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Forbes Books presents the Startup Science Podcast
with Gregory Shepherd, brought to you by
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Startupscience dot Io. Greg is an
entrepreneur who has built and sold twelve businesses.
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He's a recipient of four private equity
awards and is a featured ten X
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speaker and Forbes author. Here's Gregory
Shepherd. My guest this week is Heather
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Meeker, a leading expert on open
source software, licensing and business strategy.
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She's the general partner at OSS Capital, an early stage VC fund exclusively investing
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in commercial open source software startups.
We're also going to talk about her latest
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book, From Project to Profit,
How to Build a business around your open
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source project. Heather, Welcome to
the podcast. Oh thank you. It's
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pleasure to be here to talk to
you today. When we look at open
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source and building and scaling open source
software businesses can seem like magic right and
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at least smoking mirrors in some cases
where you're talking to open source people and
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they're trying to get people to understand
what open source is in the first place
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and how that could even become a
business. So I think to start out
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with just to get everybody leveled up
a little bit here Over the last thirty
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years in open source. You're a
massive specialist in this area. One of
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the questions that I get from people
all the time is how is open source
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a business? Like? How do
you take something that's where engineers are developing
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code that's available for anybody to use
and turn that into a business in the
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first place. Yeah, there are
two things to keep in mind about that.
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It can kind of seem like magic
are smoking mirrors, But if you
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take a step back and you think
about a couple of things, it makes
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a lot more sense. One is
that if we look at the way business
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works today as opposed to maybe the
way it worked ten twenty years ago,
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we've got a lot more businesses that
offer a lot of free stuff and then
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some paid stuff. We're all used
to that now. It's mainly because of
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the advent of the Web and the
advent of digital goods and technology. It
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makes it easy to make stuff available
to people to try out, and so
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we're all accustomed to this kind of
free goods model, and that's part of
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what's going on with open source.
The other thing that's super important to understand,
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and I actually think even most people
in Silicon Valley in the tech business
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don't really get this, so I
love to talk about it. You know,
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is that open source it's a license
to do things with source code,
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an unrestricted license to do things with
source code, and those are intellectual property
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rights. It's basically a grant of
a copyright license. But nobody sells intellectual
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property grants. It might seem like
people do on the surface, but even
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if I, let's say I buy
a subscription to a software product, I'm
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not really buying the intellectual property.
What I'm buying is a product that does
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something for me. And there's actually
a delta between say, the source code
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for that thing and the packaged up, served up product that does something for
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me. And the difference between those
two is mostly what open source businesses sell.
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And it's actually it adds an enormous
amount of value between just kind of
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the bare intellectual property license and the
actual product that's put together that people can
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actually use to solve problems. M
h. And you would know, as
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somebody that was named Business Insider in
one of the top ten people transforming the
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way technology does business along with this
CEO of Salesforce Drike like, I mean
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like that was like my little surprise
for the listeners, I'm sure many people
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are like sitting up in their chair
now going, oh, wait a minute,
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Wait a minute. So when you
look at this open source community and
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you look at what's happening with AI, do you see AI starting to move
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into more of an open source environment
or do you think it's going to be
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more privatized. So I personally think
that unless AI becomes quote unquote open source
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to some degree, it's going to
have a lot of problems. The thing
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about AI is that we really don't
have models for what open source AI means
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because AI, you know, machine
learning models are not source code. They're
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some they're a different creature. And
we have this great model for open source
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and open source licensing that we've been
using for like thirty years and everybody loves
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it and it's proven it's worth and
everything, but it's very hard to graph
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that onto AI because because of the
difference in the technology. So just to
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kind of explain what I mean,
if you want to give someone the ability
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to study a software program and see
how it works and maybe think about improvements,
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you give them the source code,
and if they're skilled in programming and
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so forth, they'll get there.
Like they'll look at the source code,
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they'll be able to figure it out
see what changes they want to make.
