Well, once you add AI to the mix, maybe it'll work this time. I mean, this is the thing that is being sold, right? Is you have now a powerful intelligent orchestration layer that will finally realize the stream.
Three parts snake oil, two parts bull.
I find it amusing that if you put an engineer, a designer, and a product manager in a room, they'd all sit there and they'd all say, 'Oh man, it sucks to be you. Your job's going to go away.'
And I'm like, ah, that probably is your answer right there. Mark Zuckerberg, there are reports that he wants managers to have teams of 50 empowered by AI somewhere inside of Meta. This strikes me as not a great idea, but we're going to see how it goes.
I'm certainly more measured than that.
Hello and welcome to Decoder. I'm Nilay Patel, editor and chief of the Verge, and Decoder is my show about big ideas and other problems. Today I'm talking with Mike Cannon-Brookes, the co-founder and CEO of Atlassian. Atlassian is one of those companies that every other company runs on. It makes important platform tools like Jira and Trello that allow people to organize and manage big teams, create shared databases of company information, and generally allow work to happen.
Atlassian is also right in the middle of the way AI is changing how every company works. AI tools might be able to look at all the different tools and systems you have and just read them for you, making a big migration to Atlassian's platform less enticing. Or if you buy the idea of the so-called SaaS apocalypse, AI might just build all of these tools for you, destroying this entire category of businesses.
Of course, AI has also changed Atlassian. Like so many other tech companies, Atlassian did a round of layoffs earlier this year with Mike saying that he felt the company needed a different mix of skills. So, I asked him what he thinks that mix of skills is and how AI is changing what it means to run a software company. This was a fun one. We got deep. And shout out to Mike who recorded this from Atlassian home base in Australia, which means that he had a deep conversation about org charts at 5:00 a.m.
Before we get started, subscribe to Decoder on YouTube to watch new episodes every Monday and Thursday, and subscribe to The Verge to listen to this episode and every episode completely ad-free. Okay, Atlassian CEO Mike Cannon-Brookes. Here we go. Mike Cannon-Brookes, you're the co-founder and CEO of Atlassian. Welcome to Decoder.
Thanks for having me, man.
I am really excited to talk to you. I believe you're in Australia. We have a massive time difference. It's tomorrow for you.
It is, as well as today. It depends on what's going to happen tomorrow.
At this point in the AI news cycle, I feel like I can just demand to know what's going to happen tomorrow. And everyone understands the urgency behind that question. There's a lot going on with Atlassian. There's a lot going on with your products. There's a lot going on in the very concept of SaaS businesses and business processes generally. Let's start at the very start. I think people know Atlassian. They know Jira. I started my career as a Trello person. Tell people what you think Atlassian is today.
Atlassian is a platform that enables businesses to collaborate and manage work across their teams. So we connect their business teams and their technology teams to handle the most challenging work problems in a singular platform across any organization that is technologically driven. So any organization that has software and technology as its core competitive advantage, we make a broad platform that allows them to collaborate on content, manage projects, unleash the knowledge of their teams and organizations across their business, their strategy operations, all the way through to their service teams in all areas of their business. So it's a very large platform now that we provide.
Let me ask a question about that just in terms of what work is today and what it might be in the future as we add more and more AI to these enterprises. Maybe this is too reductive, but I have always thought of Atlassian as a company that makes tools to help teams figure out what they're going to do tomorrow. So like Jira is like a perfect example of this. You don't do the software engineering in Jira, but Jira helps you organize large teams of software engineers and file tickets and prioritize problems and tasks.
I was a Trello person a long time ago. My goal in life now is to never use any enterprise software. I feel like that's a sign of true success if you're just like an iPad person in first class. I'm working on it. I'm not saying I'm there yet, but that's one of my goals. But there was a time when I was the managing editor of the Verge and my job straightforwardly was to look at Trello every day and make sure everyone was doing the right thing at the right time.
And I've always thought of Atlassian as that class of products, right? We're going to organize the processes of the company. There's something about AI that's changing that. Has that conception of Atlassian changed for you and is it changing faster because of AI?
