Peter Wang (3:56)

Thank you, thank you. So glad to be here. Thank you for having me.

Omar Khan (3:59)

My pleasure. Do you have a favorite quote, something that inspires or motivates you that you can share with us?

Peter Wang (4:05)

Yeah. Well, there's, I think one of my favorite quotes when it comes to entrepreneurship and business is a quote from Antoine de Saint Exupery, the author of the Little Prince. And he had this fabulous quote. He said, if you want to build a ship, don't drum up the men to gather wood, divide the work and give orders, but instead teach them to yearn for the vast and endless sea. And it's just one of those like beautiful quotes that especially when you're early stage, you seem to be doing an impossible task. It's a vast, endless sea and the thing that the founder can do is to create that inspiration. Now if all you ever do is teach them to you, you will not get a ship built. At some point you have to go gather wood and divvy up the orders. But. But I think it's important. It's important when we're building new things to put that dent in the universe, kind of that mentality. Founders need to give themselves some credit of of having the chutzpah, having the inspiration to do that. And that is a unique founder thing to own always.

Omar Khan (5:07)

Yeah, I love that. That's a great quote. So tell us about Anaconda. What is the pro, what should I say? What do the products do you know, who do you, you know, who do you help and what are the main problems that you're solving?

Peter Wang (5:24)

Yeah, so Anaconda is. It is a very popular distribution of software that people use to do data science, machine learning, Python, numerical computing, engineering and all that kind of stuff. So it is a collection of open source software. Primarily this is what people know us for. And you go to our website, you download the software and now you have all of the different pieces that you need and if you don't have all of them, you can use our software package installer update tool to go get other bits and pieces. Because the open source ecosystem around Python is very, very large and some of those pieces you want to get are very complex. Even though it's all open source software, it's extremely hard to build those things correctly, make them work right for whatever hardware you have, etc. So we've taken care of that problem for people and we've been doing that for a very long time. 12 years now we've done that. And really we were at the center of the movement to bring Python, numerical computing, Python, data science into the mainstream. So the reason why Python is the most popular tool for data and AI today is because of what was launched at Anaconda. So we both build that open source software installation and update mechanism, but we also do, we invest a lot into the open source ecosystem, incubating new things, maintaining old things that people don't want to maintain anymore, but that are critical to the ecosystem, all of that. But the really, I think the important piece for the people listening to this podcast is the monetization of that. And so that is then the enterprise products and services that we sell to our thousands and thousands of enterprise customers. Enterprise accounts, right. There's millions of users of those places, but thousands of accounts. So we do all of those things, but I think we're best known and probably most known for our software download that people use to run Python. Data science, AIML stuff.

Omar Khan (7:14)

Great. So open source product used by millions of people around the world and then a few thousand enterprise accounts, which are how, you know, basically is how you.

Peter Wang (7:29)

You monetize a few thousand paying enterprise accounts. There's hundreds of thousands of enterprises that use us, but only, only a few thousand that are paying us Right now? Yeah.

Omar Khan (7:40)

And give us a sense of the size of the business. Where are you in terms of revenue, size of team, where we're a very, very healthy.

Peter Wang (7:49)

Eight figures.

Omar Khan (7:51)

Eight figures in ARR. And how big is the team today?

Peter Wang (7:57)

North of 350. We've been hiring pretty rapidly.

Omar Khan (8:00)

And you've raised just around. We were talking about this earlier. It's about 80 million.

Peter Wang (8:05)

That's right.

Omar Khan (8:06)

Great. Okay, so the business was founded in 2012. It was you and your co founder, Travis. He. So I did this was a little factoid that I sort of discovered that he was the. Was he the creator of numpy?

Peter Wang (8:26)

Yes, Travis is actually the creator of Numpy and scipy. And for those on the call who are not familiar with those libraries, who.

Omar Khan (8:33)

Know what is numpy?

Peter Wang (8:35)

Yeah, I mean, I don't assume anything. It's a big world out there. Software. So numpy is the numerical Python library that basically creates, you know, allows you to do vector and array matrix math with Python. And it's a fundamental, very fundamental piece of numerical. I mean it is sort of the most fundamental piece of numerical computing. Sort of like what the Linux kernel is to the Linux ecosystem. Right. And then scipy is scientific Python and that is the various additional functions and like nerdy math calculations that you want to do. And so it's used very heavily in engineering, it's used very heavily in finance and places like that. And then all of the other tools in the Python ecosystem really build upon these things. So there are two really, I would say like one is maybe one is Linux kernel, the other is GCC or something. They're just so fundamental to what makes Python work for data. But yes, Travis is the founder, so he's the creator of those two tools. And I guess he's a co creator of SciPy, but he is the sole original creator of NumPy. These are all of course now maintained by teams much bigger. And that came later. But yeah, we created the company because we started doing a lot of consulting around this stuff. So before we started the company, we were consultants on top of numerical computing in Python and Python for engineering simulation, financial simulation analysis, all of that stuff.

Omar Khan (9:55)

So let's go back to 2011, you guys. I mean we say the company was founded in 2012, but you bootstrapped for the first three years. Mostly your revenue was coming from consulting and training services. So just walk us through that, like how you guys got started here and then at what point did this become a product business?