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Machine learning is different. It's not
even clear what you really all the stuff
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you really need to give people in
order to make models transparent and improvable and
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so forth in the industry has not
figured that out yet. So we're really
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in a state of flux with AI, where a lot of people want to
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apply the open source model to it, but it's actually a little bit difficult
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to figure out how to do that. I've been spending a lot of time
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talking about that and so forth for
the last year, ever, since this
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stuff just blew up about you know, a year ago, and people are
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working on figuring out what it means. Personally, I think that unless we
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can figure out a way to make
machine learning models more transparent and accountable,
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they risk getting basically regulated off the
face of the earth, or being just
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in the hands of really big companies
that can, I don't know, like
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figure out how to make people accept
them notwithstanding their lack of transparency or to
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your point, the transparency for the
listeners to understand is that machine learning goes
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through processes where it starts thinking and
it does things right, and then it
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comes out with an outcome. And
the part that everybody seems to be worried
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about is how did it get to
the outcome exactly? And it's not a
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set of instructions. I think that's
something that like most people just are not
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focused on. Like with software,
there's a set of instructions that was written,
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you know, by a person or
at least checked by a person,
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and so if you want to see
how did it get from A to B,
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you can actually go through and follow
the logic. But that's not how
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machine learning works. Like the transformational
models, they they just kind of you
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put something in. It's kind of
a black box and it comes out the
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other end. And that's a great
concern to people because when you where you
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try to figure out, well,
did it make this decision properly or fairly?
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It's actually quite challenging to figure out. Yeah, I mean, it's
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incredibly challenging. And one of the
fears that I keep hearing about people keep
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saying is, you know, is
this going to make us dumber? It's
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basically doing the thinking for us,
right, Like, yeah, you would
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normally delegate to somebody You're like,
hey, can you go figure this out.
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Can you go do this research and
come back to me with this.
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That's the kind of work that AI
seems to be for right now quite often,
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right and I mean we use it
right but then you know, you
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can't help it to think, well, is this going to make us dumber
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because nobody's actually learning because the AI
is doing the work for us. What
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are your thoughts on that? Yeah, I don't. I'm on the fence
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about that. I mean, there
are a lot of things that AI can
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take some burden off of us for
doing repetitive tasks that humans have had to
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do. One of the examples I
use, because I'm a lawyer, is
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that like maybe a little bit before
I started practicing, so be in you
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know, the late nineteen eighties,
when people negotiated contracts, they would you
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would send someone a document showing the
differences between one version of the contract and
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the others, called a red line
in legal practice. And people used to
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do those by hand. And today
no one would ever do one of those
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by hand. Those like programs that
do them. The programs are more reliable
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and probably more worthy than the humans
that we're doing now. And so there
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are a lot of tests like that
that I think it's actually great if we
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can free people up to do tasks
that require more sophisticated mental energy. But
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on the other hand, the machine
learning models are very good at spitting out
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what I would call kind of plain
vanilla pros. And one thing I do
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worry about is that people won't learn
to write anymore. And that's been kind
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of a problem, honestly for those
of us who have been in sort of
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management positions for knowledge workers, right, is that it's hard to find people
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who write well, you know,
And I think it gets harder and harder
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every year. And I'm actually kind
of worried that people they're just not going
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to learn anymore because they don't have
to. What the way you learn to
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write is just by producing a whole
lot of writing. You have to do
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it over and over again. So
yeah, it'll have good and bad effects,
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I'm sure. So let's talk a
little bit about this book. So
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you have there's a lot of different
themes in the book software entrepreneurship, investing,
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demystifying the process of building a scalable
open source business, and I want
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to talk a little bit about that. So I want to shift topics a
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little bit into that. One of
the things that I learned is when I
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was looking at the content surrounding open
source, and that I hear all the
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time is why should somebody start an
open source business? Why is this a
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good investment? And what does it
come down to? And maybe you could
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shed some light on some of that. Yeah, I'm a VC as well
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as a lawyer, and I specifically
invest in commercial open source software development,
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and I think it's a great investment
for many reasons, just you know,
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touch on a couple. The first
is that when you're an investor and you're
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looking at, say a company you
want to invest in, one of the
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things you do is you you go
through a diligence process to try to figure
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out, well, how good is
their technology and how you know, how
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good is the market in, are
are people going to be interested in the
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product? And so forth? With
open source, you can tell a lot
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of about that stuff before you even
engage with the company, because usually what's
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happened is they've thrown an open source
project out there and you can see,
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oh, it has X number of
stars, it has x number of contributors.
