This Week in Startups - Bootstrapping to IPO, product-led growth, & scaling SaaS with Atlassian's Scott Farquhar | E1800

Episode Date: September 1, 2023

This Week in Startups is brought to you by… Squarespace. Turn your idea into a new website! Go to Squarespace.com/TWIST for a free trial. When you’re ready to launch, use offer code TWIST to save 10% off your first purchase of a website or domain. Supergut is the only nutrition brand clinically-proven to improve digestion, balance blood sugar, sustain energy, and manage weight. Save 25% on their delicious shakes, bars, and prebiotic mix at Supergut.com with code TWIST. Miro. Working remotely doesn’t mean you need to feel disconnected from your team. Miro is an online whiteboard that brings teams together - anytime, anywhere. Go to https://miro.com/startups to sign up for a FREE account with unlimited team members. * Today’s show: Atlassian Co-CEO Scott Farquhar joins Jason to break down how he and his Co-Founder bootstrapped Atlassian to an IPO (1:34), his lessons from acquiring, growing and eventually selling HipChat (13:43), thoughts on product-led growth (29:27), generative AI tools for the enterprise (39:43), and more! * Time stamps: (0:00) Atlassian Co-CEO Scott Farquhar joins Jason! (1:34) Atlassian origins, bootstrapping to IPO (12:22) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://Squarespace.com/TWIST (13:43) Lessons from HipChat: Acquiring and growing the service, selling to Slack, what Scott would do differently (19:39) Atlassian's two playbooks: bootstrapping and aggressive growth (27:57) Supergut - Get 25% off with code TWIST at https://supergut.com (29:27) How product-led growth leads to a sharper product team, how Atlassian is thinking about LLMs and generative AI (38:20) Miro - Sign up for a free account at https://miro.com/startups (39:43) Remaining flexible re: AI tools, utilizing customer data, when to expect major knowledge work breakthroughs in AI (47:27) How Scott thinks about AI, remote work, and efficiency (1:01:39) Staying motivated in Atlassian's third decade * Check out Atlassian: https://www.atlassian.com FOLLOW Scott: https://twitter.com/scottfarkas * Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four Apply for Funding: https://www.launch.co/apply Buy ANGEL: https://www.angelthebook.com Great recent interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland, PrayingForExits, Jenny Lefcourt Check out Jason’s suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

Our biggest competitor back then was a company called, well, it was a product called Bugzilla, which was made by Apache, and it was an open source product out there. And the word Bugzilla came from Godzilla, which was the sort of Japanese film. But it turns out that actually Godzilla was the anglicized Western name. Gojira. He's actually the Japanese name, Godzilla. And so we dropped the go, and Jira became, you know, the product. and back then you could buy forwarded domain names, you know, just on your credit card without any problems.

Yeah, maybe I should have not started a software company and just bought up forwarded domain names. It would be more profitable. For sure. But we bought, yeah, bought gero.com and, you know, we started building the product. This week in startups is brought to you by Squarespace. Turn your idea into a new website. Go to Squarespace.com slash twist for a free trial. When you're ready to launch, use offer code twists.

save 10% off your first purchase of a website or domain. Supergut is the only nutrition brand clinically proven to improve digestion, balanced blood sugar, sustain energy, and manage weight. Save 25% on their delicious shakes, bars, and prebiotic mix at Supergut.com with code twist. And Miro helps take ideas from in your head to out there in the world with his ability to democratize collaboration and input. Sign up for free at Miro.com slash startups. Hey, everybody, welcome to this week in startups. Some companies are just so influential.

They define a category or a region. And Atlassian really became the anchor, the foundation of the Australian startup industry. A lot of the great startups are alumni of Atlassian. And if you're in the tech space, you know the firm. Today, we're joined by the co-CEO. and co-founder, Scott Farquhar, on the program. How are you, sir?

Great. What year is this for Atlassian? You're obviously in the second decade. I'm not sure what year it is, though. This is year 21 or 22, depending on where you count the starting point. So it's been a while now. We've been around the block a few times. Yeah, so technically entering your third decade.

Well done. And the crazy thing about your startup was it was largely bootstrap, right? Like just two small rounds of funding, if I remember correctly. And then they came in years 8, 9 or 10 or something. Yeah, we never took primary capital onto the balance sheet. So, like, we did some secondary rounds to allow myconitor to take some money off the table and allow employees to take some money off the table. But we've never taken primary money.

And then we IPOed in 2015. And so we originally took cash in 2010 from Excel because we wanted to head towards going public. And some interesting stories can go into that. And then in 2014, we did that sort of pre-public. round that sort of popularized a bit these days from a public market investor to help us get across to being public. Yeah, to a real price, right? I have here in my notes.

So this is incredible to build a company from inception to IPO without outside capital. I mean, that's really what I'd love to talk to you about. So how did the company start? What was the first product? And how do you build a company with essentially no venture capital, no seed funding? People right now in the United States, man, it's the entire. level was really high for about five years there, where people wouldn't even start working unless somebody gave them $3 million. They say, how do I do it? And so let's get into it.

It's perfect topic for, you know, 2023 when, let's face it, the seed funding is really dried up, continued funding, also dried up. It's pretty dark out there for startup. So how did you do it? What was the first product? We started in sort of 2001 each time. And back then, it was. was really like the dot-com crash. And we think, you know, 2008, 2009 and, you know, kind of the current downturn is bad for technology. And it is, but nothing really compares to the dot-com crash where most companies lost 90% of their market capitalization.

People laid off in huge, huge numbers. And back then, we thought it was the right time to start a company, probably because we were just graduating at a college. We had no other choices. It was either that or that or go. work for a bank. And so we decided we didn't want to have to wear a suit and go to work. We knew the graduate salary to work at PWC was $48,500. And we figured as long as we could earn more than that and not like have to wear a suit to work, we would be good. And so we didn't really

have huge, you know, venture capital style ambitions at the start of our company. And I think the advantage back then was that, you know, while we couldn't get any money, none of our competitors or no one else in the industry could get money either. And so, it was a level playing field there. Yeah. Yeah, totally. Bootschropping was a viable alternative. And we also, I think there was some technology transitions going on at that time. And, you know, we built our products on open source software,

which meant we could catch up to many of the competitors that were, you know, had out there, been out there. We, the browser was coming around. And so many of our competitors at the time had built client server products. And they all built a browser, you know, add-on as an extra thing, which meant they just had to support two different versions. And so we could catch up while they were building two versions of every feature. We could build it once. And so we bet on a new technology.