Peter Wang (10:28)

So the company was founded on a Thesis that the Python numerical Python and the Python data stack as we called it, that that ecosystem was ready to really be taken mainstream into business, into transforming and disrupting business data analysis and disrupting in a good way, right? Transformatively, innovatively disrupting that software ecosystem. That was our bet. And we knew that there were many deficiencies, many things that needed to be made better. Usability bits, scalability, data connectivity, all these kinds of things. But based on our consultancy we could see that this was starting to organically get adopted into big banks, investment banks, hedge funds, people with a lot of money to throw at it and software, but they were opting to use this, you know, still emerging somewhat open source Python stuff. So that was the bet. And so when we started the company, we knew that a lot of our work was going to be community building, a lot of our work was going to be advocacy and marketing. So very unique among startups. We founded the startup Anaconda, actually used to be called Continuum Analytics. So we started Continuum analytics as a company to build products around this open source ecosystem and sustainably fund additional continued innovation there. We also started a nonprofit. So not many founding teams start a 501C3 and a Delaware C corp at the same time as multiple components of the same underlying business thesis. But we did that. And so as part of the community efforts, we then created a foundation to house a lot of these projects, raise grant money for them. We created a meet a set of meetups and conferences. So I personally created the concept of PI Data and the PI Data meetups and conferences, they're now global. So all of those things were efforts to drive adoption of Python. So those first three years we had a couple of million dollars of seed funding from friends and family that we raised. We won some DARPA contracts which was amazing. DARPA was great partner to work with. All that money that came in either went directly into seeding and building new kinds of open source projects or creating more adoption and creating more marketing materials to advocate for the use of Python. All these kinds of things, some that went into building things that would eventually become our core product, right? And that is the Anaconda package installation system, the Conda package manager, some of our various websites and hosting sites that host then the packages that people build, that we build. All those things were then, you know, those were built on the, on the margins coming from the consulting service that we did in those days.

Omar Khan (13:05)

Can you explain the rationale? I mean in the early stages when you're, you're trying to get your business off the Ground. There are so many things to do. You just, you know, a founder just doesn't have time to do everything. And then for you to also say, okay, we're going to have this nonprofit side, you know, we're going to kind of focus on that piece as well. Can you just explain the rationale, like why you guys felt that was so important for you to take on at that point, you know, over.

Peter Wang (13:38)

That's actually a question no one's ever asked me. That's a really, really good question. I've told the astounding story many times. Many podcasts I've told about, like, Sweden. I've told it on other podcasts and whatnot, but no one's ever asked me that question, like, why did you waste your time with this distracting activity around this other stuff? But I think in a beautiful way it connects back to that original quote, right? Yearn for the vast and endless sea. There was a mission to what we were doing. We really believed in the quality and the amazing power, the amazing power of the open source community to create high quality, innovative things. And so for us, building that community and supporting and pushing that was not an optional thing. That was what we were trying to do. There's a related quote that I that almost selected, but I'll give it to you now, and it's from Peter Norvig. And he said the best businesses are built by those who wanted to achieve a goal and had to start a business to do so. And in our case, it was exactly that. We wanted to achieve a goal, which is to make Python the language and the technology for data analysis and numerical computing in the world. And to do that we had to create community support things. And to really make that sustainable, we had done enough consulting to know that just trying to piggyback this on the basis on the back of consulting margins was not going to be enough. And if we really want to create a sustainable ecosystem, like, we need to actually show we could build products around this. So we started a company around that as well. So for us, the company is, is, is, is sort of integral to the community motion that we were doing. But, but from a very tactical and pragmatic business standpoint, it proved to be a great business strategy because the bigger you make the pie and the more people you get to grow that pie, the bigger your share naturally becomes. It's an expanding universe. Right. So it was really important that we did that.

Omar Khan (15:26)

Yeah, I think it's turned out to be a very smart strategy. I was just curious whether it was intentional at the time or an accidental outcome of you doing it for a different reason.

Peter Wang (15:40)

Oh, and I didn't take it in a defensive way at all. I mean, I think it's a great question, right? But, but it's really. Yeah, it was, it, it, it was something that we, we were, we were really motivated. You know, we did have a vision behind this and I think this has helped in terms of general credibility of what we do. We certainly made a lot of mistakes along the way and whatnot. But all along, so many people in that community have, you know, they've gotten a start, they've many others have started, have gotten funded, startups, built companies, built their careers out of this ecosystem and they would not have had that opportunity. We don't have such a vibrant ecosystem if we hadn't had, if we were always just doing it from just a bean counting perspective, like how much are we getting out of it and all that stuff. So in the end it really did pay it forward for us. Right. As a business.

Omar Khan (16:26)

One of the things that's interesting to me is that personally, if I think about data science or data scientists today, it's hard to imagine anyone talking about those terms without the word Python coming up at some point. And so it was really surprising to me when earlier we were chatting, you said in those early days we were struggling to convince people to use Python. Just tell us what was going on, what were people using at the time and what kind of objections were you getting?

