April 2003  
  
 _(This essay is derived from a keynote talk at PyCon 2003.)_  
  
It's hard to predict what life will be like in a hundred years. There are only
a few things we can say with certainty. We know that everyone will drive
flying cars, that zoning laws will be relaxed to allow buildings hundreds of
stories tall, that it will be dark most of the time, and that women will all
be trained in the martial arts. Here I want to zoom in on one detail of this
picture. What kind of programming language will they use to write the software
controlling those flying cars?  
  
This is worth thinking about not so much because we'll actually get to use
these languages as because, if we're lucky, we'll use languages on the path
from this point to that.  
  
  
  
I think that, like species, languages will form evolutionary trees, with dead-
ends branching off all over. We can see this happening already. Cobol, for all
its sometime popularity, does not seem to have any intellectual descendants.
It is an evolutionary dead-end-- a Neanderthal language.  
  
I predict a similar fate for Java. People sometimes send me mail saying, "How
can you say that Java won't turn out to be a successful language? It's already
a successful language." And I admit that it is, if you measure success by
shelf space taken up by books on it (particularly individual books on it), or
by the number of undergrads who believe they have to learn it to get a job.
When I say Java won't turn out to be a successful language, I mean something
more specific: that Java will turn out to be an evolutionary dead-end, like
Cobol.  
  
This is just a guess. I may be wrong. My point here is not to dis Java, but to
raise the issue of evolutionary trees and get people asking, where on the tree
is language X? The reason to ask this question isn't just so that our ghosts
can say, in a hundred years, I told you so. It's because staying close to the
main branches is a useful heuristic for finding languages that will be good to
program in now.  
  
At any given time, you're probably happiest on the main branches of an
evolutionary tree. Even when there were still plenty of Neanderthals, it must
have sucked to be one. The Cro-Magnons would have been constantly coming over
and beating you up and stealing your food.  
  
The reason I want to know what languages will be like in a hundred years is so
that I know what branch of the tree to bet on now.  
  
  
  
The evolution of languages differs from the evolution of species because
branches can converge. The Fortran branch, for example, seems to be merging
with the descendants of Algol. In theory this is possible for species too, but
it's not likely to have happened to any bigger than a cell.  
  
Convergence is more likely for languages partly because the space of
possibilities is smaller, and partly because mutations are not random.
Language designers deliberately incorporate ideas from other languages.  
  
It's especially useful for language designers to think about where the
evolution of programming languages is likely to lead, because they can steer
accordingly. In that case, "stay on a main branch" becomes more than a way to
choose a good language. It becomes a heuristic for making the right decisions
about language design.  
  
  
  
Any programming language can be divided into two parts: some set of
fundamental operators that play the role of axioms, and the rest of the
language, which could in principle be written in terms of these fundamental
operators.  
  
I think the fundamental operators are the most important factor in a
language's long term survival. The rest you can change. It's like the rule
that in buying a house you should consider location first of all. Everything
else you can fix later, but you can't fix the location.  
  
I think it's important not just that the axioms be well chosen, but that there
be few of them. Mathematicians have always felt this way about axioms-- the
fewer, the better-- and I think they're onto something.  
  
At the very least, it has to be a useful exercise to look closely at the core
of a language to see if there are any axioms that could be weeded out. I've
found in my long career as a slob that cruft breeds cruft, and I've seen this
happen in software as well as under beds and in the corners of rooms.  
  
I have a hunch that the main branches of the evolutionary tree pass through
the languages that have the smallest, cleanest cores. The more of a language
you can write in itself, the better.  
  
  
  
Of course, I'm making a big assumption in even asking what programming
languages will be like in a hundred years. Will we even be writing programs in
a hundred years? Won't we just tell computers what we want them to do?  
  
There hasn't been a lot of progress in that department so far. My guess is
that a hundred years from now people will still tell computers what to do
using programs we would recognize as such. There may be tasks that we solve
now by writing programs and which in a hundred years you won't have to write
programs to solve, but I think there will still be a good deal of programming
of the type that we do today.  
  
It may seem presumptuous to think anyone can predict what any technology will
look like in a hundred years. But remember that we already have almost fifty
years of history behind us. Looking forward a hundred years is a graspable
idea when we consider how slowly languages have evolved in the past fifty.  
  