And also then, you know, the internet distribution had come around. And it's a long, you know, way from there to now stripe where you can just write a lighter code and take credit cards. but we were the very early days where you could, you know, conduct commerce online. And so instead of having to sell software for $50,000 or $100,000, which you sort of had to do when you were, you know, doing it on a golf course and with credit cards and faxes and purchase orders, like we could sell software for $5,000. And so we have these huge advantages in a time that we could catch up with our competitors.

And, you know, being in Australia, I guess we didn't really know any different. I guess if we'd been in the Silicon Valley, everyone would have told us that was impossible. In fact, many Australian venture capitalists told us it was really impossible to build a business that way. But it is a time and place that worked out for us. And you were builder founders. You were doing consulting gigs on the side, high-pite-priced consulting gigs, right? You're paid a couple hundred bucks an hour. And then at night you're building Jira.

And that is really the heart of bootstrapping. Almost every bootstrapping story I hear starts with people doing some kind of consulting work. They got an ad agency. They got a dev shop. And then they see some opportunity to build a problem. product and they go from building the product, you know, 20 hours a week and doing consulting 40 or 50 hours a week. And then the numbers just slowly switch to the point at which you just start telling customers, listen, I can't do any consulting for you anymore because

we got this other product that we built. Is that what happened over a couple years? Yeah, it was two bits of consulting. One is we started a support firm for a, it was a company built out of Sweden called Iron Flair and they had a product called Orion Server, which is back in the application server days where there was like 50 different application servers, you know, for supremacy on the internet. And so we provide support for this small Swedish company who had most of their customers in America. And that was a terrible, terrible business.

In fact, I'm glad it was so bad because many, I know many founders, you know, great founders get trapped in mediocre businesses. This was so bad. We had to get up at, you know, four in the morning or three in the morning when the phone rang to answer someone from the US, you know, and try and sound credible at three in the morning

trying to solve support calls. And so we did that for a while. then we discovered writing software is actually our passion not supporting someone else's software out there and we started building that but in order to bootstrap we still needed money so some of the people who had paid for consulting or paid for the support chose to fly me across to the Netherlands to work and do some code review over there and so I flew across for a couple of weeks and eventually a couple months and I would work during the day in in the Netherlands on what was a billing system for the Dutch telecommunications company. And I'd do a good job over there.

And I'd read up textbooks at night on how to code. And then the rest of the time, I'd be coding on Jira, which is our first product. Yeah, I mean, that's bootstrapping at its best, doing whatever it takes to keep the lights on, pay the bills. And then diverting, you know, the extra hours you have to building that product. And so how did you come up with the idea for Jira? What's the origin story there? We found back then in building our own software, like doing the support work, we realized there was nothing to track all the tasks that we needed to get done. And we built something sort of really crappily internally just because there was a need for it. And then we realized and went out

to the market to sort of see, well, what else is there out there? And there was nothing. There was either open source and the open source stuff was really terrible and it would take you literally a week to set up. You know, the first stage was compile my essentially. with these special flags and you know that's not really easy for for most people to do and uh all you had very expensive stuff that started at a hundred thousand dollars you know going up from there and you know sold by ibn and in many cases the software was consulting where you know they'd come in and that was their introduction to your company rather than a product you would you would buy and so we really felt that there was something in the middle that you could put on your credit card and uh and do that

and our biggest competitor back then was a company called, well, it was a product called Bugzilla, which was made by Apache, and it was an open-source product out there. And the word Bugzilla came from Godzilla, which was the sort of Japanese film. But it turns out that actually Godzilla was the, you know, anglicized Western name.

Gojira is actually the Japanese name, Godzilla. And so we dropped the go and Jira became, you know, the product. And back then you could buy forwarded domain names, you know, just on your credit card without any problems. Yeah, maybe I should have not started a software company and just bought up forwarder domain names. It would be more profitable. For sure. But we bought, yeah, bought gero.com. And, you know, we started building the product.

Amazing. And it was open source to start. You're doing bug tracking, project management, all that simple stuff. But people had solutions for this in the market. It was just generally client server software or that was what you were up against, the proprietary IBM software, Microsoft software, etc? Yeah, so I think there's a couple things here. One is there's a kind of called Rational, and I think it still exists somewhere.

I don't know if they've been sold to these days, but when we started, Rational had about 1,000 customers worldwide. And, you know, those customers probably on average, you know, spent a million dollars with Rational, you know, between software and services and so forth and keeping it up. And when you're spending a million dollars on software, that really reflects it down to a very small number of customers that can afford to do that. And I think a decade later, when I stopped tracking it, Rational still had about a thousand customers.

And so our original goal was to be very different. We had our first big, our first big, Air Adacious goal was to get to 50,000 customers worldwide. And it took us about 12 years to do that. And we set that goal when we had 500. So our big, Harry audacious goal for the company was 100 times our current size. But more importantly, it was in a sort of a sort of a, a vector that really made us differentiated from everyone else in the market because we were really

going to go after high volume, low cost at scale and sell globally. And so that really put the tenants. Like, if we're going to sell globally, you have to basically sell through your website. If you sell through the website, it has to be in a credit card. If it's on a credit card, you know, it needs to be able to sell itself because, you know, and it needs to be good enough that you can sort of try it out and then buy it. So I sit at this virtuous cycle that we probably now known as product word growth was sort of the didn't have a name back then, but we were probably the one of the earliest pioneers of people being able to try and buy business software on the internet. If your landing page is terrible, I'm out, right? Most consumers are. It's 2023. You can't

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And when you're ready to launch, I want you to go to Squarespace.com slash Twist. And they're going to give you 10% off your first purchase of a website or domain. go to Squarespace.com slash twist because they know we sent you. The other thing people don't realize is you created Slack before Slack existed. People were using IRC, I guess, to do, you know, like some general team chat. Really hard to set up an IRC server. It's meant for developers. It's, you know, core infrastructure of the internet before the web, IRC.

But you created HipChat, and that was just a side project. Talk a little bit about that. I mean, obviously, you wind up selling anything ultimately to Slack. But talk about that side project and what you got right there and maybe what you missed in terms of the opportunity to build something really big. Totally. So there's a company called HipChat that we were using internally. And we were early on the internet.

I sort of feel like we didn't build Giro. We would have built a dozen other products that we needed, you know, in terms of, you know, to build a software company on the internet. We built our own, you know, billing system. And we built our own, you know, effectively version of HubSpot internally. So when you're, I guess, early in these trends, you get to see a lot of, you know, kind of the new ways of doing things. And one of the things we felt was we used,

I see internally for a long time we used a whole bunch of other, you know, tools out there. And then eventually we set it on HitChat, which is great because it was all in one. And our developers loved it. And so did everyone else in the organization. So it was sort of the first development tool where developers loved it and everyone else loved it. And we acquired them.

