seek work that compounds.  
  
There are two ways work can compound. It can compound directly, in the sense
that doing well in one cycle causes you to do better in the next. That happens
for example when you're building infrastructure, or growing an audience or
brand. Or work can compound by teaching you, since learning compounds. This
second case is an interesting one because you may feel you're doing badly as
it's happening. You may be failing to achieve your immediate goal. But if
you're learning a lot, then you're getting exponential growth nonetheless.  
  
This is one reason Silicon Valley is so tolerant of failure. People in Silicon
Valley aren't blindly tolerant of failure. They'll only continue to bet on you
if you're learning from your failures. But if you are, you are in fact a good
bet: maybe your company didn't grow the way you wanted, but you yourself have,
and that should yield results eventually.  
  
Indeed, the forms of exponential growth that don't consist of learning are so
often intermixed with it that we should probably treat this as the rule rather
than the exception. Which yields another heuristic: always be learning. If
you're not learning, you're probably not on a path that leads to superlinear
returns.  
  
But don't overoptimize _what_ you're learning. Don't limit yourself to
learning things that are already known to be valuable. You're learning; you
don't know for sure yet what's going to be valuable, and if you're too strict
you'll lop off the outliers.  
  
What about step functions? Are there also useful heuristics of the form "seek
thresholds" or "seek competition?" Here the situation is trickier. The
existence of a threshold doesn't guarantee the game will be worth playing. If
you play a round of Russian roulette, you'll be in a situation with a
threshold, certainly, but in the best case you're no better off. "Seek
competition" is similarly useless; what if the prize isn't worth competing
for? Sufficiently fast exponential growth guarantees both the shape and
magnitude of the return curve — because something that grows fast enough will
grow big even if it's trivially small at first — but thresholds only guarantee
the shape.   
  
A principle for taking advantage of thresholds has to include a test to ensure
the game is worth playing. Here's one that does: if you come across something
that's mediocre yet still popular, it could be a good idea to replace it. For
example, if a company makes a product that people dislike yet still buy, then
presumably they'd buy a better alternative if you made one.   
  
It would be great if there were a way to find promising intellectual
thresholds. Is there a way to tell which questions have whole new fields
beyond them? I doubt we could ever predict this with certainty, but the prize
is so valuable that it would be useful to have predictors that were even a
little better than random, and there's hope of finding those. We can to some
degree predict when a research problem _isn't_ likely to lead to new
discoveries: when it seems legit but boring. Whereas the kind that do lead to
new discoveries tend to seem very mystifying, but perhaps unimportant. (If
they were mystifying and obviously important, they'd be famous open questions
with lots of people already working on them.) So one heuristic here is to be
driven by curiosity rather than careerism — to give free rein to your
curiosity instead of working on what you're supposed to.  
  
  
  
  
  
The prospect of superlinear returns for performance is an exciting one for the
ambitious. And there's good news in this department: this territory is
expanding in both directions. There are more types of work in which you can
get superlinear returns, and the returns themselves are growing.  
  
There are two reasons for this, though they're so closely intertwined that
they're more like one and a half: progress in technology, and the decreasing
importance of organizations.  
  
Fifty years ago it used to be much more necessary to be part of an
organization to work on ambitious projects. It was the only way to get the
resources you needed, the only way to have colleagues, and the only way to get
distribution. So in 1970 your prestige was in most cases the prestige of the
organization you belonged to. And prestige was an accurate predictor, because
if you weren't part of an organization, you weren't likely to achieve much.
There were a handful of exceptions, most notably artists and writers, who
worked alone using inexpensive tools and had their own brands. But even they
were at the mercy of organizations for reaching audiences.   
  
A world dominated by organizations damped variation in the returns for
performance. But this world has eroded significantly just in my lifetime. Now
a lot more people can have the freedom that artists and writers had in the
20th century. There are lots of ambitious projects that don't require much
initial funding, and lots of new ways to learn, make money, find colleagues,
and reach audiences.  
  
There's still plenty of the old world left, but the rate of change has been
dramatic by historical standards. Especially considering what's at stake. It's
hard to imagine a more fundamental change than one in the returns for
performance.  
  
Without the damping effect of institutions, there will be more variation in
outcomes. Which doesn't imply everyone will be better off: people who do well
will do even better, but those who do badly will do worse. That's an important
point to bear in mind. Exposing oneself to superlinear returns is not for
everyone. Most people will be better off as part of the pool. So who should
shoot for superlinear returns? Ambitious people of two types: those who know
they're so good that they'll be net ahead in a world with higher variation,
and those, particularly the young, who can afford to risk trying it to find
out.   
  
The switch away from institutions won't simply be an exodus of their current
inhabitants. Many of the new winners will be people they'd never have let in.
So the resulting democratization of opportunity will be both greater and more
authentic than any tame intramural version the institutions themselves might
have cooked up.  
  
  
  
  
  
Not everyone is happy about this great unlocking of ambition. It threatens
some vested interests and contradicts some ideologies.  But if you're an
ambitious individual it's good news for you. How should you take advantage of
it?  
  
The most obvious way to take advantage of superlinear returns for performance
is by doing exceptionally good work. At the far end of the curve, incremental
effort is a bargain. All the more so because there's less competition at the
far end — and not just for the obvious reason that it's hard to do something
exceptionally well, but also because people find the prospect so intimidating
that few even try. Which means it's not just a bargain to do exceptional work,
but a bargain even to try to.  
  
