[APPLAUSE]
Thank you very
much, Hadley..
Today, I'm going to talk about
free and open-source software
for data science.
The lens I'm going to
use to talk about it
is kind of the how and
why of RStudio--
a little bit about how
the company got started,
and where our mission of
creating free and open-source
software derives from.
But then I want to get
into the ways that tools--
both open-source and proprietary
for scientific and technical
computing-- are developed.
How are they
financially supported?
How are they sustained?
How can you come to trust them?
And get into a little bit about
the nature of corporations
as stewards of this
sort of software--
and I'll ask the
question, are corporations
inherently sketchy as stewards
of this sort of software?
Spoiler-- they are.
So then I want to talk
a little bit about what
we can do about that.
So I'll start by a
little background
about how the
company got started
so you get a flavor for
where I was coming from,
starting the company.
The seeds of the company go
all the way back to 1983.
How many people here
have heard of Bill James?
People have heard a Bill James?
OK.
So Bill James was a math
teacher in Kansas City.
And back in 1983, I was a
avid follower of baseball,
and I absorbed all
of the analysis
about baseball from writers,
and sportscasters, and players,
and coaches, and experts.
And I came to believe
a lot of things
I was hearing about
how baseball teams win
games, how you assess the value
of players and strategies.
So what Bill James
did, interestingly--
you can see back in 1977, his
first baseball abstract was
this little pamphlet--
he went from that
pamphlet over 10 years
to a New York Times bestselling
book, where he used data--
empirical data analysis
to systematically debunk
many of the assumptions
that people had
about how baseball works.
And for me, as a
14-year-old, that
was really striking
that all these people
who had spent their lives
in the game of baseball,
who had developed conclusions
and intuitions about how it
worked, that all that
could be debunked
by systematically using data.
It was shocking to me,
and that stuck with me.
When I went to college,
I didn't go to college
for baseball studies.
I went to college for
political science.
And when you talk about
political science,
you get into public policy,
and public policy decisions
are made that affect
the well-being
of many, many hundreds
of millions of people.
And so the same thing
that occurred to me,
that we've got lots
of experts and we've
got lots of people who've been
absorbed in different fields
and observing phenomena
for years making
these decisions, and possibly--
probably, as it turned out,
not actually using data
to inform those decisions.
And as I've become part
of the R community,
I've realized that the
same phenomena repeats
in fields like
medicine and business--
that people are making highly
consequential decisions,
and they're not availing
themselves of the tools
they have to really understand
how the world works.
So this then, to
me, through college,
struck me as the fundamental
problem to solve.
And it seemed to me that
software was an important part
of the answer.
This is some of the
software that I use
in college and graduate school.
And say what you might
about some of this software,
I had the experience of
software providing leverage.
My mind and what I could absorb
was amplified by the fact
that I could use the software
to better understand things.
So from there, I went
to graduate school
at the University
Wisconsin Madison,
studying political science.
And this is going to be
an aside that this group
I think we'll find interesting.
it's not really in the
main thread of the talk,
but I worked on a study
there that was studying
the effectiveness of
vouchers and public school
performance in Milwaukee.
Milwaukee was one of the
first, if not the first cities
to implement a school
voucher program,
and there was a study
going on in Madison
that was going to assess how
effective school vouchers were.
And I worked on that study.
So at the same time, there
was a group at Harvard
that was very convinced
that the conclusions that
would be drawn by the folks at
Madison were going to be wrong.
So they were very keen to
reproduce and criticize
the results of the study.
So they asked for the data.
They said, could we
please have the data
so we can do our own analysis?
So we have the data
in Paradox databases
that were made machine-readable,
super easy to send to them.
But what we did
instead-- true story--
was we printed out all
the data and shipped
them crates of paper
so that they could
reenter the data on their own.
So that's kind of where we were
in reproducibility and academia
back then.
So that's a true story.
So anyway, so when
I got to Madison,
I was really excited about
data analysis and computation,
and I was like,
could I specialize
in software for data analysis?