When we acquired them, I think they were about six developers. And they had a quirky personality, you know, as a brand, as a company. And we, you know, doubled or tripled that team. and it was growing really fast. I think it was growing three to four hundred percent year on year, which for most of the companies, you would say is like, that's a home run. Like, you know, that's incredible.

And, but what eventually happened was Slack, you know, spun out of a games company that, you know, went south with Stewart, the game company that went south, the first created Flickr, the second created Slack. And so they came out with a ready-made product. And, you know, Stuart was very good at branding and PR and so forth. And so they ended up growing a thousand percent year on year or more.

And so it showed there was this huge category there. And eventually, we felt that when Microsoft teams ended the market, there was two players between Slack and Microsoft, and we didn't think that it would support a third player in the market. Even though we knew we had a better product, but I've used Slack since and it used our product and I would still maintain we had a better product but the market dynamics one can allow three. And so my lesson for that particular thing, like, you know, when people ask me,

what did you learn, what would you teach yourself or other entrepreneurs? A couple ones. One is, if you're early to a market, you need to bet heavily on that market. And, you know, we bet we, you know, doubled and tripled the team size, you know, in line with its growth. But we didn't bet heavily enough in that, in that market. We should have taken out banner ads and we should have really pushed that. The second one for me is that in an engineering sense, when you have a small engineering team,

you know, sub 10 people, the way that they operate as an engineering team is very, very efficient because you don't need to create documentation. Everyone knows where everything is. It's like a really small team. When you double that team to 20 or 25 people, you actually go backwards in productivity. you'll actually go, you know, and you really need to triple or quadruple the team to actually get forward momentum. And so though we doubled the team, we went backwards in productivity because you had to create all the documentation and the way the team's interacted and so forth. And so we should have really, you know, put a lot more effort behind that engineering team.

And the last one is big markets can be way bigger than you think. And even if you've got the numbers at your back in terms of raw growth, you should always look at in terms of like, what does that compared to the market size and what does that compare to your competitors and they were growing at stratospheric rates. We could have grown faster if we'd push. And this is the amazing lesson of entrepreneurship. Even if something's growing three or four times year over year, three or four hundred percent growth, you really need to test to see if it should be 10x growth and not accept that it's three or four X and you have to be even more ambitious. But sometimes, you know, especially you guys were, you know, first time entrepreneurs, now acquiring,

companies, you have to invest more. And then the paradox of investing, I love your second point, which is, hey, you had a bunch of people, that actually creates all this overhead. It slows people down. And now it has to move from a 10-person little SWAT team, a little Navy SEAL team, a perfect Olympic team, to now it's got to be an organization. And that's painful and requires infrastructure. And I suppose, you know, the other parts of the business, like Jira, are also growing incredibly well. So now you've got to pick which of these projects to focus on. And that also as first-time founders doing this, you know, running a house of brands is hard. You need to have leadership in each one, huh?

I agree. The trade-off between different, you know, capital investments is hard. And the interesting one for us is we're always profitable. So we actually had the cash ability to do that. It was constrained by the ability to find people and probably a little bit of our risk. tolerance. And that's interesting as a bootstrap. There's a lot of advantages in that, you know, you're in control of your own destiny. But, you know, you have a history of measured, you know, kind of investment and seeing the return and putting more investment in over time. And I think that,

you know, you've got to move to a VC model when you've got this huge opportunity that, you know, does that potential entrance and move a lot faster. I think we've learned that now when we looked at, you know, entering new products and new markets these days. We're much more aggressive. with putting the investments behind the new markets we go into. So tell me, now that you've got this new playbook and they go fast and really take the opportunity, what's the what's the aggressive playbook? What's in that one as opposed to the bootstrapping playbook? And how have you evolved that?

Yeah. So the two original bootstrapping ones would probably say geo and confluence where our original bootstrapped products and both came from just listening to market needs where a customer had come to us. And we looked and said, you know, we need this ourselves internally. And if we'll get more recent products, so Atlas and Compass, they still form that same playbook, which is we internally have needed something and we've gone out to the market and seen what's out there. And more and more, what we find is that we find products that companies like Facebook and Google, you know, have internal products to do these things.

But there's no product out in the market. And so we feel like, well, we need to build internally or we can build it for our and customers, and that's what we've done really well over the years. And so now we build products internally for ourselves, and we invest a lot more behind them. Like, we'll put, you know, 50 to 100 person teams on them. Not to start with, because I think there's always a ramp period where you want a really small dozen people that build the core of any product. I think that's the way to build any new go-to-market. But as soon as we start seeing traction in the market, we ramp up so that we can, you know, continue delivering features and make noise out there on.

on these products. Okay, so I want to get to two things. One, how you market them and scale them? But before that, how do you know you actually have product market fit? I got that piece in there that you slid in there about how you find products. If Facebook or Tesla or somebody, you know, a video game company like Stewart's old video game company builds some internal product to make everybody more efficient, well, of course, the long tail of companies, the quarter million companies that you serve, half million companies

service, whatever it is now, they're going to need it, right? And they don't have the ability to put 10 developers on and you build an internal thing. Some brilliant insight. How do you know you have product market fit? Once you do have product market fit, how do you scale? What are the things that actually work with product-led growth and products for developers and business teams? I think product market growth hits you in the face when you have it. And if you're ever worried that you don't have it, it's probably true, is my experience. And if I go back to it, Alassians revenue numbers, and I'll be off a bit on this. This is not SEC approved sort of numbers, but like from my memory, like our first year, first full year of, you know, selling Jira, was about $300,000 worth of revenue. The next year was $1.2 million. The year after that was four. year after that was 12. We then had 21, 35, 42. And that was when the global financial crisis hit.

We sort of only went up 20% that year. Then we went to 56, 75, and I think about 110. And so each of those, you know, when you go from 4 million to 12 million in revenue and you've got a dozen people working for you, life is pretty good and you're really just trying to hold on. And I feel like, that's when you've got product market fit. You really have that sort of near vertical growth. And I've advised a whole bunch of startup founders who think they have a great product, but they get trapped in these terrible businesses that grow at, you know, 15% year on year on a couple of million dollars worth of revenue and they're never going to be a hit.

And in fact, a good friend of mine runs a company called CultureRamp. And they do employee surveys and they're, you know, very very big company these days. But he was also someone I advised. They did a different product early on, and it just wasn't going anywhere. And, you know, I advised him that product market fit really does feel, you know, when you've got it. So we did that with, you know, GERAN Confluence. And so these days, I guess we've got a good playbook about what that looks like, and we keep trying, you know, until we've got that.