Peter Wang (17:04)

Yeah, so again, 2011 timeframe, 2010, that was when Big Data was the thing. People really were starting to turn the corner on cloud. So cloud is probably much more popularly adopted. And NoSQL, big data, Hadoop, right. These are the big things people are talking about. Data science was even still sort of a term that people were looking at with suspicion. 2012, 2013, 14, you know, lots of jokes about data scientists. Just a statistician who lives in San Francisco, these kinds of things, those are new terms at that time. And so everyone, all the focus was on Hadoop and Big Data. So when you're then talking about this Python stuff on the side, it's like, well, that's not Java and Big Data, Big IR and Big Systems, Hadoop was all written in Java. Spark was Scala, which at least ran on the jvm. And many people thought, well, Scala and Spark, that's going to be the future of all this stuff. We can rebuild everything that the R people and the Python people are doing. You could do all of that inside Spark and inside the jvm. We certainly didn't feel that that was the case, but you know, that was where the argument, well, ultimately resolved itself in the market. Right, but, but there were certainly perspectives there. And from a statistics standpoint, you know, there are these, there are a lot of big statistical tools and big analytical software that were around and people did see that open source tools like R would be a competitor to that. So I think R was seen as the rightful heir to the throne in that space. And then in comes like a bunch of these, like applied physics, engineering, whatever, kinds of like nerds from the Python land. And you know, Python at that time was defending itself on all fronts right there. You know, there was competitive competition, Ruby for web frameworks, and then on the data side, numerical side, we were just a weird little sideshow. So the first, first or second Strata conference I went to, I think Strata was the big data conference that Riley put on. I was getting, I would say not roundly mocked, but people were definitely like, wait, why, why are you talking about Python? I mean, this is a big data conference, right? And, and so definitely there was a lot of that. But you know, we, we had a bet and I think our bet played out pretty well. And my fundamental thesis was that, and what we've seen, our consulting was that end users, people who are not traditional programmers, they have analytical needs, they could be, they could be a mechanical engineer or electrical engineer, they could be a quant finance person, they could be someone doing marketing analytics, they have a lot of data they want to go and ask really interesting questions of, beyond what they can do in Excel, certainly beyond what you can ask in SQL. And they're not programmers, so they don't want to learn 15 different languages, hodgepodge of stuff. And Python was something that would fit in their heads, let them do the data stuff they wanted to do, maybe do a few other things, and it was good enough. And so our bet was that that was going to drive a lot of adoption and that we could then plumb other kinds of connectivity into that ecosystem, that Python ecosystem, if you could just be the first mile, or I guess in the telecom, since they call it the last mile, right when it actually goes to the house. So when you actually have a person with a brain with interesting questions of data, fingers on keyboard, what are they thinking? What are they typing? And our bet was from what's seen in our consultancy for what we had seen in our open source ecosystem, who are the people that were showing up in it? There are people that didn't have a traditional programming background. They're not software devs, they don't have 12 years of C experience, they have all these other domain expertise. And we saw those people reaching for Python as a preferred tool. So we bet on the end user, we bet on this tool that would fit in their heads and then we try to bring other things to make that ecosystem more powerful, but we plummet through to the end user. And now this has really been validated. I don't know if you guys saw the announcement just recently, the Excel integration of Python. So now anyone who's using Excel can reach for these Python tools and it's right there in the formula bar. So our thesis has proven out brilliantly beyond what we possibly could have imagined when we started 12 years ago.

Omar Khan (21:13)

So you were facing a lot of skepticism about using Python rather than sort of pushing a rock uphill and trying to convince all these people. What I'm hearing is you were like, well, we did that to some degree. But what was more impactful was focusing on the end user, people who, you know, weren't sort of traditional software developers but needed to be able to use a tool like this and make sure that you were, you were speaking to them, serving their needs and so on. How you talked about community building. But how are you reaching these people? How, or was it all inbound? Like how did this, this sort of community effort really, really take off? I mean today, like how many, how many developers would you say, or let's not say developers, how many end users you know are using Anaconda today?

Peter Wang (22:17)

It's in the tens of millions. You know, it's, it's, it's weird because we don't have a traditional multi tenant SaaS. You know, you don't go to website to run Python. You download Python, you run it locally. We don't put creepy trackers and everything in it like the software, you download it and if you choose not to give us your email address, then, you know, you just have your stuff. Now when you go and you update packages, of course that reaches our, our, our, you know, package hosting repositories and we can sort of, we, we run a lot of analytics and we do a lot of this kind of stuff to sort of see even when people don't log in or don't create an account with us, you know, what, what is the actual number of uniques and all that kind of stuff. And based on the best analytics that we can tell, it's north of 40 million users, which is shocking. But then if you think about the fact that we have you know, millions and millions of unique downloads of our bulk installer every single week. I mean it adds up a lot. And you think about how many people in schools, universities that are learning Python, the first thing they do is they download Anaconda and they start playing with this stuff. So, and it's international, you know, it's China, it's India, it's South Korea, it's Australia, it's everyone in India. Everyone is doing this stuff because everyone needs data and they want to do machine learning on it. It's millions and millions of people. But to your question, how do we get it going? It was all organic. And that's why the community building was so important, that by creating these PI data conferences and having even that term, GitHub was part of it, Twitter was part of it. There was definitely a socialization element in the virtual spaces. But having these real conferences and meetups create a venue for these practitioners who wouldn't go to like, they don't go to traditional programming conference because that's where people talk about language things and all these like very, very sort of programmer dev centric stuff. And so when you have like a PI data conference where people go there and they learn tips and tricks and how to apply these different libraries to the data problems they have, well, they feel like they found a tribe, right? And then people start writing books and they're, you know, just all these things, YouTube videos, tutorials, we do webinars. We invest a lot in that community building and really validating that. There is a Python data science thing that that actually is a thing and not just an off label use. I think that's a lot of sweat equity and sort of, I'll call it emotional labor, but it's a lot of invisible investment. I mean Anaconda Inc. Underwrote a lot of those conferences. You know, you got to sign venues, contracts, food, vendor stuff, all these things. And the nonprofit NumFocus did not have much money at the time, so we underwrote a lot of those conferences and once those became self funding, we handed all of that off now in focus. So it became a revenue generator for them and they could take those revenues and turn around to being grants to the open source projects. So that was kind of how we got that separate engine going in the nonprofit open source space.