And unfortunately, in
political science at that time,
software was definitely
not something
you could specialize in.
And at Madison,
even data analysis
was kind of barely something
you could specialize in.
So I was kind of
in the wrong place.
When I was supposed to be
doing my graduate school,
work I was teaching myself how
to program in C++ and teaching
myself how to program the Mac.
And that inevitably led
shortly to me dropping out
of the program and saying, I
want to be a software engineer.
I alluded to this a
little bit before-- what
fascinated me so much about
being a software engineer
is summed up well by this quote
from Steve Jobs, where he says,
what a computer is to me--
it's the most remarkable tool
that we've ever come up with.
It's the equivalent of
a bicycle for our minds.
And the other thing he cites,
when he recounted that quote,
was there was a
Scientific American study
that looked at the efficiency
of different modalities
of motion--
the cost of transport, calories
per gram per kilometer, as it
relates to body weight.
So they looked at organisms.
Salmon were super efficient.
Horses were super efficient.
They looked at
different modalities
of human
transportation, and they
found that a person on a bicycle
was by far the most efficient.
And that's, I think,
what computers are
and what software is.
So that became my
fascination, and I
decided I want to become
a software engineer
and I wanted to
build software tools.
So I did that for quite
a number of years.
I built programming tools.
I built research tools.
I built some writing tools.
And the software
I worked on was--
had a couple of characteristics.
One was it was
proprietary software.
Two was I was working
in startup companies.
And proprietary software
worked on in startup companies
is almost--
it has the seeds
of its demise built
in from the beginning,
because startup companies are
built to be sold.
And usually, when
they're sold, they're,
in some form or fashion,
destroyed or warped.
And the proprietary
software is often
very bound up in the fortunes
and fate of the companies that
sponsor its development.
So I really enjoyed
working on software tools,
but I found that proprietary
software in startups
was not something I wanted
to do anymore after that.
So I was searching for, what
would I like to do next?
And I came to the
conclusion that one
of things I wanted
was I wanted to build
tools that were durable, that
could outlast a given company.
And I wanted to build
tools that were accessible
to everyone, that anyone could
use, irrespective of cost.
And that led me to
the idea of working
on open-source software.
So I knew I wanted to work
on open-source software.
I knew that I didn't want
to do software startups.
At that time, I found out about
R, which kind of took me back
to the work that I had
done in data analysis
as an undergrad graduate school.
I don't know how
long it took me.
It was definitely
less than 24 hours
to conclude, this is what I
want to work on, at the time,
I felt like maybe for
the next 10 years--
now I feel like for
the rest of my career.
So I was very lucky
to discover R.
And it also felt to me like
I could offer something
to the community, because I
had worked on programming tools
and tools to make
people more productive
with complex software.
So I also didn't
want to do a startup,
and I thought,
well, this is fine,
because I think
one or two people
could actually make a
significant contribution.
We wouldn't need
to have a startup--
we could just make
a contribution.
And so that's when I started
working on the RStudio IDE.
And I worked on it
initially by myself,
and then Joe Cheng,
who I'd worked
with in a couple of
previous companies,
joined me a few months later,
and together, we set out
to build the RStudio IDE.
And the general
mission was to try
to make a contribution
to open-source software
for statistical computing.
So I want to now
say, why is free
and open-source software
for science and data science
so important?
At the time, I was focused
on this idea of durability
and accessibility, but as
I've joined the r community,
I've come to realize there's
lots of other good reasons
to prefer open-source
software for data science.
So I want to get into
a little bit of that.
And I first want to
make a distinction
between different
senses of the word free.
There's for free-- gratis--
and there's with little
or no restriction-- libre.
And both are
relevant, obviously,
with open-source software.
Famously, Richard Stallman
summarized the difference
and the nature of libre as think
free as in free speech, not
free beer.
We sometimes, with
free software,
focused too much on the fact
that the software has no cost,
but the most important
thing is that it
comes without restrictions.