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I can actually look at the software
and see if any good you know,
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so it actually de risks the investment
a lot if you're interested in investing
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a business in a business that surrounds
the open source software on the entrepreneurs side
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open and the investors side. Like, open source businesses are incredibly capital efficient.
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They you know, long before the
pandemic, open source collaborators were collaborating
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from across the globe. They were
virtual. They don't need offices. They
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run lean and mean, and that's
the kind of business that's good for both
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investors and entrepreneurs. So I can
give a business half a million a million
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dollars and they can do amazing stuff
with it, and I can figure out
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in advance, like whether people seem
interested in the product, and that's awesome,
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I think for both sides. Anytime
you have something that allows somebody to
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start a business with less capital,
it means more innovation, and the capital
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that the investors provide gets used,
you know, with more leverage and more
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efficiency. So as essentially, like
what you're saying is like you can earn
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and learn about product market fit.
Yeah, you can exactly learn out the
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personas that you're dealing with and the
icps that are interested and all the little
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things that present risk if they're unknown, because those things have already been launched
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and the numbers are already in.
Is that yeah, exactly exactly. And
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you know, I hear people say
all the time, well, GitHub stars
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are kind of you know, I
shouldn't pay that much attention to it,
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and I understand that. But if
you look at how many contributors there are
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to a project, how many downloads
there are of the project, how engaged
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the community is, you have amazing
intelligence about product market fit, and that
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kind of thing is hard to buy
with a regular business. So it's really
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exciting. Yeah, I mean,
and anything, you know, the naysayers
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that are out there, they always
you know, there's always people out there
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that saying, well, you don't
get this and you don't get that,
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and you're like, well, it's
better than nothing. How about we start
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with that? Yeah, exactly.
Another great thing is that open source businesses
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get a lot of really good feedback
from their communities. So you know,
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how you use products, and they're
always asking you surveys and all this horrible
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stuff like how how would you recommend
this and so forth. Well, the
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open source projects get tons of feedback
from their community, and that stuff is
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incredibly valuable and you get it for
free, Like you don't even have to
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send marketers after people to talk about
it. So there's just a lot of
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aspects of running a business around open
source. If you if you do it
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properly that you know, it's it's
a it's a huge benefit to the people
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running the company and therefore their investors. Yeah, I mean you get the
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engagement right out of the bat.
Yeah, because they're participating, right,
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so exactly in the game, and
they're willing to engage, and you're right
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it the you know how many times
a day you get the NPS email,
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you know, the net rotor score
email it's like would you recommend? And
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you're like, oh my god,
yeah, and you get like thirty of
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them a day and these different surveys
all the time, and I think people
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abuse the hell out of that process. But yeah, and in open source,
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people will open an issue and they'll
do it for you, right,
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yeah, somebody else will solve the
problem. Yeah, It's like, you're
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right, it's a pretty it's a
pretty innocuous process. Yeah. The corollary
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of that though, is like for
open source businesses, they have to pay
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attention to their community, like you
can't just start a business around an open
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source project and then expect your community
to just give give, give to you.
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You have to give back to them. You've got to respect them,
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you've got to pay attention to them, you've got to take their concern seriously.