Omar Khan (25:06)

It's just mind blowing, that number of like 40 million plus end users. And in many ways I think a lot of people would be like just freaked out by this idea of wait a minute, you've got 40 million plus free users of your product, you don't ask for an email.

Peter Wang (25:27)

We ask for it, but we don't require it. We do ask for it but you can click the X and you don't have to give it to us. Right.

Omar Khan (25:32)

You don't require it and you don't have a multi tenant solution. People have to either. The way they can get Anaconda, as you said, is either download locally on their computer or for enterprise customers, which we'll talk about in a while. They can have some kind of on prem solution. So in many ways you're doing so many things which are so contrarian I guess in terms of how you are supposed to run a subscription business like this. Let's talk about like how that on prem product, you know, it sort of started to come, you know, come about. So we talked about like the first three years of consulting, training, services and then year four, 2015, 2016 is when you started to provide some clients as on premises solution. How did that happen?

Peter Wang (26:34)

Well, I do, I do want to clarify. We do have a cloud based, multitenant sort of traditional SaaS application for hosted notebooks in the cloud. You go to Anaconda cloud. I encourage you to. If they don't want to install something, they can use it there. But it's not our primary thing. Right. We have, we have, you know, a healthy number of users on there. It's growing, but it's not like our primary thing. So the on prem, the on prem bit, and your question specifically was which aspect of it was like how did that business emerge?

Omar Khan (27:04)

Yeah. So you're kind of going down this path of consulting training, you're building this community, you're starting to get some traction. You've sort of figured out who the right ICP is, at least in terms of the community side and the open source side. Did you guys just wake up one day and say, hey, let's add an on premise, you know, offering?

Peter Wang (27:27)

No, no, actually this was very lean startups because this is me and Travis's first, first startup. Right. So we're like, well we read a bunch of books for a couple of nerds. So we read some books. The, we were both in Austin, which did not have as much of. There was like no like venture ecosystem. It's not like we were in the Bay Area. Every, every, every night there's a meetup, drink up something where you're just swimming in other founders and like you just learned so much from them through osmosis. We had very little of that. So we were reading books. We would go to some of these things, but it wasn't, I would say it was not rigorous and it was not in the groundwater right in Austin. So we really took like four steps to the epiphany. Lean Startup, a lot of these kinds of things, very much to heart. So we got inbound. So our software download, you download this thing and like so many pieces of open source software, there's always updates. You want to get the newest version with some feature or maybe there's a bug fix. So people are getting package updates all the time. And it's a very large ecosystem, hundreds of thousands of different libraries and packages. Okay, great. So we get emails from companies. The first is actually from a law enforcement agency. And they said, hey, we love what you guys are doing with Python and this is great, but we cannot have our folks here internally just installing random packages off the Internet. Do you have something you could sell us? That we would have our own behind the firewall package repository that we could then manage, right. That we would be able to excel certain packages that we don't want people to use, that we could force certain kinds of updates, if there's security updates, etc. Etc. And we said, you know, we don't, but we could put one together. And we put that together. So we put that together, sold it to them, and there it is. That was your first product sale. And Lean Startup just says, when customers come to you with money in hand saying, we want this product, you should at least listen to that. Right? So we listened to that, we built that, and then we started talking to other people. You know, again, inbounds, but we also did some webinars, which really, we did invest in a little bit of outbound marketing and positioning of this stuff. And it turns out that a lot of people have this need, especially when it comes to running. You know, when people do Python stuff for data, they're running it on data. And so there's sensitivity around it oftentimes, you know, there's, there's corporate environments where the data cannot leave a particular network, in some cases, data cannot leave a particular machine, right? So they really need to have all the stuff running. They really want to have an internal mirror and a way to manage their open source consumption internally. So that became then this. We just added more and more features. People wanted the ability to filter out GPL code. They didn't want their junior data scientists to absolutely put, to accidentally put some GPL web service into production. Right. And so all these kinds of things. And then we started doing things like CVE scoring and vulnerability alerts and stuff like that. We had custom package build services, we had ways for people internally to create additional channels and make it so that only the dev cluster only sees some packages, the production cluster sees other packages. We just added more and more bells and whistles and things like that. And that product is still a mainstay of what we sell today.

Omar Khan (30:27)

What percentage of your revenue roughly comes from that product?

Peter Wang (30:35)

So that product is in the, I'd say probably about 30% of revenue, maybe more than that. So it's a little bit difficult because there's the on prem. But then we also can run that in the cloud for people. So people do want their own customized package server, but they want it, they're all cloud. You know, they might be cloud native and have a Kubernetes thing spun up on Amazon or, you know, wherever. And so we do that. And then we also have a secure commercial package repo that we sell subscriptions to and that's, that's another good chunk of our revenue.