And they actually--
GNU summarizes the four
essential freedoms of free
and open-source software, and
they mostly have to do with
being able to do with the
program what you wish,
including inspect it, modify
it, create your own derivative
works from it--
and the fact that
you are not dependent
upon the original purveyor
of the software to continue
using or evolving the software.
So that's actually, for science
especially, more important.
The fact that it
comes without cost
is important-- that's
actually even more important.
So what are some
of the reasons why
we want to prefer free
and open-source software?
I don't need to speak
at any length about this
with this group, but it's
worth reflecting on the fact
that, if I use proprietary
software to do science or data
science, and someone
else wants to reproduce
my results, at the
best, that person
needs to buy a license for
the software that I used.
But at worst-- and
this often happens--
I can't even reproduce my
own work in the future,
because maybe the vendor who
created the software has gone
out of business, or
they've older versions
of their products inaccessible.
So long-term reproducibility
is really only assured
by using free and
open-source software.
I want to point out briefly
just how important the R
community has been
in this movement
toward reproducibility.
There was an article
written in Nature in 2012
making this case.
And at the time,
they cited there
are two systems known to enable
the packaging of code data
and text at the time.
One of those two was Sweave,
which the R community actually
came up with in 2002.
So we've been at
this for 18 years--
certainly longer than any other
programming language community.
There's another consideration,
which is resiliency.
As I said before, software
products and companies
come and go.
We don't want our
research, our ability
to reproduce the
research, tied to the fate
of a specific product or vendor.
Now, a variation on this
theme is that software
doesn't come and go.
It actually stays and becomes
really, really important
to customers, and
then the vendor
decides, oh, this is
opportunity for us
to dramatically raise
prices and extract
more value from our customers.
So notably, the four essential
freedoms that I talked about
ensure that this cannot
happen with free software.
There's a great example of
this from the database world,
where MySQL--
which is a GPL open-source
database-- was acquired by Sun,
and then subsequently, Sun
was acquired by Oracle.
So Oracle, a proprietary
database vendor,
now owned all the
copyrights to MySQL,
and they were gearing up to
try to do all kinds of things
to try to exploit that position.
Even though Oracle actually
owned all the copyrights
for MySQL, the community
took the code for MySQL,
forked it, and created
a another product
called MariaDB, and continued
on with development.
So only the fact
that that software
was free and open-source
ultimately protected
the community from a vendor
that was going to be abusive.
And we can think,
in the R community,
RStudio and
many other vendors
have provided offerings around
R. The commitments of vendors
can vary over time.
Companies can get acquired.
They can shift strategies.
If, as a user, your primary
investment is an open-source R
code that will run irrespective
of any vendor's products,
then you're protected from that.
You have that resiliency.
There's another piece,
which is participation.
I think maybe 10
or 15 years ago,
many people might have believed,
well, a proprietary software
vendor can enumerate and
account for all the methods
that are important in a
field, and then provide that.
Or a lot of people
believed that.
I think now, the
explosion of innovation
in statistical
methodology, nobody
believes that-- that a single
vendor could be the filter who
decides what methods are
supported, and available,
and easy to use.
So the fact that we have an
open-source ecosystem around R
enables what you've
seen with CRAN,
where there's this huge
long tail of innovation,
there can be many, many
different approaches
to analysis, there can
be innovation in methods,
and it's all supported
by the software.
So participation's another
fundamentally important thing.
And finally, what I talked
about at the beginning,
accessibility--
and data literacy is
becoming-- it's fundamentally
important for organizations,
and it's also fundamentally
important for individuals.
And open-source
software allows everyone
to participate and use
these tools, again,
without regard for cost.
So let's talk then a little
more generally about how
these tools for--
not just data science, but tools
for scientific and technical
computing get built--
both proprietary tools
and open-source
tools-- how they're
built, how they're funded,
what the underlying structural
incentives are.
Looking at the history of
scientific and technical
computing companies,
these are leaders.