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But if you do that, it's
a wonderful partnership between the community and
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the company, and you can,
you know, use that to leverage what
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you can do with the commercial product
end of it, and you can use
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the commercial product revenue to leverage what
you do with open source. That's kind
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of the dance that you have to
do with a business like that. So
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let's talk. We have a lot
of hackers that listen to the show,
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right from the different hacker angel Hack
and all the other communities, and so
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what should developers consider if they're thinking
about going into the open source business?
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What should some of the things that
they should be thinking about. First thing
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they should think about is whether it
really should be a business. So this
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is one of the things I talk
about in my book. And the answer
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to that, there's no wrong answer
to that. It's just that based on
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the answer to that, you go
in different directions. So, for instance,
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if I'm a developer and I'm I'm
scratching an it. You know,
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I'm thinking this thing really needs to
be written, and I'm going to sit
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down and write it and spend my
hours of nights a weekends doing it.
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And I do that. I might
do that because I just wanted to do
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it. Maybe I want I did
it because I wanted to kind of put
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it on my resume. I might
it might be a hobby for me,
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and then it might start becoming a
business. But even if it does,
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it might be in different levels,
Like do I want to start a business
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where maybe I'm going to become a
professional services provider, do some maintenance and
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support for people and so forth,
create a living for maybe me, maybe
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a couple of my friends. And
that's fine, right, But then there
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are some people who really want to
take the open source project and turn it
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into a big business. So depending
on what you want out of it,
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what your time horizon is for an
exit, how big you want the project
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to be. Do you want to
actually manage of business or you just want
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to work for yourself. Based on
that, you get to the answers about
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how are you going to run the
business, how are you going to fund
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it? What are your goals?
So that's one important thing. The other
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is that you have to have a
business model for what you're going to do,
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and it doesn't need to be perfect. You just have to have an
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idea. If you go and you
ask investors for money, you say,
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I have this project with ten thousand
stars and it's truly great and people love
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it. The next thing they're going
to ask is, Okay, what are
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you selling? Who are you selling
to? How are you going to make
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money? And you need to have
answers to that, even if even if
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they're just your best guesses, but
you have to have answers to that.
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And I think that some open source
developers can tend to engage in a little
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bit of magical thinking about well,
stars means money, it's not doesn't quite
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work that way, but you can
get from A to B if you plan
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it out and you think about it. M yeah, I think that's where
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I was coming from with the comments
of you know, black magic and some
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over the mirrors. A lot of
the engineers, you know, when I
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look at those deals, you know, they come in and the engineers like,
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well, I've got five stars and
I've got all these people contributing and
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you're like, yeah, they're not
buying anything. Yeah, right. That
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can be a fine outcome, but
it's not the outcome that will get you
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funding, right right, So you
just have to think about, like,
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what is your objective? What do
you really want to get out of the
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project. So I wanted to switch
things up a little bit here. We
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talked about founder engineers. They're creating
all this stuff on one side of the
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equation. Now I want to shift
to the investor side, but we have
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to stop here. Also coming up
next time on the Startup Science Podcast,
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Heather, we'll show a good vertical
to dive into when it comes to open
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source. If you had to pick
one business model that tends to work really
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well with open it's what we call
an opencore model, backed up by SaaS.
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So what you do is you develop
some core functionality and you release that
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as open source, and then around
that you build software as a service.
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The Startup Science Podcast with Gregory Shepherd
is brought to you by Startupscience dot io.
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00:20:23.400 --> 00:20:30.039
Founders can log on too startupscience dot
io and use code Forbes to get
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00:20:30.039 --> 00:20:36.440
free access to connect and find out
more about greg Go to Gregoryshepherd dot com.
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00:20:36.920 --> 00:20:41.039
The Startup Science Podcast is a production
of Forbes books.
1
00:00:01.280 --> 00:00:07.040
Forbes Books presents the Startup Science Podcast
with Gregory Shepherd, brought to you by
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00:00:07.080 --> 00:00:13.720
Startupscience dot Io. Greg is an
entrepreneur who has built and sold twelve businesses.