Languages evolve slowly because they're not really technologies. Languages are
notation. A program is a formal description of the problem you want a computer
to solve for you. So the rate of evolution in programming languages is more
like the rate of evolution in mathematical notation than, say, transportation
or communications. Mathematical notation does evolve, but not with the giant
leaps you see in technology.  
  
  
  
Whatever computers are made of in a hundred years, it seems safe to predict
they will be much faster than they are now. If Moore's Law continues to put
out, they will be 74 quintillion (73,786,976,294,838,206,464) times faster.
That's kind of hard to imagine. And indeed, the most likely prediction in the
speed department may be that Moore's Law will stop working. Anything that is
supposed to double every eighteen months seems likely to run up against some
kind of fundamental limit eventually. But I have no trouble believing that
computers will be very much faster. Even if they only end up being a paltry
million times faster, that should change the ground rules for programming
languages substantially. Among other things, there will be more room for what
would now be considered slow languages, meaning languages that don't yield
very efficient code.  
  
And yet some applications will still demand speed. Some of the problems we
want to solve with computers are created by computers; for example, the rate
at which you have to process video images depends on the rate at which another
computer can generate them. And there is another class of problems which
inherently have an unlimited capacity to soak up cycles: image rendering,
cryptography, simulations.  
  
If some applications can be increasingly inefficient while others continue to
demand all the speed the hardware can deliver, faster computers will mean that
languages have to cover an ever wider range of efficiencies. We've seen this
happening already. Current implementations of some popular new languages are
shockingly wasteful by the standards of previous decades.  
  
This isn't just something that happens with programming languages. It's a
general historical trend. As technologies improve, each generation can do
things that the previous generation would have considered wasteful. People
thirty years ago would be astonished at how casually we make long distance
phone calls. People a hundred years ago would be even more astonished that a
package would one day travel from Boston to New York via Memphis.  
  
  
  
I can already tell you what's going to happen to all those extra cycles that
faster hardware is going to give us in the next hundred years. They're nearly
all going to be wasted.  
  
I learned to program when computer power was scarce. I can remember taking all
the spaces out of my Basic programs so they would fit into the memory of a 4K
TRS-80. The thought of all this stupendously inefficient software burning up
cycles doing the same thing over and over seems kind of gross to me. But I
think my intuitions here are wrong. I'm like someone who grew up poor, and
can't bear to spend money even for something important, like going to the
doctor.  
  
Some kinds of waste really are disgusting. SUVs, for example, would arguably
be gross even if they ran on a fuel which would never run out and generated no
pollution. SUVs are gross because they're the solution to a gross problem.
(How to make minivans look more masculine.) But not all waste is bad. Now that
we have the infrastructure to support it, counting the minutes of your long-
distance calls starts to seem niggling. If you have the resources, it's more
elegant to think of all phone calls as one kind of thing, no matter where the
other person is.  
  
There's good waste, and bad waste. I'm interested in good waste-- the kind
where, by spending more, we can get simpler designs. How will we take
advantage of the opportunities to waste cycles that we'll get from new, faster
hardware?  
  
The desire for speed is so deeply engrained in us, with our puny computers,
that it will take a conscious effort to overcome it. In language design, we
should be consciously seeking out situations where we can trade efficiency for
even the smallest increase in convenience.  
  
  
  
Most data structures exist because of speed. For example, many languages today
have both strings and lists. Semantically, strings are more or less a subset
of lists in which the elements are characters. So why do you need a separate
data type? You don't, really. Strings only exist for efficiency. But it's lame
to clutter up the semantics of the language with hacks to make programs run
faster. Having strings in a language seems to be a case of premature
optimization.  
  
If we think of the core of a language as a set of axioms, surely it's gross to
have additional axioms that add no expressive power, simply for the sake of
efficiency. Efficiency is important, but I don't think that's the right way to
get it.  
  
The right way to solve that problem, I think, is to separate the meaning of a
program from the implementation details. Instead of having both lists and
strings, have just lists, with some way to give the compiler optimization
advice that will allow it to lay out strings as contiguous bytes if necessary.  
  
Since speed doesn't matter in most of a program, you won't ordinarily need to
bother with this sort of micromanagement. This will be more and more true as
computers get faster.  
  
  
  
Saying less about implementation should also make programs more flexible.
Specifications change while a program is being written, and this is not only
inevitable, but desirable.  
  
The word "essay" comes from the French verb "essayer", which means "to try".
An essay, in the original sense, is something you write to try to figure
something out. This happens in software too. I think some of the best programs
were essays, in the sense that the authors didn't know when they started
exactly what they were trying to write.  
  