And then on the growth side of things, it's interesting. Many enterprise businesses have sales-driven motions. If you'll get many of our peers, it will be, what's my revenue going to be? Well, that's really just the number of salespeople I've got times the quota that we put in them, times the attainment ratios, times our rent period. Like, that is the way that they predict, you know, revenue. And look, at scale for enterprises, that's a, that's a reasonable way to do it. But if you want to build a product-led growth company, it's much more like a consumer model.

It's basically how many trials do I get? How many people are using the product? At what stage in the funnel are they? and that's much more scalable at that stage. You're not throwing salespeople at it to get every incremental dollar of revenue. And, you know, so we focus on metrics like how many active instances are there of our product out there, whether they're paying us or not, just how many people are using it. We focus on consumer metrics like monthly active users, which is a big metric we use internally. So we're sort of much more consumer focused in the metrics we use.

Does that mean you still don't have famously a sales team? You're still not doing like the SaaS model. Let's get a bunch of salespeople here. You're still committed to, hey, the product-led growth model, or did you ever add salespeople? I remember in the early days you didn't have salespeople. Yeah, we, for a long time, we had the sort of no sales mentality. It'd be like Salesforce is no software mentality. It's a great tagline.

You know, a lot of journalists want to write about it. And I would say, you know, of the 250 plus 260,000 plus customers we have today, you know, of 260,000, 250,000 don't have any person that touches them in terms of sales. So it's still largely product-led growth for everything that we do. What we found, though, is as you get successful inside large companies, they then want to standardise on you, particularly in the last year or so when people are trying to save costs, they want to standardise on you. And so they want to call someone up to have that conversation.

And we found that having a more traditional sort of, you know, sales motion in those customers, makes a lot of sense. And what we did with those people is we have incredibly high quotas because these salespeople are not out there prospecting for customers, trying to call people up, you know, looking at LinkedIn, trying to find a new prospect. It is really an existing customer that wants to talk to someone about how to use more of our products.

And so I think that's the way sales of the future is that, you know, if you're trying to get a new customer by calling them up on the phone, I think that's a really difficult motion, very expensive. you're ending up in head-to-head notions. And whereas if you start bottoms up, start with a team and a team's successful to another team, the enterprise process at the end to do a consolidation and get them to use a second or third product

is a way different conversation that you're having. In particular, at our price points, we're subbing out a lot of competitors, you know, that are much high price points, but we have the credibility because they're already in there. And I think that's eventually the model that, you know, I think most enterprise software companies should do, unless you're a workday and you sell one copy inside your entire system,

if you or any sort of collaboration product, whether it's us or Slack or anything like that, bottoms up with a motion at the top to help people consolidate is the way that it's going to go. Now, I do see some people make mistakes here, and there are many companies that started like that, and what they discover is that, well, when we add salespeople, we get great results. So we keep adding salespeople at every stage of the funnel, and eventually you get to the stage where every person who gets touched by a salesperson and it's death by a thousand cuts and it makes sense on an ROI basis at every stage but then eventually your website you know has contact us for a price and talk to a salesperson and you don't invest in the onboarding experience and

over time I think that ends up being an issue um so we are very clear about who gets touched by a salesperson and who doesn't yeah you I mean the total number of customers across all the products approximately I had read somewhere, you broke a quarter of million. Yeah, we have 260,000 customers around the world. I think that's like pretty much every country and territory we can sell to. That's wild. You've heard me talk about super gut a bunch. This has been a key part of my health journey.

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And they had to have the same discipline, which is the product had to be exceptional. You know, and the product had to sell itself. And sure, yeah, if you're big enough, we could have a consultative sale later. But there's something about having to please a two-person or a 20-person organization that just makes you really efficient and sharp on products, huh? I think so. Darmich is a great friend of mine, an incredible technologist, and also hats off to him for wanting to be the sort of technical person and not be the CEO. Like, I think it's very difficult for people often to make that. But he realizes exactly what he's great at, which is building products and he's deep rolling his arms up in the AI side of things as well.

And I think HubSpot, one of our employees is a board member over there, and they've learned a lot from our product-word growth model. And they're very similar, which is, yes, we could have a great product that sells itself. If you need to talk to us eventually at scale, we're here, but like we win because of a great product that sells itself. And I think that's really hard to disrupt, whereas there are many enterprise companies that are there because they've got a great sales team. But you can sell, you know, I presume Salesforce is,

HubSpot's large competitor, you know, there can be pockets of HubSpot, you know, in a Salesforce deployment. It's unlikely to be the reverse because of the way that the sales motion happens. That's fascinating. Yeah. I mean, it's just like hand-to-hand combat, like a much more guerrilla style. In a way, the product-led growth teams, like the ones at HubSpot, the ones at Atlassian, they're doing this like street level winning over the actual customer who uses the product every day. And then, you know, you look at like the oracles or Salesforce. You know, they might be doing like the, you know, going to the Warriors game or taking people out to dinner and using the sales and the CTO top down sales. And it's going to result in something very different as you're saying.

Darmus is obsessed with AI. Are you obsessed with it now too? And what impact is it having in the organization here as we move into this year one of chat GPT, language models and, you know, an actual platform starting to emerge that can be used by, you know, any business user, can just use any number of these language models themselves. And you get hugging face, throwing up new changes every day. It's got to be in your consciousness, huh? Yeah. Anchor AI in the way I think about it, in technology, because it's so much of a winner-takes-all

market, it usually takes some sort of technology shift to shuffle the playing board. And in those technology shifts, you find some companies that endure, you know, across multiple with them and some that stumble. and if you look back historically, you know, back to at least my lifetime, you had this sort of move to desktop software, which obviously, you know, Microsoft and Windows and Office were, you know, kind of the big beneficiaries of that. And then you had, you know, the sort of turn of 2009-99-2000 where you had the internet came along and you'd say, you know, Netscape was, you know, the big window there and Microsoft played catch-up, but it birthed, you know, companies like Google that wouldn't

have existed previously. And then you sort of roll through to mobile, and, you know, that birthed Apple, really, as a company. And, you know, in Google, you know, put sort of with Android, did it, did a good job there, but Microsoft wasn't anywhere really to be seen. Then Cloud came along, which is not so much a consumer product, but Microsoft, you know, caught up heavily with Cloud and suddenly Amazon's, you know, in the race as well. And so you see these technology shifts where a couple of the big players maybe make the shift

and a couple of players don't make the shift. And I think AI is the next big technology shift that's going to shuffle the playing board of technology. And when I look at, you know, I've gone deep with all the different large language models and how they all work. In some ways, the industry has been saved by large language models because if you weren't deep in AI,

you can basically rent a large language model from someone, which is very different, I think, to how most people thought this world would play out. And so there are a lot of companies who are really just playing, you know, catch up effectively by bringing these large language models who didn't have investment. So it really is different than most pundits would play. I thought it would. I think the real value is going to come from putting data together with these models. Because the large language models at the moment, I view them a bit like a Swiss army knife.