If you don't recognize
them by company name,
you certainly recognize
their products--
SAS, MATLAB, Mathematica.
And they actually have
some significant shared
characteristics-- all of them
started in academia by one
or two people, and over the
first few years of the project,
it was only one or two people.
And then eventually,
they organically spread
within academia, and then
grew into becoming companies.
All of these
companies, I would say,
if you look at their
about page or read
the writings of their founders,
or talks by their founders,
they identify their
principal mission
is to support research
and science, not profit.
And consequently, these
are all private companies.
None of these are
publicly traded.
They're all closely held
by the original group
that founded them.
And of course, notably, they all
make proprietary software, not
open-source software.
So what's problematic
about this mirrors
what I said about what's good
about open-source software.
We have reproducibility
problems,
accessibility
problems, centralizing
decisions about what
methods are supported.
And there's also that
risk of companies
that want to perpetuate
themselves being incentivized
to hold their customers hostage.
So those are some problems.
One really good thing
about these companies,
though, is that
they actually have
an economic engine that they
can use to fund development.
And without some kind
of an economic engine,
oftentimes, insufficient
progress isn't made.
The tools actually
don't reach the bar
where people can use them
to solve all the problems.
So they have some
significant drawbacks,
but they have that benefit of
having this economic engine.
So now, let's consider
open-source tools
for scientific and
technical computing.
The ones that the
people here are probably
most hands-on familiar with are
the ones the R and Python data
science ecosystems.
But there have
been lots of tools
before that-- notably,
SageMath and GNU Octave.
And these all have
roots quite similar
to the proprietary
software vendors.
They started in academia.
They grew very slowly and
organically at the beginning.
And the founders
of these projects
also were concerned
principally with supporting
research and science, and they
wanted to protect the software.
Their means of protecting it
was to make it open-source,
because at the time a lot
of these projects emerged,
the idea of
open-source had gained
more currency-- the viability of
open-source had more currency.
Linux had achieved some success.
So this is the approach that
these project have taken.
And so what's problematic here
is, do these projects have
enough funding to
sustain momentum and get
where users want them to be?
And honestly, over half
of software engineering
is solving boring
problems, and sometimes
you just need to
have the size of team
and a commitment of a
team to go and solve
those difficult and
boring problems.
Getting enough resources,
as I say, in these projects
can be a challenge.
And there's also an issue
of project organization.
Are these projects
cohesive enough
to deliver the software
that users need?
And then significantly,
are organizations
who are making
decisions about taking
big long-term dependencies
on software projects--
are they comfortable adopting
the software without visibility
into the long-term
health of the project?
Will the project be around?
So there's different ways
that people have come up with
to find open-source development.
There's grants.
And Jupyter has actually
done quite a bit with grants.
There's probably a natural
limit to how much funding you
can get from grants, but
Jupyter's done quite well
with that.
Open-source software can be
funded by companies that have
an interest in the software, and
Linux is the best success story
here, where lots of really large
companies have had an interest
in having a free--
robust, free Unix operating
system all contributed to it.
And that's the initial
model for Ursa Labs working
on Apache Arrow, where there's
a bunch of companies, including
RStudio, that invested
in building the software,
because we think it's
going to benefit our users.
There's also the traditional
method of venture capital,
but if you look at those
companies I talked about--
MathWorks, SAS, Wolfram--
they've been around.
The evolution of their products
and the adoption that products
took like 30 or 40 years.
They've been run
for a long time.
The venture capital model
is much, much shorter,
so I don't think it fits
particularly well with building
open-source tools for science.
So the question is, are
any of these-- do any
these models actually work?
And this question
was asked rather--
or answered rather
pointedly by a gentleman
from Wolfram, because
apparently, Wolfram gets
asked all the time, why isn't
Mathematica open-source?
And so he wrote a
blog post, 12 reasons
why it's not open-source.
And I'm not going to read
these reasons out loud to you.
If you take a
quick scan of them,
I think you get a sense
for what the reasons are.