Lisp hackers already know about the value of being flexible with data
structures. We tend to write the first version of a program so that it does
everything with lists. These initial versions can be so shockingly inefficient
that it takes a conscious effort not to think about what they're doing, just
as, for me at least, eating a steak requires a conscious effort not to think
where it came from.  
  
What programmers in a hundred years will be looking for, most of all, is a
language where you can throw together an unbelievably inefficient version 1 of
a program with the least possible effort. At least, that's how we'd describe
it in present-day terms. What they'll say is that they want a language that's
easy to program in.  
  
Inefficient software isn't gross. What's gross is a language that makes
programmers do needless work. Wasting programmer time is the true
inefficiency, not wasting machine time. This will become ever more clear as
computers get faster.  
  
  
  
I think getting rid of strings is already something we could bear to think
about. We did it in [Arc](arc.html), and it seems to be a win; some operations
that would be awkward to describe as regular expressions can be described
easily as recursive functions.  
  
How far will this flattening of data structures go? I can think of
possibilities that shock even me, with my conscientiously broadened mind. Will
we get rid of arrays, for example? After all, they're just a subset of hash
tables where the keys are vectors of integers. Will we replace hash tables
themselves with lists?  
  
There are more shocking prospects even than that. The Lisp that McCarthy
described in 1960, for example, didn't have numbers. Logically, you don't need
to have a separate notion of numbers, because you can represent them as lists:
the integer n could be represented as a list of n elements. You can do math
this way. It's just unbearably inefficient.  
  
No one actually proposed implementing numbers as lists in practice. In fact,
McCarthy's 1960 paper was not, at the time, intended to be implemented at all.
It was a [theoretical exercise](rootsoflisp.html), an attempt to create a more
elegant alternative to the Turing Machine. When someone did, unexpectedly,
take this paper and translate it into a working Lisp interpreter, numbers
certainly weren't represented as lists; they were represented in binary, as in
every other language.  
  
Could a programming language go so far as to get rid of numbers as a
fundamental data type? I ask this not so much as a serious question as as a
way to play chicken with the future. It's like the hypothetical case of an
irresistible force meeting an immovable object-- here, an unimaginably
inefficient implementation meeting unimaginably great resources. I don't see
why not. The future is pretty long. If there's something we can do to decrease
the number of axioms in the core language, that would seem to be the side to
bet on as t approaches infinity. If the idea still seems unbearable in a
hundred years, maybe it won't in a thousand.  
  
Just to be clear about this, I'm not proposing that all numerical calculations
would actually be carried out using lists. I'm proposing that the core
language, prior to any additional notations about implementation, be defined
this way. In practice any program that wanted to do any amount of math would
probably represent numbers in binary, but this would be an optimization, not
part of the core language semantics.  
  
  
  
Another way to burn up cycles is to have many layers of software between the
application and the hardware. This too is a trend we see happening already:
many recent languages are compiled into byte code. Bill Woods once told me
that, as a rule of thumb, each layer of interpretation costs a factor of 10 in
speed. This extra cost buys you flexibility.  
  
The very first version of Arc was an extreme case of this sort of multi-level
slowness, with corresponding benefits. It was a classic "metacircular"
interpreter written on top of Common Lisp, with a definite family resemblance
to the eval function defined in McCarthy's original Lisp paper. The whole
thing was only a couple hundred lines of code, so it was very easy to
understand and change. The Common Lisp we used, CLisp, itself runs on top of a
byte code interpreter. So here we had two levels of interpretation, one of
them (the top one) shockingly inefficient, and the language was usable. Barely
usable, I admit, but usable.  
  
Writing software as multiple layers is a powerful technique even within
applications. Bottom-up programming means writing a program as a series of
layers, each of which serves as a language for the one above. This approach
tends to yield smaller, more flexible programs. It's also the best route to
that holy grail, reusability. A language is by definition reusable. The more
of your application you can push down into a language for writing that type of
application, the more of your software will be reusable.  
  
Somehow the idea of reusability got attached to object-oriented programming in
the 1980s, and no amount of evidence to the contrary seems to be able to shake
it free. But although some object-oriented software is reusable, what makes it
reusable is its bottom-upness, not its object-orientedness. Consider
libraries: they're reusable because they're language, whether they're written
in an object-oriented style or not.  
  
