pookie3000/paul_graham_all_essays huggingface dataset:
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March 2006, rev August 2009  
  
Yesterday one of the founders we funded asked me why we started Y Combinator.
Or more precisely, he asked if we'd started YC mainly for fun.  
  
Kind of, but not quite. It is enormously fun to be able to work with Rtm and
Trevor again. I missed that after we sold Viaweb, and for all the years after
I always had a background process running, looking for something we could do
together. There is definitely an aspect of a band reunion to Y Combinator.
Every couple days I slip and call it "Viaweb."  
  
Viaweb we started very explicitly to make money. I was sick of living from one
freelance project to the next, and decided to just work as hard as I could
till I'd made enough to solve the problem once and for all. Viaweb was
sometimes fun, but it wasn't designed for fun, and mostly it wasn't. I'd be
surprised if any startup is. All startups are mostly schleps.  
  
The real reason we started Y Combinator is neither selfish nor virtuous. We
didn't start it mainly to make money; we have no idea what our average returns
might be, and won't know for years. Nor did we start YC mainly to help out
young would-be founders, though we do like the idea, and comfort ourselves
occasionally with the thought that if all our investments tank, we will thus
have been doing something unselfish. (It's oddly nondeterministic.)  
  
The real reason we started Y Combinator is one probably only a hacker would
understand. We did it because it seems such a great hack. There are thousands
of smart people who could start companies and don't, and with a relatively
small amount of force applied at just the right place, we can spring on the
world a stream of new startups that might otherwise not have existed.  
  
In a way this is virtuous, because I think startups are a good thing. But
really what motivates us is the completely amoral desire that would motivate
any hacker who looked at some complex device and realized that with a tiny
tweak he could make it run more efficiently. In this case, the device is the
world's economy, which fortunately happens to be open source.  
  
  
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November 2022  
  
In the science fiction books I read as a kid, reading had often been replaced
by some more efficient way of acquiring knowledge. Mysterious "tapes" would
load it into one's brain like a program being loaded into a computer.  
  
That sort of thing is unlikely to happen anytime soon. Not just because it
would be hard to build a replacement for reading, but because even if one
existed, it would be insufficient. Reading about x doesn't just teach you
about x; it also teaches you how to write.   
  
Would that matter? If we replaced reading, would anyone need to be good at
writing?  
  
The reason it would matter is that writing is not just a way to convey ideas,
but also a way to have them.  
  
A good writer doesn't just think, and then write down what he thought, as a
sort of transcript. A good writer will almost always discover new things in
the process of writing. And there is, as far as I know, no substitute for this
kind of discovery. Talking about your ideas with other people is a good way to
develop them. But even after doing this, you'll find you still discover new
things when you sit down to write. There is a kind of thinking that can only
be done by _writing_.  
  
There are of course kinds of thinking that can be done without writing. If you
don't need to go too deeply into a problem, you can solve it without writing.
If you're thinking about how two pieces of machinery should fit together,
writing about it probably won't help much. And when a problem can be described
formally, you can sometimes solve it in your head. But if you need to solve a
complicated, ill-defined problem, it will almost always help to write about
it. Which in turn means that someone who's not good at writing will almost
always be at a disadvantage in solving such problems.  
  
You can't think well without writing well, and you can't write well without
reading well. And I mean that last "well" in both senses. You have to be good
at reading, and read good things.   
  
People who just want information may find other ways to get it. But people who
want to have ideas can't afford to.  
  
  
  
  
  
  
  
  
  
  
  
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August 2021  
  
When people say that in their experience all programming languages are
basically equivalent, they're making a statement not about languages but about
the kind of programming they've done.  
  
99.5% of programming consists of gluing together calls to library functions.
All popular languages are equally good at this. So one can easily spend one's
whole career operating in the intersection of popular programming languages.  
  
But the other .5% of programming is disproportionately interesting. If you
want to learn what it consists of, the weirdness of weird languages is a good
clue to follow.  
  
Weird languages aren't weird by accident. Not the good ones, at least. The
weirdness of the good ones usually implies the existence of some form of
programming that's not just the usual gluing together of library calls.  
  
A concrete example: Lisp macros. Lisp macros seem weird even to many Lisp
programmers. They're not only not in the intersection of popular languages,
but by their nature would be hard to implement properly in a language without
turning it into a dialect of Lisp. And macros are definitely evidence of
techniques that go beyond glue programming. For example, solving problems by
first writing a language for problems of that type, and then writing your
specific application in it. Nor is this all you can do with macros; it's just
one region in a space of program-manipulating techniques that even now is far
from fully explored.  
  