And I would say, generally,
I agree with this analysis,
to the extent that I do believe
that you have to assemble
a group that works together
to achieve a set of shared
goals over a sustained
period of time,
you have to have strong
technical leadership
to solve hard problems, and
you do need a financial engine
that can compensate
talented people
to work on these problems.
That's kind of the
main gist of his case.
I agree that those
things are required,
but I actually think
it's possible to do it
with open-source software.
And I think that the history of
RStudio demonstrates that.
So if we look a little bit at
the history of the company,
2008--
late 2008 is, I believe,
when we started.
It was, as I said
before, just one,
or two, or three people working
on open-source software.
And then about seven years ago,
we decided that we weren't--
we thought we could
do a lot more with--
just having three people
working on open-source software
was fine, but we thought there
was potential to do a lot more.
And so we made a
decision at that time
to build a company around
the open-source work
we had done, with
the notion that we
could fund lots more
open-source development,
if we had a company
producing revenue
to fund the development.
So we did that, and you can
see, over the last seven years
or so, we've produced a huge
amount of open-source software.
We have, I think, over
250 open-source projects
that we are active
developers on,
and we have this set
of commercial products
that we sell.
And we've grown from that
original one, or two, or three,
six people to now
over 150 people.
Our company is
profitable, so we're
able to take the revenue
from our commercial products
and fund it back into--
feed back into
open-source development,
and then continue, as
the company grows to grow
that commitment over time.
So I think we've figured out
a model by which we can still
produce open-source
software, but do it
in this sustainable,
well-funded way.
We sometimes refer to
this as a virtuous cycle,
where we create open-source
software, and lots and lots
of people-- because
it's accessible,
lots and lots people use it.
And when lots and lots of people
use software, what happens
is large organizations
start to adopt the software.
And large
organizations typically
have deployment, and management,
and scalability requirements
that are different than
individuals or small groups,
and that creates an
opportunity for us
to build products to solve those
problems, which then gives us
revenue to invest back
in open-source tools.
Now, you'll notice
here-- it's pretty
subtle on this slide-- there's
a line between these things.
And you might ask the question,
well, where's that line,
and what's where's the
assurance that a company like
RStudio--
as I said, corporations
are inherently sketchy--
isn't going to move
that line at some point?
Let me give you the
operative principle that--
for us, that's behind that line.
The operative principle has to
do with preserving those four
freedoms.
So the core libraries, packages,
protocols, file formats,
even productivity tools,
like RStudio IDE need to be
open-source so that users who
adopt the software experience
the benefits of those four
freedoms, and essentially,
are not--
their work is not locked in
to the products of a given
software vendor.
The tools that we create
to facilitate adoption of R
in large, complex environments,
those tools are commercial.
Those are tools that
candidly, if we're not
providing good
value to customers,
they can continue using R,
continue using the Tidyverse,
R Markdown, Shiny-- everything
without those tools.
So for us, we have to
continue to offer a good value
proposition or customers
could walk away.
That's because the core software
preserves the four freedoms.
So that's where
we draw the line,
and how we think about
open-source versus commercial.
So I think we've
managed to create
a new kind of scientific and
technical computing company.
I think we have those attributes
that the gentleman from Wolfram
cited, which is that we
have a financial engine
that we can work on this in a
sustained way for many years
and apply adequate resources.
We also, like those
companies, think
it's critical that we
remain independent to pursue
our mission.
But we've managed
to do this in a way
where the core software
is open-source,
and we don't have lock-in
the way the software--
and that is by design.
So we're pleased with this, but
it begs the question, again,
I said about corporations.
Are we trustworthy?
And I would say we're
not, at face value,
trustworthy, because in
today's world, corporations--
And I'm going to get into
this in a little more detail
in a minute-- they are
pure profit maximizers.
And so they're-- by default, you
shouldn't trust corporations.
I think the R community
needs more than,
we're a corporation that
has acted well in the past,
in order to place their trust
in the work that we're doing.