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00:00:14.160 --> 00:00:18.760
He's a recipient of four private equity
awards and is a featured ten X
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00:00:18.800 --> 00:00:24.320
speaker and Forbes author. Here's Gregory
Shepherd. My guest this week is Heather
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00:00:24.440 --> 00:00:29.120
Meeker, a leading expert on open
source software, licensing and business strategy.
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00:00:30.160 --> 00:00:37.000
She's the general partner at OSS Capital, an early stage VC fund exclusively investing
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00:00:37.039 --> 00:00:42.079
in commercial open source software startups.
We're also going to talk about her latest
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book, From Project to Profit,
How to Build a business around your open
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00:00:45.799 --> 00:00:50.000
source project. Heather, Welcome to
the podcast. Oh thank you. It's
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pleasure to be here to talk to
you today. When we look at open
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source and building and scaling open source
software businesses can seem like magic right and
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at least smoking mirrors in some cases
where you're talking to open source people and
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they're trying to get people to understand
what open source is in the first place
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and how that could even become a
business. So I think to start out
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with just to get everybody leveled up
a little bit here Over the last thirty
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years in open source. You're a
massive specialist in this area. One of
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the questions that I get from people
all the time is how is open source
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a business? Like? How do
you take something that's where engineers are developing
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code that's available for anybody to use
and turn that into a business in the
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first place. Yeah, there are
two things to keep in mind about that.
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It can kind of seem like magic
are smoking mirrors, But if you
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take a step back and you think
about a couple of things, it makes
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a lot more sense. One is
that if we look at the way business
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works today as opposed to maybe the
way it worked ten twenty years ago,
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we've got a lot more businesses that
offer a lot of free stuff and then
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some paid stuff. We're all used
to that now. It's mainly because of
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the advent of the Web and the
advent of digital goods and technology. It
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makes it easy to make stuff available
to people to try out, and so
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we're all accustomed to this kind of
free goods model, and that's part of
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what's going on with open source.
The other thing that's super important to understand,
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and I actually think even most people
in Silicon Valley in the tech business
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don't really get this, so I
love to talk about it. You know,
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is that open source it's a license
to do things with source code,
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an unrestricted license to do things with
source code, and those are intellectual property
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rights. It's basically a grant of
a copyright license. But nobody sells intellectual
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property grants. It might seem like
people do on the surface, but even
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if I, let's say I buy
a subscription to a software product, I'm
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not really buying the intellectual property.
What I'm buying is a product that does
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something for me. And there's actually
a delta between say, the source code
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for that thing and the packaged up, served up product that does something for
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me. And the difference between those
two is mostly what open source businesses sell.
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And it's actually it adds an enormous
amount of value between just kind of
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the bare intellectual property license and the
actual product that's put together that people can
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actually use to solve problems. M
h. And you would know, as
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somebody that was named Business Insider in
one of the top ten people transforming the
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way technology does business along with this
CEO of Salesforce Drike like, I mean
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like that was like my little surprise
for the listeners, I'm sure many people
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are like sitting up in their chair
now going, oh, wait a minute,
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Wait a minute. So when you
look at this open source community and
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you look at what's happening with AI, do you see AI starting to move
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into more of an open source environment
or do you think it's going to be
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more privatized. So I personally think
that unless AI becomes quote unquote open source
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to some degree, it's going to
have a lot of problems. The thing
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about AI is that we really don't
have models for what open source AI means
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because AI, you know, machine
learning models are not source code. They're
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some they're a different creature. And
we have this great model for open source
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and open source licensing that we've been
using for like thirty years and everybody loves
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it and it's proven it's worth and
everything, but it's very hard to graph
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that onto AI because because of the
difference in the technology. So just to
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kind of explain what I mean,
if you want to give someone the ability
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to study a software program and see
how it works and maybe think about improvements,
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you give them the source code,
and if they're skilled in programming and
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so forth, they'll get there.