So if you want to expand your concept of what programming can be, one way to
do it is by learning weird languages. Pick a language that most programmers
consider weird but whose median user is smart, and then focus on the
differences between this language and the intersection of popular languages.
What can you say in this language that would be impossibly inconvenient to say
in others? In the process of learning how to say things you couldn't
previously say, you'll probably be learning how to think things you couldn't
previously think.  
  
  
  
  
  
  
  
**Thanks** to Trevor Blackwell, Patrick Collison, Daniel Gackle, Amjad Masad,
and Robert Morris for reading drafts of this.  
  
  
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Japanese Translation  
  
  
  
  

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January 2023  
  
 _(_Someone_ fed my essays into GPT to make something that could answer
questions based on them, then asked it where good ideas come from. The answer
was ok, but not what I would have said. This is what I would have said.)_  
  
The way to get new ideas is to notice anomalies: what seems strange, or
missing, or broken? You can see anomalies in everyday life (much of standup
comedy is based on this), but the best place to look for them is at the
frontiers of knowledge.  
  
Knowledge grows fractally. From a distance its edges look smooth, but when you
learn enough to get close to one, you'll notice it's full of gaps. These gaps
will seem obvious; it will seem inexplicable that no one has tried x or
wondered about y. In the best case, exploring such gaps yields whole new
fractal buds.  
  
  
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May 2003  
  
If Lisp is so great, why don't more people use it? I was asked this question
by a student in the audience at a talk I gave recently. Not for the first
time, either.  
  
In languages, as in so many things, there's not much correlation between
popularity and quality. Why does John Grisham (_King of Torts_ sales rank, 44)
outsell Jane Austen (_Pride and Prejudice_ sales rank, 6191)? Would even
Grisham claim that it's because he's a better writer?  
  
Here's the first sentence of _Pride and Prejudice:_

> It is a truth universally acknowledged, that a single man in possession of a
> good fortune must be in want of a wife.

"It is a truth universally acknowledged?" Long words for the first sentence of
a love story.  
  
Like Jane Austen, Lisp looks hard. Its syntax, or lack of syntax, makes it
look completely unlike the languages most people are used to. Before I learned
Lisp, I was afraid of it too. I recently came across a notebook from 1983 in
which I'd written:

> I suppose I should learn Lisp, but it seems so foreign.

Fortunately, I was 19 at the time and not too resistant to learning new
things. I was so ignorant that learning almost anything meant learning new
things.  
  
People frightened by Lisp make up other reasons for not using it. The standard
excuse, back when C was the default language, was that Lisp was too slow. Now
that Lisp dialects are among the faster languages available, that excuse has
gone away. Now the standard excuse is openly circular: that other languages
are more popular.  
  
(Beware of such reasoning. It gets you Windows.)  
  
Popularity is always self-perpetuating, but it's especially so in programming
languages. More libraries get written for popular languages, which makes them
still more popular. Programs often have to work with existing programs, and
this is easier if they're written in the same language, so languages spread
from program to program like a virus. And managers prefer popular languages,
because they give them more leverage over developers, who can more easily be
replaced.  
  
Indeed, if programming languages were all more or less equivalent, there would
be little justification for using any but the most popular. But they aren't
all equivalent, not by a long shot. And that's why less popular languages,
like Jane Austen's novels, continue to survive at all. When everyone else is
reading the latest John Grisham novel, there will always be a few people
reading Jane Austen instead.  
  
  
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May 2001  _(I wrote this article to help myself understand exactly what
McCarthy discovered. You don't need to know this stuff to program in Lisp, but
it should be helpful to anyone who wants to understand the essence of Lisp 
both in the sense of its origins and its semantic core. The fact that it has
such a core is one of Lisp's distinguishing features, and the reason why,
unlike other languages, Lisp has dialects.)_  
  
In 1960, John McCarthy published a remarkable paper in which he did for
programming something like what Euclid did for geometry. He showed how, given
a handful of simple operators and a notation for functions, you can build a
whole programming language. He called this language Lisp, for "List
Processing," because one of his key ideas was to use a simple data structure
called a _list_ for both code and data.  
  
It's worth understanding what McCarthy discovered, not just as a landmark in
the history of computers, but as a model for what programming is tending to
become in our own time. It seems to me that there have been two really clean,
consistent models of programming so far: the C model and the Lisp model. These
two seem points of high ground, with swampy lowlands between them. As
computers have grown more powerful, the new languages being developed have
been moving steadily toward the Lisp model. A popular recipe for new
programming languages in the past 20 years has been to take the C model of
computing and add to it, piecemeal, parts taken from the Lisp model, like
runtime typing and garbage collection.  
  