And I think customers
need to trust
that we're looking for a
relationship of mutual benefit,
and that we're not
going to exploit
our position as a software
vendor in the future,
as has happened with
other proprietary software
companies in the past.
We need to make our motivation
as clear and transparent
as possible, and we
need to try to build
more real, long-term trust.
And I think some
of that has to do
with the nature
of corporations--
how corporations work, and how
maybe corporations actually
need to evolve.
So I'm going to go back.
This is a little bit elemental,
but I think it's not something
that I had thought really
carefully and critically
about until a few years ago.
What actually is a corporation?
Why do they even exist?
And what a corporation
is is actually--
it's a virtual person.
So it's acting as a
person-- legally a person--
but it's actually--
it's constituted
by a group of people.
So why do we even
have corporations?
Well, what did we do
before corporations?
Before corporations, any
business that existed
was either undertaken by
an individual or maybe
a partnership of individuals.
And so the individuals were
actually personally liable
for everything the
enterprise did,
and when a contract was
made between a business
and another party,
it was actually
a contract with the individual
or the group of individuals.
So when someone left a
company, the contracts actually
had to be renegotiated.
Business was really
fundamentally person to person,
and that meant that anything
that a business could
accomplish was bounded by
what one person's assets could
accomplish and
what liability one
person was willing to accrue.
So as the industrial
age progressed,
this model didn't seem adequate,
and the first corporations
were actually formed by royal
act, the East India Company
being one of the most
significant examples of that.
And then, as governments
realized that we needed things
like bridges, railways,
banks, utilities,
we need to operate
on a different scale
that individuals have
been able to operate at,
so they created this instrument
called a corporation that they
believed needed the sort
of absolving of liability
and additional capital that the
corporate model provided for.
So if you think about
then the essential nature
of a corporation,
it's an institution
that's actually
created by government
or to benefit the
societies they govern.
That was the original
purpose of corporations.
And this worked very well, and
served to the public benefit
in many in many regards.
But if you look at
that second bullet,
about without fear of
personal liability,
that actually is a
recipe for bad behavior,
and we've seen that
play out as well.
So what we have today with
corporations has worked well,
but I think we're seeing
it crack a little bit
in the contemporary world.
So if you think about the
legal theories of what
the primary purpose
of a corporation is,
there's actually two
competing theories.
One is stakeholder.
This is kind of
the original idea,
and the original idea that
I've been advocating for,
which is that the corporation
is created by the government,
and therefore, has
a social function.
And the directors and officers
of that corporation should
consider all the stakeholders
affected by the corporation--
employees, the environment,
the community, shareholders--
looking at
everybody's interests,
when they make decisions.
There's another theory,
which is shareholder primacy,
and it's a pretty
raw notion, which
is the purpose of
a corporation is
to maximize value
for shareholders
within the bounds of law--
really narrow.
And sadly, that is
actually the legal theory
that has won in
Anglo-American legal systems.
Shareholder primacy is the law
that we live under currently.
There's been a couple
significant cases
that establish that-- or many
cases that have built that up.
A couple of the more
significant ones
was, in 1919, Ford
decided that they wanted
to produce less
expensive products
and pay their employees more,
so they stopped paying dividends
to shareholders--
again, balancing the needs of
all different constituencies.
And the Michigan Supreme Court
said, that's not a thing.
You're not allowed to do that.
Your purpose is to
create profit first
for shareholders, full stop.
You can't do that.
Another case-- a little
more contemporary--
Revlon was faced with
an acquisition proposal
that actually--
that appealed to
the shareholders.
It was a good deal for
them, but the board
felt that it was going to not
be a good deal for the employees
or the bondholders-- people
who actually held debt.
And so they tried to block the
acquisition, and in this case,
the Delaware Supreme
Court rejected the idea
that they had the duty
or even the option
to consider the
interests of stakeholders
other than shareholders.
So there's been a number
of cases like that,
and that's kind of where we
stand with corporate law.
And many people
find this lacking.