They're expensive. They're not particularly good at any individual thing, but they do a lot of different things for you. And that's great. Everyone's going to use them because they're the first thing available when I can pick up one of them and use it for lots of different use cases. I think over time there's going to be specialized models. If you just want to do language transformation, which we do, we convert text to a query language. We don't need a multi-trillion parameter model to do that. And we can have much faster and cheaper and, you know, even cheaper and faster are pretty

much the same here. And so we can actually have better user experience and save ourselves money. So over time, I think you can see specialization of a particular niche. or niches, I think, is it said in the U.S. Yeah, either of those are acceptable. We'll accept both of those as answers for niche or niche. You're allowed to say both, yeah. But I think that's the correct answer is, you know, right now, the Swiss Army Knife,

I love the way you put that. I can go in there and ask it about travel or to write me a blog post or throw in some code and clean it up. Great, great Swiss Army knife. But if somebody makes a verticalized thing like, you know, GitHub's co-pilot or Stack Overflow is working on one, or somebody works on something just for finance or just for travel. Of course, it's going to have a lot of features wrapped around it and a lot of verticalization and then reinforcement learning, and it's going to blow away the general model.

That should be pretty obvious. I think it's pretty intuitive to think that's going to happen. And those are going to start showing up in the next year probably, especially with all the open source projects. So you're in open source, a lot of your success based on open source. So do you think the proprietary models like closed AI, previously known as Open AI is pursuing, what do you think is going to win?

The Open AI model where it's closed, or the actual open source models, you know, I guess Facebook's Lambda is open source and other ones that are coming out. Which one's going to win the day? I don't think that for us where we sit in the ecosystem, it really matters if one or other people, win the day because they're so interchangeable.

You know, for me, it's an API call to use a large language model. And so unlike saying, well, you know, how hard a choice was it to choose between Amazon or Azure or Google to host in the cloud, if I'm going to switch between one of those things and I've invested millions of dollars, you know, right into their APIs and working that way, like the switching costs are very high. The switching costs, you know, for large language models are very low. and we've at the moment partnered with Open AI because they're the best and we've worked with them even on their contracts and how they store data and making sure they're much more, you know, B2B friendly. We think that we'll be probably using lots of models over time and the real value is going to come from putting data together and having those data use cases.

And this is where I think the landscape is going to change a lot because if you're a very small point player at the moment, you do one very, very specific thing for customers, I think you're going to be at a real, relative disadvantage compared to, you know, the larger players out there that do multiple things. Because if you do multiple things, you have lots of data across, you know, the life cycle of that customer. And that can mean that your experiences can be way, way better than they could be initially. And so I do think there's going to be a bit of a, the big get bigger in this next phase of the internet. I'm not sure if that's good for everyone. I mean, it's great for Alassian. I don't know how good it is for society. But I do think that the, you know, the use of DOT or is going to be the big, big win here.

It does seem that you could switch these out really easy. I've been playing with it. And you can very easily, you know, send your prompt engineering through one and then try it in the other. Look at the response teams. I think there's going to be some meta layer here, kind of like CDNs or, you know, I guess some people with their cloud providers create, there's a term of art for it where you can have your, you know, jobs in the cloud, go to different providers.

You go to AWS for one thing, go to another. And have that redone. Multi-cloud, yeah. So you can have like a multi-cloud thing. I can see people having like multiple, you know, chat GPDs and just go to the language model that you think is best for this current job. And meta's model obviously is Loma. Googles is Lambda.

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Yeah, it does seem like they're already getting commodified in some way, and then whoever has the data is going to win the day. You have all this data on, you know, I mean, that's another thing with a long tail of 260,000 customers. You've got a lot of data, and you can provide a lot of speed efficiency

if people are trying to manage tasks or clear out bugs or whatever. they're doing in the software that you provide, you're going to be finishing their sentences, huh, and being their co-pilot, any of those products released yet? Have you put any in the wild or you're in the laboratory right now? Yeah, a couple of things. One is I think that from a B-to-B sense, like they'll be interchangeable. We all know that consumer behavior is something that, you know, is harder to change. And so, you know, there might be a better search engine, but if I'm used to using Google every single day, am I going to try a vertical search engine for

that one search to do something, probably less likely. So I think there is, you know, And I think Open AI has probably got the lightest consumer side of things. So I think it'll be, you know, from a B2B sense, yes, there'll be a lot of providers. I think it's still open how the consumer side will play out. If I go back to Elassian strengths, you know, we have knowledge about how teams work and, you know, how strictly around engineering teams and development teams. And so we have information around, you know, what code gets written, what customer problems happen, what bug reports happened.

And I keep challenging my team. teams to not just improve the way that our current customers do something that really sit about the job to be done. And so take an example like customer support. No customer wants a faster customer support experience. They want to have not had to reach out to customer support in the first place. And, you know, if you take it to the example of a app, you know, on your phone, it's like, well, why don't we send all the log files and all the errors from that app on your phone,

every single night, like, you know, back to the developer. And, you know, previously, if there were minor errors in products, you just couldn't find the needle in the haystack and or fixing them would be too expensive. But these days, with, you know, with AI, you can now see, oh, actually this little bug here, I can even see the code. It creates that bug. And to fix that bug is a trivial, you know, change. And so I can, you know, change the code.

And maybe it's a human gets reviewed it. Maybe it's a different large language model that has a whole different training set reviews it. So you've got two different sets of eye. you know, reviewing that code. And suddenly your code gets more robust and, you know, sent out based on, you know, real-life data that comes out from the field. So instead of your customers having to file a bug report, that actually just never had sort of bug in the first place.

And I think that's what we're going to see over, you know, in the short term, we're seeing great party tricks like, hey, turn this list of three things into a list of 15. That's great. It looks good. It's kind of cool. But the real value, I think, comes from totally reimagining, you know, customer experiences. How far away do you think those will be? We've had this parlor trick,

like incredible. It's helping me write the blog post. I make it shorter. I make it funnier. And hey, it got 60% done, 70% done. I polish it. Wow, this is crazy. I need to, I don't need to have a PR firm write a press release. The AI wrote it for me and I just polished it. So for a young startup, why hire some PR firm if I can just, the CEO or the person running product can just, you know, become a bionic. So when do we see what you're talking about, which is, hey, we're going to intercept bugs. We're going to have multiple language models, review it. And, you know, it's sort of like precogs and minority report predicting what's going to go wrong and solving it in real time. That's trippy stuff. Is that five years out, 10 years out, two years out?