Omar Khan (31:08)

Let's talk a little bit about. You sort of mentioned the, the incumbents that you know, were around at the time. And so you're making a bet that Python and Anaconda is the right solution for these end users. But you also told me earlier that one of the struggles that you had was that you just felt that we weren't as good or as polished as a lot of those other alternatives. Can you give an example about that? I can say not as good as polish, but you know, make it real for us. Give us an example like, so people can understand, you know, some of these.

Peter Wang (31:55)

Other analytical tools, they have a lot of specialized analysis functions that have been developed over decades. I mean some these, these tools, these incumbents are decades old, right? And so if you are doing a particular kind of financial analysis, you'll literally have every possible thing in there. Because they've been accumulating customer use cases for decades and they put functions in there and they would just, you know, that's what they've got. In the open source world, you get whatever, you get whatever somebody decided they needed and they took the time to go write it and put into the thing. So when you're first starting in an open source ecosystem, that crowdsourcing contribution dynamic is not so large. Your baseline of how many people you have is not that big. Now as it gets bigger and bigger, you then acutely have more and more people, more pull requests and then maintainers merge those in and it becomes more and more capable. And that's Great. But when you're early on, you don't have as much coverage of basic functionality maybe. Right. But then there's other kinds of things where. Yeah, where like there may be some visualization, some special visualization they want some particular format. I mean, we saw this in geophysics, like I'll just give you an example for like oil and gas geosciences, geophysics. There are a lot of very expensive tools for doing analysis that have just been there forever. And so when we're first doing, even consulting with some of this stuff, people like using the Python as an alternative maybe to matlab, they like using certain kinds of things, were nicer to write in Python, but then certain kinds of visualizations that were very custom. You imagine looking down a well, like if they have a well and they do some analysis of all the measurements down a well, that's not a standard plot type that most people run into. Right. It's kind of a weird kind of chart. But they've got a very custom way of looking at this kind of data in the geosciences. And all of the big software suites for that industry have very custom, very nice and polished UIs for this they've been developing over decades. And in the open source world, it's like whenever I get hacked up together in like space a couple of months, that's what you get. Right. So there's that kind of a thing. So there's just like a lot of that kind of stuff that makes any sense.

Omar Khan (34:00)

Yeah, that totally makes sense. You know, it kind of. I was thinking about that, that with many founders, they have this dilemma that they have to deal with that. On the one hand, they know all the flaws in their product, they know all the stuff that kind of works but is kind of just glued together and could fall apart. They know about things that they want to do that could be ten times better, but they just haven't been able to invest the time and the resources to do that. And yet at the same time they have to go out there and tell the world and tell customers how great their product is. And it sounds like you went through sort of a similar process. Was that, was that like a difficult thing to do at the time?

Peter Wang (35:00)

It wasn't a difficult thing because I was such a believer in the community and the amazing people in it. And I really believed that the idea of empowering these subject matter experts or domain experts, empowering them was fundamentally the right thing to do. So this is where if you're the founder, you're creating a religion, you have to be the center of inner subjective belief that there's a meaningful mission, that there's a thing to do. So for me, it wasn't difficult to manifest that future visionary, positive thing. But also for me, like, if you talk to other people who work with me and whatnot, that is sort of my natural mode is I'm very forward looking, optimistic. So that was also a little bit easier for me. And I recognize that for many people, depending on their psychological and sort of neurological thing, that might be a really difficult thing to do. Right. And I know that a lot of technical people do struggle with that kind of thing. I'm naturally very extroverted. So it's easy. So I don't want to make it sound like it's always going to. It should be easy because you're absolutely right. That is an enormously hard thing to do. It is enormously hard because you have to hold that vision. You have to manifest the vision and the fact that you could do it. You have to fundamentally be existentially positive and optimistic if you're going to push any kind of organization to the promised land. But I think the way you get through that is developing an internal capacity to sort of manifest two things at the same time. So you have to manifest for yourself that honesty and like, oh, here's what we need to do, but I really believe we can do it. But the other thing is that it's sort of a customer subjectivity. So you're going to be your own. You better be your own harshest critic or you're not going to make it right. So you're going to be your own harshest criticism. Your customer isn't. Your customer is looking at you for the same reason why it's hard to sell stuff. You also actually probably have a bit more leeway than you think. Right. Which is the customer. If you use the jobs to be done framework for product development, your customer is hiring you to do or hire your product to do something. And all you have to convince them is that it does that one thing better than whatever they've got currently going on, that it's worth them making. You know, taking the leap and making the switch. You don't have to solve, like when you're building the product, you feel like you have to solve the world's problems, make the best possible thing. But every single customer only has some subset of those things that are value propositions for them. So if you just handle it for them, well, that's great. If some prospect customer comes in and says this is not good enough for me. Great. Maybe in the future I'll go back to them and it will be good. So you move on to the next prospect. And so manifesting that other center of perspective, it's very, it can be very liberating in the sense that you don't have to be such a harsh critic for yourself. You can just say, well, what does that customer really need? Out of all the things we've got, can we cobble something together? Can we put this in a way that it solves that problem for them? And if it does, they'll be happy to pay you for it and that's great. And then you come back later when you've got all this other stuff that you think is going to be even better. It's also tied to like, don't sell past the close. It's tied to all these kinds of things where as a founder, as a visionary, as all these things, you could see all things that could be. But you don't have to sell all of those things to every customer. Right. And that in that, in that space, you can find a lot of forgiveness for yourself and find a lot of peace, hopefully. Does that make sense? There's like a lot of hand waving.