Companies, while
they're pursuing profit,
can create lots and
lots of public health
and environmental problems.
They can create systemic
risks, as we saw
with recent financial crises.
It leads back to
the question-- is
that we've given
these corporations
a special legal status.
Does that carry any reciprocal
obligation to the public good?
And it's, shouldn't
companies be able to consider
the welfare of their own
employees and their community,
when they make decisions?
A lot of the bad behavior
you see by corporations
is precisely because they
can't consider-- legally
can't consider these things.
So in response to
this, a bunch of states
have actually adopted
legislation that permit--
note, permit, not require--
permit directors
to consider things
other than shareholder value.
And now we've seen recently,
this last year, there's
this group called the
Business Roundtable that's
composed of a bunch of CEOs of
mostly big public companies.
And for the first
time since 1997,
they in a public statement
said that corporations
shouldn't exist solely
to serve shareholders.
I think people are sensing
that this regime is wrong.
This isn't really talking
about fundamentally
changing the system.
This is just more saying,
we have dissatisfaction
with the system.
So what can be done
to change this system?
So I don't know how many of you
have heard of the company AND1.
Have people have heard of AND1?
It's a basketball shoe company.
And I think, as
it will turn out,
this company will
be very significant
in the history of corporate
law, and perhaps, of capitalism.
So let me tell you a
little bit about AND1--
baseball shoe company
founded in 1993.
They were a socially
responsible business.
They treated employees really
well, had great benefits.
They allocated 5% of their
profits to local charities.
And significantly,
they're a shoe company,
so they had overseas factories.
They worked to implement
a supplier code of conduct
to make sure that workers
in overseas factories
were treated well, safely,
had good wages, et cetera.
So they were a socially
responsible business
before that became something
more widely practiced.
So what happened to AND1?
Well, they were pretty
small in the mid '90s.
They took on external
investors in 1999,
and it turned out that they
grew quite a bit over that span,
from '95 to 2001.
But then, ultimately,
they had competition
from Nike and others,
and their sales dropped,
and they were forced
to sell the company.
And what was really
surprising and disturbing
to the founders was--
who created this
company with the idea--
we created the company,
we control the company,
we want to build a socially
responsible business.
When they went to
sell the company,
it was done exclusively to
maximize shareholder value.
And so after the sale--
and they could do
nothing about this--
all those commitments
to employees
or overseas workers
and local community
were just stripped away.
So they were shocked
and disappointed,
and it made them think
something needs to change.
So they got together
with a friend of theirs
to start a nonprofit called B
Lab, and the idea behind B Lab
was to create a new form
of corporate governance.
So they created the
nonprofit, and they actually
created a new corporate
structure called a benefit
corporation.
And this is a reaction to the
shareholder primacy regime,
where the directors
of the company
are legally required to account
for all of the stakeholders--
community, employees--
in their decisions.
It's a legal requirement,
not an option.
And they also name a
public beneficial purpose
as part of their charter
that, again, they're
accountable to pursue--
legally accountable to pursue.
I won't read this
in detail, but you
can see a little bit of
the actual legislation
behind Delaware public
benefit corporations,
and it puts a stringent legal
requirement on the directors
to act in a different
way, and to not use
the shareholder privacy regime.
So this nonprofit
has actually been
successful in getting 34
states to pass legislation that
permit benefit corporations.
There's currently
over 7,000 of them.
These are some examples.
You've probably heard of
some of these companies,
and you might not have known
that they were a benefit
corporation.
In addition to these companies,
there are some public company--
so the line at the top
are public companies.
They're not benefit
corporations,
but they have
wholly-owned subsidiaries
that are, in fact,
benefit corporations.
So I think this benefit
corporation idea
has the seeds of what it takes
to transform what corporations
are and how they
relate to their world,
and to their communities, and
all of their stakeholders.
And it kind of takes this idea
of good companies that has
existed over the last 20 years--
whether that be about Energy
Star compliance, or fair
trade, or organic food--
and it elevates that to
the idea of good companies.