There are many variables that affect how good your work is, and if you want to
be an outlier you need to get nearly all of them right. For example, to do
something exceptionally well, you have to be interested in it. Mere diligence
is not enough. So in a world with superlinear returns, it's even more valuable
to know what you're interested in, and to find ways to work on it.  It will
also be important to choose work that suits your circumstances. For example,
if there's a kind of work that inherently requires a huge expenditure of time
and energy, it will be increasingly valuable to do it when you're young and
don't yet have children.  
  
There's a surprising amount of technique to doing great work. It's not just a
matter of trying hard. I'm going to take a shot giving a recipe in one
paragraph.  
  
Choose work you have a natural aptitude for and a deep interest in. Develop a
habit of working on your own projects; it doesn't matter what they are so long
as you find them excitingly ambitious. Work as hard as you can without burning
out, and this will eventually bring you to one of the frontiers of knowledge.
These look smooth from a distance, but up close they're full of gaps. Notice
and explore such gaps, and if you're lucky one will expand into a whole new
field. Take as much risk as you can afford; if you're not failing occasionally
you're probably being too conservative. Seek out the best colleagues. Develop
good taste and learn from the best examples. Be honest, especially with
yourself. Exercise and eat and sleep well and avoid the more dangerous drugs.
When in doubt, follow your curiosity. It never lies, and it knows more than
you do about what's worth paying attention to.   
  
And there is of course one other thing you need: to be lucky. Luck is always a
factor, but it's even more of a factor when you're working on your own rather
than as part of an organization. And though there are some valid aphorisms
about luck being where preparedness meets opportunity and so on, there's also
a component of true chance that you can't do anything about. The solution is
to take multiple shots. Which is another reason to start taking risks early.  
  
  
  
  
  
The best example of a field with superlinear returns is probably science. It
has exponential growth, in the form of learning, combined with thresholds at
the extreme edge of performance — literally at the limits of knowledge.  
  
The result has been a level of inequality in scientific discovery that makes
the wealth inequality of even the most stratified societies seem mild by
comparison. Newton's discoveries were arguably greater than all his
contemporaries' combined.   
  
This point may seem obvious, but it might be just as well to spell it out.
Superlinear returns imply inequality. The steeper the return curve, the
greater the variation in outcomes.  
  
In fact, the correlation between superlinear returns and inequality is so
strong that it yields another heuristic for finding work of this type: look
for fields where a few big winners outperform everyone else. A kind of work
where everyone does about the same is unlikely to be one with superlinear
returns.  
  
What are fields where a few big winners outperform everyone else? Here are
some obvious ones: sports, politics, art, music, acting, directing, writing,
math, science, starting companies, and investing. In sports the phenomenon is
due to externally imposed thresholds; you only need to be a few percent faster
to win every race. In politics, power grows much as it did in the days of
emperors. And in some of the other fields (including politics) success is
driven largely by fame, which has its own source of superlinear growth. But
when we exclude sports and politics and the effects of fame, a remarkable
pattern emerges: the remaining list is exactly the same as the list of fields
where you have to be _independent-minded_ to succeed — where your ideas have
to be not just correct, but novel as well.   
  
This is obviously the case in science. You can't publish papers saying things
that other people have already said. But it's just as true in investing, for
example. It's only useful to believe that a company will do well if most other
investors don't; if everyone else thinks the company will do well, then its
stock price will already reflect that, and there's no room to make money.  
  
What else can we learn from these fields? In all of them you have to put in
the initial effort. Superlinear returns seem small at first. _At this rate,_
you find yourself thinking, _I'll never get anywhere._ But because the reward
curve rises so steeply at the far end, it's worth taking extraordinary
measures to get there.  
  
In the startup world, the name for this principle is "do things that don't
scale." If you pay a ridiculous amount of attention to your tiny initial set
of customers, ideally you'll kick off exponential growth by word of mouth. But
this same principle applies to anything that grows exponentially. Learning,
for example. When you first start learning something, you feel lost. But it's
worth making the initial effort to get a toehold, because the more you learn,
the easier it will get.  
  
There's another more subtle lesson in the list of fields with superlinear
returns: not to equate work with a job. For most of the 20th century the two
were identical for nearly everyone, and as a result we've inherited a custom
that equates productivity with having a job. Even now to most people the
phrase "your work" means their job. But to a writer or artist or scientist it
means whatever they're currently studying or creating. For someone like that,
their work is something they carry with them from job to job, if they have
jobs at all. It may be done for an employer, but it's part of their portfolio.  
  
  
  
  
  
It's an intimidating prospect to enter a field where a few big winners
outperform everyone else. Some people do this deliberately, but you don't need
to. If you have sufficient natural ability and you follow your curiosity
sufficiently far, you'll end up in one. Your curiosity won't let you be
interested in boring questions, and interesting questions tend to create
fields with superlinear returns if they're not already part of one.  
  
The territory of superlinear returns is by no means static. Indeed, the most
extreme returns come from expanding it. So while both ambition and curiosity
can get you into this territory, curiosity may be the more powerful of the
two. Ambition tends to make you climb existing peaks, but if you stick close
enough to an interesting enough question, it may grow into a mountain beneath
you.  
  
  
  
  
  
  
  
  
  
**