Omar Khan (38:31)

But hopefully that totally, yeah, the hand waving is fine. Totally makes sense. And I think the way you put it, it's an obvious thing, but I think the way you describe it, maybe I haven't heard that before, but often I think founders, when they're building a product, they look at the competitors or the incumbents, they see something that has 100 features and they're thinking, oh my God, I just got this one or two things here. There's just a huge feature gap between what I can offer and what these products have. Number one, often it's taken decades to get to that point, but what you pointed out, hey, how many customers are using all those features in those products? And if you can figure out which subset of customers are using a small percentage of those features and just focus on them and help them do a particular task better, like you don't have to go and build like, you know, all those features and spend a decade before you launch the product.

Peter Wang (39:39)

Right.

Omar Khan (39:40)

Which kind of is often how you feel like you know what you want to do. Let's talk a little bit about the, you know, we talked about the open source side, the enterprise business. So now that's getting traction. You know, you've got more and more these on prem solutions in place. The open source piece is growing. And then as you Told me as you start to hire people it becomes like really confusing for a lot of people coming in. Like, I guess the question I asked you at the beginning, like why are we doing these things? Why are we, you know, like. And you had like two opposite kind of perspectives depending on where somebody was in the organization, right?

Peter Wang (40:28)

Yeah, there's. For us it was particularly hard for two reasons, not just one. Right. On the one hand, and the first one is that we did, we have a very broad. It's a weird product if you think about what we have, because we, although we write some of the open source that's in what we ship, we don't write most of the open source. We're really a distributor, we compile and build and that can be very, very nuanced and tricky. So there's value that we add there when we do that. But nonetheless the upstream source code comes from other people. So it doesn't look like a traditional software product. I mean if you think about, if you're a non technical marketer or salesperson, someone who's not technical, but you go into a tech company and you ask them, well, what's your product? Okay, it's this thing, it's a box of other people's bits. You're like, well how am I going to what. It's a box of other people's bits. And so that is kind of a weird thing to try to sell in any case. But then additionally to that we have this weird thing where because we pushed and sort of broke new ground and created this Python data science, you know, that job category didn't exist when we started the company because we made the tools available, that job category became a thing and people started using our tools. And so there's grassroots adoption inside businesses, but those people are oftentimes new. They don't know how to procure, they know how to buy, they don't know how to navigate budgets inside enterprises. And meanwhile enterprises, they have a love hate relationship with new technologies, right? They know they need to be innovative, they know they need to stay on top of this stuff, but they also just feel like they're constant on the technology treadmill. So there's always internal corporate inertia and pushback headwinds against new things coming in. So you have to convince those buyers, the economic buyers, the IT people, the compliance safety, CIO people, procurement, you have to convince them that this new tech is good. And by the way, you should probably buy this thing to support these people using the free thing. So we didn't have A simple like, oh, here's, you know, a screwdriver. If you want more screwdriver heads, buy the plus plus subscription. Or here's a database. If you want more rows, buy the subscription. Our problem is a very weird thing. Here's all this free amazing stuff. It works great. None of it is limited in any way. Use the crap out of it and we start using it a lot inside your business. The managers are going to want to have some way to govern it. They want to have some stuff running on prem, they want better collaboration tools and we sell through that way. So then internally, what that means when we hire people in, we'll hire someone who's doing marketing or we hire someone who's doing sales. They have to in their heads, wrap their heads around, number one, the product is a box of other people's parts. Number two, you're not upselling the straightforward upsells of these things. You're not saying, oh, you could do a million rows, we want to do 10 million rows, you got to pay us. You're not upselling in that traditional way. Your upsell is actually not even going to the people who are using it. Your upsell is to this other route, which is these other stakeholders. So it was very complicated. And if you get someone who understands the practitioner aspect, they sometimes don't understand how to do top down marketing to the B2B economic buyer, but vice versa, you buy something, you get someone who's experienced, who has a Rolodex salesperson, they know how to approach the B2B buyer but they don't actually for them, any investment in the open source, the community, the practitioner tools is wasted because you're not selling that stuff like that stuff, you don't make any money. So why are you putting your developer resources onto making the enterprise product better? Right. And so it's a really, it was just so many dimensions of hard. If I'd known that that was how hard it was going to be, I would have tried to engineer other kinds of products. Right. That would not have this confusing and conflation that happened with this stuff.

Omar Khan (44:18)

Yeah, it's fascinating because I mean it's like as you were telling me earlier, you've. You were in a situation where some people are saying, we've got this massive open source community, why are we getting distracted with this enterprise thing? And then people on the enterprise side saying we're generating revenue from this, why do we need this open source thing? But really that was how the flywheel was spinning because you had this massive distribution and then people are starting to use this in their organization. They don't need to pay for anything, they don't necessarily need permission as long as they can install Python and Anaconda and get started. And in many cases you don't have the email address so you can't even, even if you wanted to do some kind of upsell or marketing, you can't do that. So how did that actually happen? Like, so you've got let's say like a million people out there who have downloaded Anaconda who are using the product. Some of them are in enterprise, you know, organizations. Is it all inbound? Like eventually when some manager says there has to be a better way to do this and them coming to you and saying, you know, give it, give us like the first customer saying can you give us an on prem solution and we'll pay for it. Or were your team finding ways to do outbound as well in these.