So most of you or many
of you can probably
guess what the next slide
of this presentation's
going to be.
I'm really happy
to announce today
that we are now a certified
Delaware Benefit Corporation.
[APPLAUSE]
So we actually have a new name.
We're no longer RStudio,
Inc. We're RStudio, PBC.
And we've always tried to run
this-- the company this way.
That's always been
what we've tried to do,
but now it's actually
baked into our charter.
It's part of our corporate DNA.
It's a requirement,
not something
that we do at our discretion.
As part of it, we actually
name a public benefit,
and that actually
goes into our charter.
This is our public benefit.
And you note that
we cite the creation
of free and open-source
software for data
science, scientific research,
and technical communication.
It's a little bit broader
than data science.
Today, we were
just data science,
but I'm optimistic that we have
created a model that actually
is a better model for scientific
and technical software.
So someday, I think it
would be nice to do more
within scientific
computing, so that's
why we wrote the public benefit
a little bit more broadly.
As part of being a public
benefit corporation,
we actually will
release an annual report
describing how we've served
our public beneficial purpose.
We've posted the first of
those reports on our website.
A few highlights-- we
provide some metrics
around our investment
in open-source projects.
As I said before,
there's over 250 of them.
We dedicate over half of the
company's engineering resources
to open-source.
Right now, we have 36
full-time engineers
that work on
open-source software,
and that's broken down
by project in the report.
And there have been
hundreds of millions
of downloads of our open-source
products and packages.
So these are metrics
we're going to keep
reporting on every year.
So in addition to being
a benefit corporation,
the B Lab is actually created
a certification program
that both looks at whether
you've changed your charter,
but also looks at your impact
specifically on your workers,
customers, community,
and environment,
and actually rates you
across a bunch of categories.
And so we're happy to also
share that we've been certified
as a B Corp by the B Lab.
a You can see our
impact report there,
which actually has all those
ratings, is also available now.
So some of this, though,
begs the question--
we've become a B Corp, and that
reflects, I think, who we are,
and what we're about,
and what we aim to be.
But it also doesn't necessarily
talk about the future.
And I want to tell everyone
here that our plan is
to remain an independent
company, to never sell
the company.
Yes.
So that is what
we're going to do.
[APPLAUSE]
But how can we actually
make that happen?
How can we provide assurance
that's going to happen?
We do have outside investors
as minority shareholders.
And prior to this
conversion, we actually
had written into the
financing documents,
I had special rights that
I could block, what I say,
undesirable outcomes.
I could individually block
the sale of the company.
But just saying that I can block
that the sale of the company
isn't really fully reassuring,
because what if I die?
What if I change my mind?
So it's a little overreliant
on one individual,
so we also, along
with the transition,
made some changes to how
those shares are held
and how the rights
are exercised.
So now, there's actually
a group of people
all inside the company
who exercise those rights.
So if I die, or change my
mind, or anything like that,
we still have those
protections in place.
And before I conclude,
I wanted to also talk
about who ultimately benefits
from RStudio's success.
As I've said in the
talk, we've tried
to build a company
where we have lots
of beneficiaries-- all of our
stakeholders are beneficiaries.
We do have shareholders,
and the traditional way
that shareholders
get remunerated it
usually either selling
the company going public.
That is not our plan.
So what we're going to
do is take our profits
and use those to
purchase stock back
from our shareholders over time.
So once we've met
that commitment,
we are going to dedicate
a substantial portion
of our profits to
philanthropic causes that
relate to our mission
of open-source software
and open science.
And those donations--
we've documented
in this year's annual report
the donations that we've made.
But as we are able to
purchase stock back
from our shareholders and
dedicate more of our profits
to these donations, we'll
also report specifically
on that in our annual
public benefit report.
So thank you all
very much for helping
us to build this company
and build this community.
It's been an incredible
experience, far
exceeding anything I
could have ever hoped for,
and I'm excited for
what the future holds.
Thank you.
[APPLAUSE]