Like they'll look at the source code,
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they'll be able to figure it out
see what changes they want to make.
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Machine learning is different. It's not
even clear what you really all the stuff
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you really need to give people in
order to make models transparent and improvable and
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so forth in the industry has not
figured that out yet. So we're really
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in a state of flux with AI, where a lot of people want to
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apply the open source model to it, but it's actually a little bit difficult
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to figure out how to do that. I've been spending a lot of time
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talking about that and so forth for
the last year, ever, since this
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stuff just blew up about you know, a year ago, and people are
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working on figuring out what it means. Personally, I think that unless we
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can figure out a way to make
machine learning models more transparent and accountable,
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they risk getting basically regulated off the
face of the earth, or being just
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in the hands of really big companies
that can, I don't know, like
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figure out how to make people accept
them notwithstanding their lack of transparency or to
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your point, the transparency for the
listeners to understand is that machine learning goes
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through processes where it starts thinking and
it does things right, and then it
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comes out with an outcome. And
the part that everybody seems to be worried
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about is how did it get to
the outcome exactly? And it's not a
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set of instructions. I think that's
something that like most people just are not
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focused on. Like with software,
there's a set of instructions that was written,
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you know, by a person or
at least checked by a person,
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and so if you want to see
how did it get from A to B,
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you can actually go through and follow
the logic. But that's not how
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machine learning works. Like the transformational
models, they they just kind of you
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put something in. It's kind of
a black box and it comes out the
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other end. And that's a great
concern to people because when you where you
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try to figure out, well,
did it make this decision properly or fairly?
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It's actually quite challenging to figure out. Yeah, I mean, it's
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incredibly challenging. And one of the
fears that I keep hearing about people keep
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saying is, you know, is
this going to make us dumber? It's
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basically doing the thinking for us,
right, Like, yeah, you would
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normally delegate to somebody You're like,
hey, can you go figure this out.
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Can you go do this research and
come back to me with this.
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That's the kind of work that AI
seems to be for right now quite often,
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right and I mean we use it
right but then you know, you
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can't help it to think, well, is this going to make us dumber
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because nobody's actually learning because the AI
is doing the work for us. What
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are your thoughts on that? Yeah, I don't. I'm on the fence
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about that. I mean, there
are a lot of things that AI can
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take some burden off of us for
doing repetitive tasks that humans have had to
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do. One of the examples I
use, because I'm a lawyer, is
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that like maybe a little bit before
I started practicing, so be in you
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know, the late nineteen eighties,
when people negotiated contracts, they would you
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would send someone a document showing the
differences between one version of the contract and
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the others, called a red line
in legal practice. And people used to
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do those by hand. And today
no one would ever do one of those
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by hand. Those like programs that
do them. The programs are more reliable
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and probably more worthy than the humans
that we're doing now. And so there
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are a lot of tests like that
that I think it's actually great if we
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can free people up to do tasks
that require more sophisticated mental energy. But
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on the other hand, the machine
learning models are very good at spitting out
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what I would call kind of plain
vanilla pros. And one thing I do
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worry about is that people won't learn
to write anymore. And that's been kind
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of a problem, honestly for those
of us who have been in sort of
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management positions for knowledge workers, right, is that it's hard to find people
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who write well, you know,
And I think it gets harder and harder
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every year. And I'm actually kind
of worried that people they're just not going
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to learn anymore because they don't have
to. What the way you learn to
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write is just by producing a whole
lot of writing. You have to do
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it over and over again. So
yeah, it'll have good and bad effects,
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I'm sure. So let's talk a
little bit about this book. So
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you have there's a lot of different
themes in the book software entrepreneurship, investing,
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demystifying the process of building a scalable
open source business, and I want
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to talk a little bit about that. So I want to shift topics a
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little bit into that. One of
the things that I learned is when I
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was looking at the content surrounding open
source, and that I hear all the
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time is why should somebody start an
open source business? Why is this a
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good investment? And what does it
come down to? And maybe you could
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shed some light on some of that. Yeah, I'm a VC as well
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as a lawyer, and I specifically
invest in commercial open source software development,
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and I think it's a great investment
for many reasons, just you know,
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touch on a couple. The first
is that when you're an investor and you're
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looking at, say a company you
want to invest in, one of the
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things you do is you you go
through a diligence process to try to figure
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out, well, how good is
their technology and how you know, how
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good is the market in, are
are people going to be interested in the
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product? And so forth? With
open source, you can tell a lot
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of about that stuff before you even
engage with the company, because usually what's
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happened is they've thrown an open source
project out there and you can see,
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oh, it has X number of
stars, it has x number of contributors.