I don't think that's five years out. I think that we're working on things like that at the moment. And to take a more knowledge work example for, you know, the listeners of years that don't don't write code. You know, if you go away for a long weekend, you come back on a Tuesday and you're like, hey, I want to catch up. What did I miss? At the moment, that is a painful process because you go, well, what do I need to read? What's important?

What got actioned already that doesn't need my response? And because of the data we have on teams and teamwork, we can say, well, Jason, actually, your closest peers, you know, over on Monday all collaborated on this document. By the way, here's, at the end of the day, here's the change from X to Y. I don't need to do that as a diff with red lines. I can actually explain the changes in human readable form for you. And I might be able to explain it in one sentence or two sentences. And if you want to get the three paragraph version, you just click.

And if you want to see the actual red line changes, we can do that. And so the ability for a knowledge worker to catch up on work is huge. And if I look at at least my time, and I'm sure your time, is like reading through or even understanding what to read through is like a huge burden for most knowledge workers. and that's the stuff we're working on at the moment. That's absolutely brilliant. And I'm having this experience. We started recording every meeting we have at founders for funding.

Then we Zoom gives you the transcript. So you kind of get that for free. Then we put it into Notion. Notions got AI built in. And we said summarize it. And so now I get these little summaries. Hey, the investment team met with this person. They talked to the founder about this.

The founder said this. They're talking about a term sheet. There's a liquidation preference. I mean, it's scary how accurate it is. And it's saving me having to do that catch up. It's like having Jarvis in Iron Man or something. You walk up to the desktop. And it starts explaining to you, hey, here's what you missed.

You know, Thor's in the other side of the galaxy solving this problem. Hulks over here causing these problems. What do you want to do, Iron Man? And that is invaluable. That doesn't exist as a current product. And I don't know why Slack doesn't do that right now. Slack's AI is just kind of MIA. It would be incredible to open up my Slack

and just have it tell me, here's what's going on. Here's the changes that you missed. And so that's a great vision, I think, for time savings. What do you think is going to happen? Because you're running a, good. Yeah, no, respond. Well, I think the thing with Slack is that if you think about the benefit, like, at the moment, you're stringing multiple tools together.

You're saying, okay, let me take, let me do a Zoom meeting. They get a Zoom transcript and throw it into, you know, notion or confluence and get confidence to summarize it. And then I'll organize that in some way where I get to. see the summaries in a certain way in a certain time and I've got to work out my workflows. And for most people that they can do that, but it's very complicated to make that happen. And same thing with Slack is that, you know, Slack has short form, you know, here's 10 words, here's 50 words, like it's not paragraphs that need to get summarized.

And for you to really understand a Slack conversation, you need to understand the context. Who is this person? What is their job? What are they been doing? What are they written in Confluence like, you know, today? What have them been doing in their coding? And I think you actually need to understand more data points across that ecosystem. And because of where we are at Alassian, because you can see the code and the stuff that,

the specs that they're right and the jobs that they had to get done in Jira, like, we have that data. We can provide all that. So eventually we can summarize it in a way that's like much more turnkey than you having to do it like a bit at a time. And so that's what I think someone like Slack might be able to do it. But if you had to pick out of the two, you'd say Microsoft Teams probably has a better starting point because I see more of the workflow that a knowledge worker does. So that's what I think, again, that data gravity makes a big difference, and we have a huge advantage there.

And we didn't even talk about it suggesting what your next move is. So you come back to work, and it's like, hey, here's the three things that happened. By the way, you know, you have these three, this topic of this customer churning, we're threatening to churn. Here's ways in which we've saved customers before, whatever, you know, issue that you've suddenly been faced with. the AI could start giving you ideas of how to address that incoming issue. How efficient have you gotten internally?

I know you guys did a small riff, maybe 5% of the company or something during the 20-22 period, I guess in the down market. I think that's when it occurred. A lot of people did that. People did bigger ones, obviously, 10, 20%, and they didn't see, they saw things get more efficient. Obviously, if you cut the bottom couple of people, it's going to do that. That's just a performance metric there on any team. But today with AI in the enterprise, do you see yourself having to add a ton of people

or just making the decision, hey, how do we point AI at this problem? What is your default as the leader of the company or the co-lead of the company? Yeah. I'm bringing another trend here that's related because we're putting the two together. We're the largest company that's committed to remote work in the world. We have about 11,000 employees, and no one is required to come to an office any day. You can work from home. You can work from the office.

You can work from a cafe. You can work from a trailer, you know, traveling around the United States if you wanted to. And, you know, for a lot of times, you know, people's objections to that is that, well, how am I going to learn from that person sitting next to me at the desk? And, you know, you talked about the sales call example that, like, hey, we know how to save a customer or not. And, you know, one way to learn in a sales call is to, yeah, listen, you know, I'm sitting next to the person at a desk when they do the sales call. But that's a very much sort of whack-a-mole just happened to be, you know, at the right place at the right time, to hear someone slightly better. If I look at using a company like Gong, which effectively records sales calls and then, you know, does the transcription and then looks at competitive analysis and you can tag, hey, like, this was the best saving of a sale customer from this particular. competitor or this is the best pitch in this particular scenario or this vertical I'm still

into the healthcare space like here's the you know the best person that pitched the healthcare space I think we can end up with a world where you know training and learning from other people is actually mediated by computers in ways that are way way way more effective than they ever could be by just happening to sit next to the next to the person and so we as a company of committed to remote work to build out a lot of those capabilities and we're kind of of the canary in the coal mine about how we're building out those experiences, whether it's whiteboards in our confluence product, you know, and how do you make a digital whiteboard experience better than kind of running into people, or it's, you know, the summarization of data,

like, so that you can catch up on people and what's happening that's not, you know, bumping into the water cooler. So we sort of put remote and AI together because we think that the combination of those two is really changing how knowledge workers interact. And so, you know, to back to your original question around, are we doing this internally. We've got, you know, AI projects across the entire business. And we think, I think sales and customer touch will be heavily disrupted because I think there's a lot of busy work in many sales teams, a job in terms of communicating with customers, but even just researching and understanding like what a customer is doing

with our products. And we can surface that in incredible ways. And so take an example of a, you know, salesperson wants to upsell someone, you know, to an enterprise version of the product. You know, they can see, you know, we have all this data about what our products get used for and how they get used. Of course, you know, not looking at the customers, you know,

private data, but just like, hey, which features get used. But previously all that stuff would have been too hard to look at on a feature by feature basis. But we can, using AI, summarize that down for people and say, well, actually, we think the enterprise features that we most appealing are X and Y and Z. And you can do it in real time, right? be you can be doing that in real time. That's data that some team would work on a team of data

scientists for three or four weeks for some off-site meeting every other year. People would say this is amazing. Then it would quickly be outdated. And it would be like institutional knowledge that, you know, just doesn't exist anymore. Now it's going to be real time, right? So these assistants are going to be telling you in real time while you're on the phone call. Yeah, you know, this type of customer has gotten these features that work best. This customer is using two out of the three. So maybe this third one, they don't even know them. Maybe they need training on that one. Maybe they don't even know that product exists and we need to sell that into them. Do you worry about, I mean, it's kind of a, you know, a very important question in some ways and a silly one and others.