Peter Wang (45:54)

It's a little of both. It's a little of both. I mean over time we don't have to advocate for the use of Python data science anymore. People do that, right? Python's the language of AI, so that's what it is. But we do have to still drive awareness from people about the vulnerabilities they might have in adopting just purely open source approach that it's always open source, but just using things from the free and community side of things. There are, there are, there's real value in them securing their open source supply chain. That's a term that is now, you know, it's actually in executive orders. There's NIST standards around this kind of thing and people can't ignore it anymore. So that is certainly one of those like market shifts that has been a tailwind for us and it's helped us find receptive years when we talk to people about this kind of security stuff. So there's definitely an outbound aspect of the marketing around this and we could do a better job of fixing up or cleaning up some of the UI bits for what we do install, that people know about other value added services even as a practitioner or single practitioner that they can upgrade and get from us. We're sort of, we, we've split our focus in many different ways over the years, but we definitely like, that's an area that we are trying to invest more in. It's just getting that direct practitioner consumption of our cloud services or using us to share more of the things they build with other people, share how easy it can be to do that. There's a. There's a lot of that kind of stuff that we, you know, everything takes money. And so doing that kind of outbound marketing takes money. All these things take money. And so we've had to sort of sort of peanut butter some of our investments in this way. I mean, the funny thing is, we go to a conference, you know, we get very bimodal sort of responses. Some people will say, oh, Anaconda, I use you. The years are great. And then it's like, well, you guys have enterprise products. I always thought you were a nonprofit. Like, people literally, you know, and that's. That's not great, given that I'm like, you know, looking at the PNLs all the time, being like, okay, how do we, like, balance the needs for more engineering resources versus this and all that? And so it's like, oh, we thought you were a nonprofit, but now that we know you got an enterprise product, this is great. We can actually use that. So that tells me I invest more in some of that outbound messaging. Right? So these are the kinds of things that we sort of, you know, even now, we're still trying to figure out how to dial in.

Omar Khan (48:10)

One last question before we go into the lightning round. How does the world of ChatGPT and people now suddenly using prompts to, you know, query all kinds of different data sources, like, how does. How does. How do you see that impacting what you're doing?

Peter Wang (48:29)

I think that it will. So, you know, there's definitely this sentiment that ChatGPT will just write all the code for us. So why do we need to learn Python or learn any of these other things? My thesis is that right now it is humans writing code, and then we get it to where computers can be writing code. But it's important to have a language that the computer is writing the code in that we can verify. We might not be writing a lot more of the Python code or code being written in the world in general will be done by computers and machines. But we're still going to want to have an audit trail in that case. It's not about the language that a person can write as much as the language that people can read. Maybe tweak and verify on their own, and that's still Python. It's an easy language to read, it's easy language to write. It fits in most people's brains. You know, it's actually. So I think even. Even if we buy into this idea that the LLMs will do all this coding for us in a short, short order, having a language that we can read and audit and run independently to get this to verify the results is super critical. I mean, the last thing you want is a machine that says, well, I generate the code, spun up all these docker containers, I ran all this code, and here's your result. The answer is 42. And you're like, what? Let me go try that. And you look at the docker container, and it's spewed out. It's put out 10,000 lines of C, and then it's got a weird make file of another thousand lines. You're like, oh, my God. There's no way I can verify the answer is 42. Right. So I think it's really important, even if we move to Mode, where they're programming, that we have auditable things that are human readable.

Omar Khan (50:07)

Yeah, that's a great answer. I love that. Okay, let's wrap up. Let's get into the lightning round. I've got seven quick fire questions for you.

Peter Wang (50:15)

All right.

Omar Khan (50:16)

What's one of the best pieces of business advice you've received?

Peter Wang (50:20)

One of the best pieces of business advice is that it's in general. It's all about meeting other people's needs. So whenever you're thinking through a problem, when you're thinking about what do I do? Or why am I struggling with this? Or what's wrong? I mean, whenever we're in a pain, fear mode, we are very, you know, we're trying to meet our own needs, but in general, our problems come from not meeting other people's needs. So it's really important that you develop the conscientiousness and the consciousness to be able to have a state of mind that outside of your own subjectivity, frames and holds the other person's subjectivity and understands what are their needs. How do I meet their needs? And one other one. So you asked for one, but I want to give you another one that's really important that I wish I had learned earlier, which is in general, in life, but it's true in business, boundaries are loving. It's not wrong to say, here's my boundary. This is all I can do for you. As a founder, you're sort of the person who does everything of last year's award. You catch all the balls as they drop. But in relationships in general, in life, but also in client relationships, it's okay to fire a bad customer. Sometimes you have to with employees. I, you know, I. I actually had sort of my. My. My emotional mental breakdown at one point was because I was trying to be the counselor and the camp coach for a hundred people. And that was just too much like my internal emotional thermal sync was completely deregulated and dysregulated. Um, and so, so I, I, that's. That's an important lesson that I hadn't learned, but I've learned now, and I want to make sure everyone else, as many people as possible, understand that the boundaries are loving.

Omar Khan (52:02)

Great. What book would you recommend to our audience and why?

Peter Wang (52:05)

I really think Four Steps to the Epiphany was a really fantastic book. It's hard to say why. It just had a lot of good bits and pieces. I really, I just, I loved it. I thought it was a fantastic book. But another one that people should definitely read is Innovator's Dilemma. That's a fantastic book. People should definitely read that book.