I don't predict the demise of object-oriented programming, by the way. Though
I don't think it has much to offer good programmers, except in certain
specialized domains, it is irresistible to large organizations. Object-
oriented programming offers a sustainable way to write spaghetti code. It lets
you accrete programs as a series of patches.  Large organizations always tend
to develop software this way, and I expect this to be as true in a hundred
years as it is today.  
  
  
  
As long as we're talking about the future, we had better talk about parallel
computation, because that's where this idea seems to live. That is, no matter
when you're talking, parallel computation seems to be something that is going
to happen in the future.  
  
Will the future ever catch up with it? People have been talking about parallel
computation as something imminent for at least 20 years, and it hasn't
affected programming practice much so far. Or hasn't it? Already chip
designers have to think about it, and so must people trying to write systems
software on multi-cpu computers.  
  
The real question is, how far up the ladder of abstraction will parallelism
go? In a hundred years will it affect even application programmers? Or will it
be something that compiler writers think about, but which is usually invisible
in the source code of applications?  
  
One thing that does seem likely is that most opportunities for parallelism
will be wasted. This is a special case of my more general prediction that most
of the extra computer power we're given will go to waste. I expect that, as
with the stupendous speed of the underlying hardware, parallelism will be
something that is available if you ask for it explicitly, but ordinarily not
used. This implies that the kind of parallelism we have in a hundred years
will not, except in special applications, be massive parallelism. I expect for
ordinary programmers it will be more like being able to fork off processes
that all end up running in parallel.  
  
And this will, like asking for specific implementations of data structures, be
something that you do fairly late in the life of a program, when you try to
optimize it. Version 1s will ordinarily ignore any advantages to be got from
parallel computation, just as they will ignore advantages to be got from
specific representations of data.  
  
Except in special kinds of applications, parallelism won't pervade the
programs that are written in a hundred years. It would be premature
optimization if it did.  
  
  
  
How many programming languages will there be in a hundred years? There seem to
be a huge number of new programming languages lately. Part of the reason is
that faster hardware has allowed programmers to make different tradeoffs
between speed and convenience, depending on the application. If this is a real
trend, the hardware we'll have in a hundred years should only increase it.  
  
And yet there may be only a few widely-used languages in a hundred years. Part
of the reason I say this is optimism: it seems that, if you did a really good
job, you could make a language that was ideal for writing a slow version 1,
and yet with the right optimization advice to the compiler, would also yield
very fast code when necessary. So, since I'm optimistic, I'm going to predict
that despite the huge gap they'll have between acceptable and maximal
efficiency, programmers in a hundred years will have languages that can span
most of it.  
  
As this gap widens, profilers will become increasingly important. Little
attention is paid to profiling now. Many people still seem to believe that the
way to get fast applications is to write compilers that generate fast code. As
the gap between acceptable and maximal performance widens, it will become
increasingly clear that the way to get fast applications is to have a good
guide from one to the other.  
  
When I say there may only be a few languages, I'm not including domain-
specific "little languages". I think such embedded languages are a great idea,
and I expect them to proliferate. But I expect them to be written as thin
enough skins that users can see the general-purpose language underneath.  
  
  
  
Who will design the languages of the future? One of the most exciting trends
in the last ten years has been the rise of open-source languages like Perl,
Python, and Ruby. Language design is being taken over by hackers. The results
so far are messy, but encouraging. There are some stunningly novel ideas in
Perl, for example. Many are stunningly bad, but that's always true of
ambitious efforts. At its current rate of mutation, God knows what Perl might
evolve into in a hundred years.  
  
It's not true that those who can't do, teach (some of the best hackers I know
are professors), but it is true that there are a lot of things that those who
teach can't do. [Research](desres.html) imposes constraining caste
restrictions. In any academic field there are topics that are ok to work on
and others that aren't. Unfortunately the distinction between acceptable and
forbidden topics is usually based on how intellectual the work sounds when
described in research papers, rather than how important it is for getting good
results. The extreme case is probably literature; people studying literature
rarely say anything that would be of the slightest use to those producing it.  
  
Though the situation is better in the sciences, the overlap between the kind
of work you're allowed to do and the kind of work that yields good languages
is distressingly small. (Olin Shivers has grumbled eloquently about this.) For
example, types seem to be an inexhaustible source of research papers, despite
the fact that static typing seems to preclude true macros-- without which, in
my opinion, no language is worth using.  
  