And I'm wondering where you sit on the sort of spectrum. Do you worry about this technology, which is moving faster than I think you would agree anything we've seen in our lifetime. Maybe the spread of broadband, the spread of smartphones were also very fast. But this is faster, I think. this is going to have a tremendous displacement effect on certain jobs. So do you worry about that or do you think, yeah, you know, it's overblown? It's a couple things here. One is that, you know, the initial technologies, I did a lot of research around how electricity ran through and kind of changed the world in the 1800s.

And it's interesting if you ever gone to New York, you see, go to Soho, you see these multi-floor, you know, warehouses, basically, that, often been turned into waft departments, but you walk down and think, okay, this is the manufacturing district. And it looks nothing like how we do manufacturing these days. And it turns out that the way it worked was back then you had a steam engine that was in the middle of these factories, often on the second or third floor. And effectively, the steam engine would drive belts and pulleys to effectively have all these machines. And you would bring your products up to the floors because it was really almost a sphere because you're going to be as close to that steam engine as possible

because the belts and pulleys would effectively lose momentum and slack and over time. So you had to be as close to the centre as possible. And when electricity came in, what they did is, you know, those steam engines used to explode and kill people and other things. The factories would replace that steam engine with an electric engine in the middle of the same factory in the same floor with the same bolts and pulleys. and now it was great, people didn't die, but the jobs didn't really change. You know, there's like someone throwing coal into a steam engine, but apart from that,

if you were a worker on the floor, you didn't notice the difference. And so that was phase one. And that was basically, you know, sustaining what you currently did better. And then phase two came along where it's like, well, hang on, now we have this thing called electricity. We don't need a central, like one central blower, effectively powering everything. we can have multiple smaller tools. And that's when you sort of saw the Henry Ford production line. It's like, well, actually, let's move the product between the different tools as opposed to, you know, the reverse.

And that's sort of the second stage is where you start retooling how things get made. And eventually then the third phase is, of course, you know, electricity being embedded in all the products, like. And I guess you've probably seen that now with electric cars, but, you know, sort of you had various stages of that, you know, along the way. And so that's what I think about AI is you go, okay, phase one is going to be existing tools, existing products, existing processes, slowly augmented. We've still got, you know, the belts and pullies to the same engine, but like it's a better way of doing things. And that's what we're seeing right now. And then over time, you say, okay, well, actually, you have to reimagine what that looks like. And, you know, that reimagining part, that does change the jobs that are available.

But all the experience we've had in the past is that the jobs. that are available, you know, exceed, you know, the new ones exceed the previous ones. But there is a period of turmoil in between. That period of turmoil can last a decade as, you know, as things get jumbled up. When I look at the industries to be affected, I say, well, what's demand constrained and what's supply constrained? And in software, which what we sell to, you know, if we could have more software developers out there, we would.

Like, they would get sucked up in the market immediately. I feel like that's a supply constrained environment. I think the open questions is in sales, is sales supply constrained or demand constraint? If my salespeople were twice as effective, would I hire more of them, would I hire less of them? And I think that is open in certain industries

as to like when we can make them more effective, do we need more of them or less of them? Things like accountants and back office things are probably clearly in the twice as effective. I just need half as many. Other areas of the business, it's not as clear. You know, it's so funny. I think as technologists of a certain age,

We're constantly trying to, now that we have our, we both have like three decades of this. And we watch the dot com bus. We watch the great recession. And we've watched multiple paradigms shift in our own lifetimes from mainframe, computing, mini computing, desktop, client server, cloud. We've watched this so many times mobile that we even go back further and look for additional context. The one I use, you did electricity. I did.

And I'll pull it up here because I think it's hilarious. While we were talking, I pulled up my chat GPT. and I was like, hey, Chapman T, T, tell me about the history of ice shipping to homes before electricity. Because I had realized, like, this was the big craze. And when you were talking about, you know, Soho where I grew up in New York and I was obsessed with that area in the warehouse. So I wound up living in a warehouse building on the west side of Manhattan on 26 on the west side highway, 13, 14 foot ceilings. And it was made to have, you know, like steam engines and all kinds of stuff. There was a 95-year period, which here's the description of it, from 1805 to basically

1900, where there was a massive amount of investment in entrepreneurship for a century around harvesting ice and bringing it to India, the Caribbean, all over the place, and coming up with new ways to systematically harvest it and ship it and maintain it. And there were a ton of people who would just drive around with horse and buggy in New York, to bring ice to your refrigerator. You got it dropped off every day. And then boom, overnight, electricity. And then boom, the refrigeration unit.

And this is what, but we didn't, we didn't have a permanent, unemployed class after this happened. Nor did we have a permanent unemployed class after, you know, phones went away. And phone operates, connecting calls went away. We found new uses for human ingenuity. That's what will happen here. but the thing that I do find very interesting is every time I'm doing a new job wreck now, I look at the job and I'm like, what are they going to do every day? What are the actual tasks?

What are the goals? What could we automate? And I'm finding about 20 to 30 percent of each job could be automated away. So, and watching a portfolio of hundreds of companies, I'm seeing the same thing happen. You know, three or four person startups don't add the fourth or fifth position. They just, you know, they add the fourth. but they don't have the fifth and sixth. So the capital efficiency of these, you know, the most, what's the word, dexterous,

the most scrappy companies, they're looking to AI first and then solving their problem with AI and then going on. I have a friend, Brad Gerson, who was making, he wanted to make a video for a new project he's working on. He was working into script and then he took it and then he put it into, you know, clawed to make the script. And then he made this like generative AI video that looks like something, I guess he used 11 labs or something to make a marketing video.