Omar Khan (52:22)

Great. What's one attribute or characteristic in your mind of a successful founder?

Peter Wang (52:27)

Well, we already touched on this, but I think it's the ability to simultaneously manifest existential optimism while also being completely paranoid about everything going wrong. And this idea that courage isn't a lack of fear. Right. You have to see all of it. You feel it, but you do it anyway. And so I think that is an attribute that's really important is being able to manifest both those things.

Omar Khan (52:55)

What's your favorite personal productivity tool or habit?

Peter Wang (52:59)

I just take a lot of notes. So for a long time I was an Evernote user. I switched over to using Joplin. I'm trying to put other things in Obsidian now, but it's basically just I'm scrapbooking ideas. I'm putting meeting minutes down. I have, like, readings and other things. I really wish I had a better tab browser organizer. So I don't have, like, a perfect system for this at all. But I definitely feel like having a single place where I take all these notes is. Is just. That's super critical. I couldn't. I couldn't function without that.

Omar Khan (53:27)

Cool. Yeah. I'm trying to use Obsidian more and more as well these days.

Peter Wang (53:31)

It's fantastic.

Omar Khan (53:32)

Yeah, yeah, yeah. What's the new or crazy business idea you'd have to pursue if you had the time?

Peter Wang (53:37)

Well, there's some things that are related and adjacent, but I think the crazy of it, and this is something I'm definitely going to do, is create a network, a sort of research network or research group. Maybe Institute is too big of a name that really is in support of looking for weird and interesting physical phenomena that we don't have explanations for. So you can call it sort of fringe physics, but not like just crazy random theories that people have no evidence for, but actually fringe sort of evidentiary physics, like what are the weird things that people observe but we, we actually don't understand or can't explain. And creating really a single place where people can catalog all these things, talk about them, and we can really start to understand some of this stuff. So that kind of a radical physics or heretical physics institute is sort of my crazy idea.

Omar Khan (54:37)

What's an interesting or fun fact about you that most people don't know?

Peter Wang (54:41)

I'm, I'm really into astronomy, so I've been an amateur astronomer since I was like 13 or 14 years old. And I have a really big tele. I have unfortunately too many telescopes, but one of them is a really big telescope. It's a 28 inch diameter, pretty large telescope. And I, and I do the thing that I do that most even astronomers don't do is I use military night vision equipment to look through the telescope. And that is an amazingly cool thing. And you can see nebulas, you see all these things that would otherwise be photographic objects. Like you have to photograph for hours and do long exposure. But with military night vision gear and with the right kinds of filters, you can see amazing things in real time. Even a smaller scope, but in a big scope, it's really spectacular. So that's my hobby.

Omar Khan (55:27)

And finally, what's one of your most important passions outside of your work?

Peter Wang (55:30)

I'm tied into a group of thinkers that are really looking into like, what is the future? What should the future operating system for civilization really look like? How do we do infinite game, not zero sum game kinds of dynamics, and really bring that to life. And this does tie into my open source work because I think open source software is one of those proof points that human collaboration at scale can yield incredibly powerful artifacts that is way more effective than what capitalism and even market economics has produced. And as we get to a world where we have more and more technology to suffice human needs, we need to start thinking about how it's not about limits of growth or degrowth or anything like this. It's more about how do we, as a collective action principle, organize humanity so that we don't keep running, burning up the earth and burning up our own internal psyches and whatnot, chasing this infinite growth, but actually having a mentality, a growth mentality that actually can rest in the biosphere that we have, but still produce a lot of really wonderful things. So that term, my friends, I've taken to calling game B instead of game A, which is the current system of the world, hypergrowth, hyper financialization, all these things. And so if people are interested in that, they can look for some of my podcasts I've done in interviews and.

Omar Khan (56:56)

Writings around Game B. Super interesting. So thank you so much, Peter, for joining me. It's been a great conversation. It was great to just unpack the Anaconda story and a very, very unique business and very different to 99% of the types of SaaS, businesses and guests that I have on the show. But it definitely, I think it opened my eyes and hopefully it'll get people listening to this, maybe thinking a little bit differently about things that they could or should be doing with their business. So I appreciate you making the time to do this. If people want to check out Anaconda, they can go to anaconda.com and if folks want to get in touch with you, what's the best way for them to do that?

Peter Wang (57:40)

I am on bluesky at Wang Social. I was actually angel investor. Founder. Well, not founder, but angel investor Blue Sky. I am also on Twitter Wang, but I've been sort of moving to Blue sky as my primary social thing. You can also find me at LinkedIn. My DMs are open there on LinkedIn. And one thing I would say is people can also use Anaconda directly through Excel by typing equals PY in their formula bar and they can now type Python code and that runs on Anaconda backed container and Azure. You don't have to install anything. You don't have to go to a web page. You already have Anaconda if you have Excel.

Omar Khan (58:16)

That's a great integration.

Peter Wang (58:18)

It's fantastic. Yeah. Well, thank you so much for having me. This is a really fun conversation. Thanks for asking me that really beautiful question at the start that no one's ever asked me before. Yeah. Thank you so much for having me. This has been a pleasure.

Omar Khan (58:29)

It was my pleasure. Thank you. And I wish you and the team the best of success.

Peter Wang (58:33)

Thank you so much.

Omar Khan (58:34)

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