The trend is not merely toward languages being developed as open-source
projects rather than "research", but toward languages being designed by the
application programmers who need to use them, rather than by compiler writers.
This seems a good trend and I expect it to continue.  
  
  
  
Unlike physics in a hundred years, which is almost necessarily impossible to
predict, I think it may be possible in principle to design a language now that
would appeal to users in a hundred years.  
  
One way to design a language is to just write down the program you'd like to
be able to write, regardless of whether there is a compiler that can translate
it or hardware that can run it. When you do this you can assume unlimited
resources. It seems like we ought to be able to imagine unlimited resources as
well today as in a hundred years.  
  
What program would one like to write? Whatever is least work. Except not
quite: whatever _would be_ least work if your ideas about programming weren't
already influenced by the languages you're currently used to. Such influence
can be so pervasive that it takes a great effort to overcome it. You'd think
it would be obvious to creatures as lazy as us how to express a program with
the least effort. In fact, our ideas about what's possible tend to be so
[limited](avg.html) by whatever language we think in that easier formulations
of programs seem very surprising. They're something you have to discover, not
something you naturally sink into.  
  
One helpful trick here is to use the [length](power.html) of the program as an
approximation for how much work it is to write. Not the length in characters,
of course, but the length in distinct syntactic elements-- basically, the size
of the parse tree. It may not be quite true that the shortest program is the
least work to write, but it's close enough that you're better off aiming for
the solid target of brevity than the fuzzy, nearby one of least work. Then the
algorithm for language design becomes: look at a program and ask, is there any
way to write this that's shorter?  
  
In practice, writing programs in an imaginary hundred-year language will work
to varying degrees depending on how close you are to the core. Sort routines
you can write now. But it would be hard to predict now what kinds of libraries
might be needed in a hundred years. Presumably many libraries will be for
domains that don't even exist yet. If SETI@home works, for example, we'll need
libraries for communicating with aliens. Unless of course they are
sufficiently advanced that they already communicate in XML.  
  
At the other extreme, I think you might be able to design the core language
today. In fact, some might argue that it was already mostly designed in 1958.  
  
  
  
If the hundred year language were available today, would we want to program in
it? One way to answer this question is to look back. If present-day
programming languages had been available in 1960, would anyone have wanted to
use them?  
  
In some ways, the answer is no. Languages today assume infrastructure that
didn't exist in 1960. For example, a language in which indentation is
significant, like Python, would not work very well on printer terminals. But
putting such problems aside-- assuming, for example, that programs were all
just written on paper-- would programmers of the 1960s have liked writing
programs in the languages we use now?  
  
I think so. Some of the less imaginative ones, who had artifacts of early
languages built into their ideas of what a program was, might have had
trouble. (How can you manipulate data without doing pointer arithmetic? How
can you implement flow charts without gotos?) But I think the smartest
programmers would have had no trouble making the most of present-day
languages, if they'd had them.  
  
If we had the hundred-year language now, it would at least make a great
pseudocode. What about using it to write software? Since the hundred-year
language will need to generate fast code for some applications, presumably it
could generate code efficient enough to run acceptably well on our hardware.
We might have to give more optimization advice than users in a hundred years,
but it still might be a net win.  
  
  
  
Now we have two ideas that, if you combine them, suggest interesting
possibilities: (1) the hundred-year language could, in principle, be designed
today, and (2) such a language, if it existed, might be good to program in
today. When you see these ideas laid out like that, it's hard not to think,
why not try writing the hundred-year language now?  
  
When you're working on language design, I think it is good to have such a
target and to keep it consciously in mind. When you learn to drive, one of the
principles they teach you is to align the car not by lining up the hood with
the stripes painted on the road, but by aiming at some point in the distance.
Even if all you care about is what happens in the next ten feet, this is the
right answer. I think we can and should do the same thing with programming
languages.  
  
  
  
**Notes**  
  
I believe Lisp Machine Lisp was the first language to embody the principle
that declarations (except those of dynamic variables) were merely optimization
advice, and would not change the meaning of a correct program. Common Lisp
seems to have been the first to state this explicitly.  
  
**Thanks** to Trevor Blackwell, Robert Morris, and Dan Giffin for reading
drafts of this, and to Guido van Rossum, Jeremy Hylton, and the rest of the
Python crew for inviting me to speak at PyCon.  
  
  