Now, this is something that a marketing agency would have spent, I don't know, low tens of thousands or an individual contributor might have spent three or four thousand dollars building. But when I talked to him about it, I had encouraged him to just do it himself, write the script himself, and then he took it to the next step. The big learning is, well, if you take some people out of the process, it becomes more efficient. To your point about when Hipchat went from 10 to 25, it's actually more efficient if you

can do the tools yourself. All these creative things, you know, you weren't allowed to, as a business executive, explore your creative chops. You had to go to a video editor, a designer, a logo person. And now you got the founders of companies. I watch them. I'm like, oh, it's a beautiful logo. How did you make it? They're like, AI. I'm like, you made your logo with AI. They're like, yeah. And then I remember five or 10 years ago, maybe 10 years ago, people were using like, you know, they would use fiber or something. They'd make their logo for 500 bucks. 10 years before that, when you were, you know, doing it Lassen in the, you know, 2001 to 2010 period, what did a logo cost? You'd pay a design firm three, five, ten grand to do a logo study.

What did you pay for your first logo? Do you remember? I think we actually, if you look at the history of our logos, I think it's pretty clear that we've made them ourselves. But that would have been made a lot better, I think, you know, if we'd had all the AI back then. And I think it's the creative class. I think that, you know, people ask me what advice do I give to kids about, you know, where to spend your efforts and what should you learn. And clearly the accumulation of knowledge is not something that is going to win anymore.

The person that knows the most in the room is going to be beaten by Wikipedia every day of the week. But of course, the accumulation of knowledge builds you the skill set of learning and like learning quickly and learning how to think differently. And so I do think there's a benefit of still acquiring knowledge, not for knowledge of sake, but for getting great at learning. And so then it comes down to, well, can you bring multiple different disciplines together, which is sort of creativity.

Like, can you create some new idea or something new to the world? And that's the interesting part. And so I do think there should be a renaissance in, you know, the way we teach kids, like to be less about our learning and more about what are the ideas that haven't been thought up yet. Again, I don't know if there's any traditional schools in the US. There aren't as many in Australia that teach that way. Yeah, we definitely need to rethink education because if you can just sit there with one of these guides, like you said, you know, pulling up the Wikipedia page, just kind of great equalizer, wrote learning. You really need creativity, drive, grit, resiliency, teamwork, leadership, you know, all of those skills, which, you know, it's very hard to teach those things become, I think, really defining, listen, you give me a bunch of time. I just want to ask one or two more questions, which is, how does they stay motivated in decade three of your startup? A lot of folks, you can get Jeff Bezos is on a boat somewhere. he's like done. I think he's going to come back. I think he's going to get bored. You saw Bob

Iger retired. He told he told CMBC he was like I was on a boat for like six months. I lost my mind. I had to come back. You think about retiring. You think about other projects. You think about philanthropy. I know you've done some great philanthropy work. Investing you backed a lot of the like I think you LP'd a lot of the Australian funds from what I understand did a bunch of angel investing and stuff like that. You ever think of life beyond Atlassian and how do you stay motivated in the third decade at the same company. Yeah, I do not plug the philanthropy side of things for answer your question. Please, yeah, open it up.

I started about a decade ago, actually almost two decades ago, we committed ourselves to the sort of one-one-one-one model, which Salesforce did as well, which is we give at Alassian 1% of our equity, 1% of our profit, 1% of our product, 1% of our employee time, our way. And we did that for a long time. And it's been great for us. Employees love it. they joined for it. It's been a huge boom for us. And about a decade ago, I looked around and realized we were still one of the few companies doing this. And so I started a foundation called

Pledge 1%. And the aim was to convince every company to do that same model. And in that decade since we've got about 17,000 companies now have pledged to give 1% of product, employee time, product or profit away. And so like we're really starting this corporate. corporate philanthropy movement. So I just encourage many of your listeners are, you know, a startup. So I just encourage you to check that out because no matter what stage you're at, it could just be the first day of your business. It could be you've been running for 10 years. You know, Pledge is a very easy thing to do because it doesn't take time until you've actually got something to give back. And so quite proud about that.

It's a big number. Well, it's a big number for Atlassian, right? Like 1% of Alaska is a lot of equity. and 1% of 10,000 people's time or so. That's 20 hours each. That's a lot of hours. You're talking about 200,000 hours. We've given away about 200-something thousand hours. We've given away 100-something thousand licenses,

either free or discounted to communities on nonprofits. And we've given away, I don't know, $50,000 or $500 million to charities over that time. Obviously, it's still corpus of money to give away. So, be huge for us. it's eclips by what Pledge 1%, you know, can do if you, as many companies out there from, you know, to PagerDuty that have, you know, taken the pledge. So it's great.

Yeah, if you're a startup out there, check it out at www.org. Go to Pledge 1%.com. On the next side of things, what's next? I still get inspired by a mission, which is to unleash the potential of every team. And the sort of Archimedes said, give me a lever long enough and a fulker on which to move it and I'll move the world.

and the idea is leverage. How do you, like, improve things with a lot of leverage? And if you look at our products, they get used by everyone from SpaceX to the American Red Cross to the Australian Antarctic Division. And if we can make every team, you know, 50% 100% more effective, if we, you know, that is going to change the world more than anything else I could do individually. Like, I could go, you know, start a space company or start, you know, any sort of like, you know, huge entrepreneurial venture. out there that's made a difference, but the compound effect of helping 250,000 companies and tens and millions of people to be more productive and like enjoy their jobs more and get more done for the world. Like that's way bigger impact that I could have in almost any other

domain. And so like that that gives me excited at the mission level. And then as an entrepreneur, you know, we're 11,000 people two decades ago. We were two people. So every single year, there's a different challenge and a different journey to go on. And that, uh, intellect is hugely intellectually stimulating for me. I love, I love learning, I love trying new things, I love, I don't like failing as much, but like that's part of the process and yeah, feeling sucks. But it's really interesting to think about what it's like to manage 11,000 people. I mean, you must meet people, you're walking around on the weekend with your kids, you must meet people who work for you who you don't, you never met. It's kind of

be surreal. You do, yeah, when people wearing a license shirt, you know, is around the street, or, you know, getting a selfie with your staff is a new one for me relatively recently in the last couple of years. And again, it's just a realization that, you know, across 11,000 people, you can't have a one-to-one relationship with all of them. But you can inspire them. In fact, one of my maxims is that the most important thing about leadership is to set a vision. People work for a complete asshole who has a great vision. They won't work for the nicest person of the world that has no direction. So my number one thing for leadership is vision.

And so I'm hoping across 11,000 people, we can still set a pretty compelling vision, even if I can't have that one-to-one relationship. Yeah, fantastic. Listen, Scott, thanks for taking so much time for the This Weekend Startups audience. Amazing journey. Keep at it. And I'll be back in Australia. I think next year we're going to do the launch festival again.

So hopefully I'll see you down there and either city or Melbourne, one of those cities. That would be great. Yeah. Congrats on everything. And we'll see you next time on this week and startups. Bye-bye.