In this article I'm going to try to explain in the simplest possible terms
what McCarthy discovered. The point is not just to learn about an interesting
theoretical result someone figured out forty years ago, but to show where
languages are heading. The unusual thing about Lisp  in fact, the defining
quality of Lisp  is that it can be written in itself. To understand what
McCarthy meant by this, we're going to retrace his steps, with his
mathematical notation translated into running Common Lisp code.  
  
  
  
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Complete Article (Postscript)  
  
What Made Lisp Different  
  
The Code  
  
Chinese Translation  
  
Japanese Translation  
  
Portuguese Translation  
  
Korean Translation  
  
  
  
  

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April 2009  
  
Om Malik is the most recent of many people to ask why Twitter is such a big
deal.  
  
The reason is that it's a new messaging protocol, where you don't specify the
recipients. New protocols are rare. Or more precisely, new protocols that take
off are. There are only a handful of commonly used ones: TCP/IP (the
Internet), SMTP (email), HTTP (the web), and so on. So any new protocol is a
big deal. But Twitter is a protocol owned by a private company. That's even
rarer.  
  
Curiously, the fact that the founders of Twitter have been slow to monetize it
may in the long run prove to be an advantage. Because they haven't tried to
control it too much, Twitter feels to everyone like previous protocols. One
forgets it's owned by a private company. That must have made it easier for
Twitter to spread.  
  
  
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April 2009  
  
I usually avoid politics, but since we now seem to have an administration
that's open to suggestions, I'm going to risk making one. The single biggest
thing the government could do to increase the number of startups in this
country is a policy that would cost nothing: establish a new class of visa for
startup founders.  
  
The biggest constraint on the number of new startups that get created in the
US is not tax policy or employment law or even Sarbanes-Oxley. It's that we
won't let the people who want to start them into the country.  
  
Letting just 10,000 startup founders into the country each year could have a
visible effect on the economy. If we assume 4 people per startup, which is
probably an overestimate, that's 2500 new companies. _Each year._ They
wouldn't all grow as big as Google, but out of 2500 some would come close.  
  
By definition these 10,000 founders wouldn't be taking jobs from Americans: it
could be part of the terms of the visa that they couldn't work for existing
companies, only new ones they'd founded. In fact they'd cause there to be more
jobs for Americans, because the companies they started would hire more
employees as they grew.  
  
The tricky part might seem to be how one defined a startup. But that could be
solved quite easily: let the market decide. Startup investors work hard to
find the best startups. The government could not do better than to piggyback
on their expertise, and use investment by recognized startup investors as the
test of whether a company was a real startup.  
  
How would the government decide who's a startup investor? The same way they
decide what counts as a university for student visas. We'll establish our own
accreditation procedure. We know who one another are.  
  
10,000 people is a drop in the bucket by immigration standards, but would
represent a huge increase in the pool of startup founders. I think this would
have such a visible effect on the economy that it would make the legislator
who introduced the bill famous. The only way to know for sure would be to try
it, and that would cost practically nothing.  
  
  
  
  
  
**Thanks** to Trevor Blackwell, Paul Buchheit, Jeff Clavier, David Hornik,
Jessica Livingston, Greg Mcadoo, Aydin Senkut, and Fred Wilson for reading
drafts of this.  
  
  
  
**Related:**  
  
  
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The United States of Entrepreneurs  
  
About Half of VC-Backed Company Founders are Immigrants  
  
  
  
  

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February 2015  
  
One of the most valuable exercises you can try if you want to understand
startups is to look at the most successful companies and explain why they were
not as lame as they seemed when they first launched. Because they practically
all seemed lame at first. Not just small, lame. Not just the first step up a
big mountain. More like the first step into a swamp.  
  
A Basic interpreter for the Altair? How could that ever grow into a giant
company? People sleeping on airbeds in strangers' apartments? A web site for
college students to stalk one another? A wimpy little single-board computer
for hobbyists that used a TV as a monitor? A new search engine, when there
were already about 10, and they were all trying to de-emphasize search? These
ideas didn't just seem small. They seemed wrong. They were the kind of ideas
you could not merely ignore, but ridicule.  
  
Often the founders themselves didn't know why their ideas were promising. They
were attracted to these ideas by instinct, because they were living in the
future and they sensed that something was missing. But they could not have put
into words exactly how their ugly ducklings were going to grow into big,
beautiful swans.  
  