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I can actually look at the software
and see if any good you know,
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so it actually de risks the investment
a lot if you're interested in investing
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a business in a business that surrounds
the open source software on the entrepreneurs side
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open and the investors side. Like, open source businesses are incredibly capital efficient.
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They you know, long before the
pandemic, open source collaborators were collaborating
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from across the globe. They were
virtual. They don't need offices. They
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run lean and mean, and that's
the kind of business that's good for both
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investors and entrepreneurs. So I can
give a business half a million a million
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dollars and they can do amazing stuff
with it, and I can figure out
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in advance, like whether people seem
interested in the product, and that's awesome,
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I think for both sides. Anytime
you have something that allows somebody to
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start a business with less capital,
it means more innovation, and the capital
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that the investors provide gets used,
you know, with more leverage and more
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efficiency. So as essentially, like
what you're saying is like you can earn
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and learn about product market fit.
Yeah, you can exactly learn out the
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personas that you're dealing with and the
icps that are interested and all the little
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things that present risk if they're unknown, because those things have already been launched
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and the numbers are already in.
Is that yeah, exactly exactly. And
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you know, I hear people say
all the time, well, GitHub stars
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are kind of you know, I
shouldn't pay that much attention to it,
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and I understand that. But if
you look at how many contributors there are
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to a project, how many downloads
there are of the project, how engaged
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the community is, you have amazing
intelligence about product market fit, and that
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kind of thing is hard to buy
with a regular business. So it's really
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exciting. Yeah, I mean,
and anything, you know, the naysayers
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that are out there, they always
you know, there's always people out there
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that saying, well, you don't
get this and you don't get that,
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and you're like, well, it's
better than nothing. How about we start
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with that? Yeah, exactly.
Another great thing is that open source businesses
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get a lot of really good feedback
from their communities. So you know,
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how you use products, and they're
always asking you surveys and all this horrible
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stuff like how how would you recommend
this and so forth. Well, the
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open source projects get tons of feedback
from their community, and that stuff is
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incredibly valuable and you get it for
free, Like you don't even have to
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send marketers after people to talk about
it. So there's just a lot of
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aspects of running a business around open
source. If you if you do it
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properly that you know, it's it's
a it's a huge benefit to the people
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running the company and therefore their investors. Yeah, I mean you get the
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engagement right out of the bat.
Yeah, because they're participating, right,
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so exactly in the game, and
they're willing to engage, and you're right
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it the you know how many times
a day you get the NPS email,
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you know, the net rotor score
email it's like would you recommend? And
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you're like, oh my god,
yeah, and you get like thirty of
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them a day and these different surveys
all the time, and I think people
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abuse the hell out of that process. But yeah, and in open source,
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people will open an issue and they'll
do it for you, right,
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yeah, somebody else will solve the
problem. Yeah, It's like, you're
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right, it's a pretty it's a
pretty innocuous process. Yeah. The corollary
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of that though, is like for
open source businesses, they have to pay
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attention to their community, like you
can't just start a business around an open
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source project and then expect your community
to just give give, give to you.