Understanding what work you have to do at an individual level like Trello, at a team level like Jira, or at an organizational level like our strategy collection for large scale strategy and operations — that is definitely a big part of what we do, right? Understanding where processes are at. It's less about work to do than processes. Right? If you think about a business as a system of processes that are put together, everything in a business is some sort of system. It's some sort of process. And the collection of all these and how well we execute them is what your business is doing. The fundamentals of that haven't changed.
The way those processes run, the number of them running, how to figure out what's going on hasn't changed. Right? So we think about Jira as a human reference to work. When we say that, it's a really interesting term because as you said, the work is not done in Jira. Developers don't live in Jira, marketers don't live in Jira, finance people don't live in Jira. What Jira is, is a workflow engine with highly collaborative parts that enables teams to understand what their colleagues and their broader organization are doing.
That's why agents and AI and other things — they're going to increase the speed of those processes. In a lot of ways they will increase the reliability, the quality, the consistency of output of certain processes. And they also put more emphasis on where the humans, where the judgment, the intuition, the human bits that are unique, the initiation come from. And it's our job to make sure that that's all still understandable, right? So someone can just kind of look at any level of their business and know what's going on, right?
What are people doing? Are we successfully doing whatever it is that we're trying to do? Whether that's a service function in a finance sales deal exception service desk to work out how many exceptions did we give out in sales this quarter? Whether it's an engineering team who's trying to work out, man, there's like stuff happening all over the place. What did we build this month? Or what are we going to build next month? All of those things are incredibly hard to do at scale and volume in a business.
One of the more important features of the tools you've built is legibility. It's user experience, right? You have a big database of exceptions given out by salespeople and you need to look at a dashboard and just see how the company's doing. Or you have a whole bunch of tasks and Jira will let you see at various different levels of abstraction how many tasks are being completed at any given time. And that was something that's very important for human managers, right?
And a lot of the capability unlock of businesses as they added software like the software Atlassian makes is to just be able to see and coordinate more things at higher scale. The turn that's coming is that maybe those databases aren't going to be looked at by human beings anymore, or they're going to be fed into more synthesized databases, or AI is going to make some other kind of information at different levels of abstraction. I know you've got a product called Rovo which feels like the new face of Atlassian in some way. What's the path here? What does the end state look like?
Look, there's no doubt AI is going to help with a lot of those processes, right? There's no doubt it's going to pick up some steps in a process. Most businesses at the moment, they have a process and they're using AI to automate 80% of this step, right? And it may not be 80% of the distance. It can be the bulk, right? So if you think about that sales deal exception process that you just mentioned again, if AI can take 80% of the exceptions — say, 'Hey, this customer wants 45 days not 30 days' and we can write a relatively readable document, AI is pretty good at reading the document and saying, 'Yep, this one's fine,' right?
But there's going to be a customer that needs a lot more complex exceptions. They have, 'Well, I want to pay in 90 days in this way and I want this thing and that thing' — and that needs to go to a human for intuition and judgment. So 80% of those may be easier done with AI now because it's very capable at processing our rules and in this particular part of our business. And that's great. It'll increase the speed of the process, the quality of it. It doesn't take away the need to know what's going on. So someone in the business will still need to know, okay, cool, how many exceptions did we give?
And what happens in most of these areas is businesses find way more ways to make themselves scale and efficient and they find way more ways to make higher quality products and services with AI. I see this all the time where they are utilizing AI in ways that people don't expect. It's not all about mechanistic efficiency. There's a huge amount about quality that is able to be measured, understood, that was never before, right?
And the deal exception process is a good example because it's actually a very human process. It's not usually rule driven. The rule driven parts are easy. It's the non-rule-driven parts, right, that are more complicated and require judgment. Overall, the business still needs to know for that process: do we need to add more people to this process? Do we need to change it? How much is going through it? You still have a lot of work to understand the flow of work around an enterprise, which gets more complicated as the flow's volume goes up a lot.
I'm obviously asking these questions because I'm headed towards a series of questions about the supposed SaaS apocalypse. And I'm very curious where you think tool providers like Atlassian fit next to, okay, the frontier models are just going to eat more and more capability eventually. Cloud will just do this for you. You'll say to Cloud, 'Run my business for me' and in the back end Claude will burn a bunch of tokens and it'll just do it all for you, which is a promise that many many people seem to believe in.