Most people's first impulse when they hear about a lame-sounding new startup
idea is to make fun of it. Even a lot of people who should know better.  
  
When I encounter a startup with a lame-sounding idea, I ask "What Microsoft is
this the Altair Basic of?" Now it's a puzzle, and the burden is on me to solve
it. Sometimes I can't think of an answer, especially when the idea is a made-
up one. But it's remarkable how often there does turn out to be an answer.
Often it's one the founders themselves hadn't seen yet.  
  
Intriguingly, there are sometimes multiple answers. I talked to a startup a
few days ago that could grow into 3 distinct Microsofts. They'd probably vary
in size by orders of magnitude. But you can never predict how big a Microsoft
is going to be, so in cases like that I encourage founders to follow whichever
path is most immediately exciting to them. Their instincts got them this far.
Why stop now?  
  
  
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January 2017  
  
Because biographies of famous scientists tend to edit out their mistakes, we
underestimate the degree of risk they were willing to take. And because
anything a famous scientist did that wasn't a mistake has probably now become
the conventional wisdom, those choices don't seem risky either.  
  
Biographies of Newton, for example, understandably focus more on physics than
alchemy or theology. The impression we get is that his unerring judgment led
him straight to truths no one else had noticed. How to explain all the time he
spent on alchemy and theology? Well, smart people are often kind of crazy.  
  
But maybe there is a simpler explanation. Maybe the smartness and the
craziness were not as separate as we think. Physics seems to us a promising
thing to work on, and alchemy and theology obvious wastes of time. But that's
because we know how things turned out. In Newton's day the three problems
seemed roughly equally promising. No one knew yet what the payoff would be for
inventing what we now call physics; if they had, more people would have been
working on it. And alchemy and theology were still then in the category Marc
Andreessen would describe as "huge, if true."  
  
Newton made three bets. One of them worked. But they were all risky.  
  
  
  
  
  
  
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January 2015  
  
No one, VC or angel, has invested in more of the top startups than Ron Conway.
He knows what happened in every deal in the Valley, half the time because he
arranged it.  
  
And yet he's a super nice guy. In fact, nice is not the word. Ronco is good. I
know of zero instances in which he has behaved badly. It's hard even to
imagine.  
  
When I first came to Silicon Valley I thought "How lucky that someone so
powerful is so benevolent." But gradually I realized it wasn't luck. It was by
being benevolent that Ronco became so powerful. All the deals he gets to
invest in come to him through referrals. Google did. Facebook did. Twitter was
a referral from Evan Williams himself. And the reason so many people refer
deals to him is that he's proven himself to be a good guy.  
  
Good does not mean being a pushover. I would not want to face an angry Ronco.
But if Ron's angry at you, it's because you did something wrong. Ron is so old
school he's Old Testament. He will smite you in his just wrath, but there's no
malice in it.  
  
In almost every domain there are advantages to seeming good. It makes people
trust you. But actually being good is an expensive way to seem good. To an
amoral person it might seem to be overkill.  
  
In some fields it might be, but apparently not in the startup world. Though
plenty of investors are jerks, there is a clear trend among them: the most
successful investors are also the most upstanding.   
  
It was not always this way. I would not feel confident saying that about
investors twenty years ago.  
  
What changed? The startup world became more transparent and more
unpredictable. Both make it harder to seem good without actually being good.  
  
It's obvious why transparency has that effect. When an investor maltreats a
founder now, it gets out. Maybe not all the way to the press, but other
founders hear about it, and that investor starts to lose deals.   
  
The effect of unpredictability is more subtle. It increases the work of being
inconsistent. If you're going to be two-faced, you have to know who you should
be nice to and who you can get away with being nasty to. In the startup world,
things change so rapidly that you can't tell. The random college kid you talk
to today might in a couple years be the CEO of the hottest startup in the
Valley. If you can't tell who to be nice to, you have to be nice to everyone.
And probably the only people who can manage that are the people who are
genuinely good.  
  
In a sufficiently connected and unpredictable world, you can't seem good
without being good.  
  
As often happens, Ron discovered how to be the investor of the future by
accident. He didn't foresee the future of startup investing, realize it would
pay to be upstanding, and force himself to behave that way. It would feel
unnatural to him to behave any other way. He was already living in the future.  
  
Fortunately that future is not limited to the startup world. The startup world
is more transparent and unpredictable than most, but almost everywhere the
trend is in that direction.  
  
  
  
  
  
  
  
  
  
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