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You have to give back to them. You've got to respect them,
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you've got to pay attention to them, you've got to take their concern seriously.
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But if you do that, it's
a wonderful partnership between the community and
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the company, and you can,
you know, use that to leverage what
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you can do with the commercial product
end of it, and you can use
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the commercial product revenue to leverage what
you do with open source. That's kind
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of the dance that you have to
do with a business like that. So
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let's talk. We have a lot
of hackers that listen to the show,
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right from the different hacker angel Hack
and all the other communities, and so
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what should developers consider if they're thinking
about going into the open source business?
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What should some of the things that
they should be thinking about. First thing
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they should think about is whether it
really should be a business. So this
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is one of the things I talk
about in my book. And the answer
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to that, there's no wrong answer
to that. It's just that based on
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the answer to that, you go
in different directions. So, for instance,
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if I'm a developer and I'm I'm
scratching an it. You know,
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I'm thinking this thing really needs to
be written, and I'm going to sit
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down and write it and spend my
hours of nights a weekends doing it.
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And I do that. I might
do that because I just wanted to do
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it. Maybe I want I did
it because I wanted to kind of put
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it on my resume. I might
it might be a hobby for me,
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and then it might start becoming a
business. But even if it does,
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it might be in different levels,
Like do I want to start a business
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where maybe I'm going to become a
professional services provider, do some maintenance and
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support for people and so forth,
create a living for maybe me, maybe
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a couple of my friends. And
that's fine, right, But then there
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are some people who really want to
take the open source project and turn it
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into a big business. So depending
on what you want out of it,
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what your time horizon is for an
exit, how big you want the project
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to be. Do you want to
actually manage of business or you just want
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to work for yourself. Based on
that, you get to the answers about
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how are you going to run the
business, how are you going to fund
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it? What are your goals?
So that's one important thing. The other
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is that you have to have a
business model for what you're going to do,
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and it doesn't need to be perfect. You just have to have an
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idea. If you go and you
ask investors for money, you say,
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I have this project with ten thousand
stars and it's truly great and people love
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it. The next thing they're going
to ask is, Okay, what are
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you selling? Who are you selling
to? How are you going to make
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money? And you need to have
answers to that, even if even if
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they're just your best guesses, but
you have to have answers to that.
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And I think that some open source
developers can tend to engage in a little
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bit of magical thinking about well,
stars means money, it's not doesn't quite
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work that way, but you can
get from A to B if you plan
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it out and you think about it. M yeah, I think that's where
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I was coming from with the comments
of you know, black magic and some
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over the mirrors. A lot of
the engineers, you know, when I
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look at those deals, you know, they come in and the engineers like,
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well, I've got five stars and
I've got all these people contributing and
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you're like, yeah, they're not
buying anything. Yeah, right. That
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can be a fine outcome, but
it's not the outcome that will get you
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funding, right right, So you
just have to think about, like,
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what is your objective? What do
you really want to get out of the
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project. So I wanted to switch
things up a little bit here. We
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talked about founder engineers. They're creating
all this stuff on one side of the
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equation. Now I want to shift
to the investor side, but we have
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to stop here. Also coming up
next time on the Startup Science Podcast,
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Heather, we'll show a good vertical
to dive into when it comes to open
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source. If you had to pick
one business model that tends to work really
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well with open it's what we call
an opencore model, backed up by SaaS.
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So what you do is you develop
some core functionality and you release that
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as open source, and then around
that you build software as a service.
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The Startup Science Podcast with Gregory Shepherd
is brought to you by Startupscience dot io.
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00:20:23.400 --> 00:20:30.039
Founders can log on too startupscience dot
io and use code Forbes to get
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00:20:30.039 --> 00:20:36.440
free access to connect and find out
more about greg Go to Gregoryshepherd dot com.
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00:20:36.920 --> 00:20:41.039
The Startup Science Podcast is a production
of Forbes books.