And you're describing a somewhat different path, right? Where the tools get smarter or more capable because of AI and you're still adding a lot of human judgment, but at no point is a frontier model just coming and running your entire business for you. I think if a frontier model is capable of coming and running the entire business for you, even assume four or five years worth of increase, you have a relatively simple business, right? If it's truly running the whole thing.
Most businesses aren't simple. They are global congregations of rules, compliance laws, staff in different places, human inconsistency — which is the same way as saying human creativity, by the way — that they're putting it all together to try to deliver products and services for their customers in their industry: healthcare, university, finance, automotive, whatever the industry is. They're trying to compete with some other business. They are still going to have a huge number of humans, I would argue more knowledge workers, more developers in five to ten years time than today, because the ability to do tasks to compete will go up.
The bar for competition will go up in almost all these industries. You will still have a huge amount of things to go do. Most of the things that we think are simple get done for you. That is the history of technology, right? I'm a big fan of saying AI is just technology. What we can do with spreadsheets, what we can do with the internet, what we can do with ordering goods online, right? You're like, ah, once Amazon arrives, we'll all just never have to go to the shops again. Like, it doesn't happen, right?
If you think about — I heard someone talking about bank branches as an analogy, which I thought was a really good one. Around 2000 when mobile banking came along, everyone thought bank branches were dead. Bank branches started closing down. Everyone wasn't going to go to their branch anymore. It was just, you know, it was a pretty negative place. At least it was in Australia. I'm pretty sure it was in America as well.
If you look over the last 10 years, bank branches are increasing. And people are like, 'Huh, this is a narrative violation. Why is this?' And the answer is bank branches did close down. People did go to internet banking. They did use their mobile app. They don't go into the branch anymore with a passbook to say, 'I'd like to send $100 to Neili, please. Here's his details and write them all down.' But bank branches have adapted. The reason they're growing is there is a huge customer service element to a bank branch.
The services they provide today are totally different than what they provided 25 years ago due to the technology of the bank and the availability of things. They are much more about helping you with higher level processes and services than they ever have been. And that turns out to be presumably profitable for the banks, which is why they're opening new branches. So bank branches are going up. The things those branches are doing are totally different to what they were 25 years ago. I think you'll see the same thing with knowledge workers and everything else — that it will still be incredibly important. I don't see that part changing.
One of the reasons I'm asking all these kind of foundational existential questions is to understand how you feel about Atlassian's relationship to businesses and how businesses might change as we add new technology. And I agree with your general framing, right, that automation technology arriving to a business is a pretty familiar phenomenon. But there's something else going on with AI that is either making that faster or more dramatic, and it's obviously happening to your business as well. You're the CEO of Atlassian and you've made some changes around AI. So I want to ask you the Decoder questions now. How many people is Atlassian today and how is it structured?
We are, I think publicly, 12, 13,000-odd around the world. We are structured globally, I would say. So we're team anywhere. Employees have the choice to come into an office or not. About 60% of people come in three days a week or more. 25% of people don't come in a single day per week. We run as a globally distributed company as we have done since the start. When you start in Australia and San Francisco, you kind of get used to the Pacific Ocean being a little bit of a distribution challenge. This is before Zoom. We used to have these giant Polycom systems in meeting rooms. Good, we don't have those anymore. Technological progress is nice.
We have customers all around the world. We have staff all around the world. We are structured functionally, I guess you would say, with a lot of matrices internally. I don't know if that's the answer you're looking for.
No, that's absolutely the answer I'm looking for. What do you mean by functionally with a lot of matrices?
So we have a CRO, we have a CFO, we have two CTOs at the moment. We have two large product groups, each of which has a CTO and a chief product officer. One in the enterprise and emerging side and one in the future of teamwork side. Our products are organized into collections. So we have collections of apps that are all built on a single platform. Well over half of our R&D is on the platform. The apps are increasingly a smaller amount of the proportional total investment in building. More and more is on the singular Atlassian platform.
Right, customers hire us as a platform across their business to get work done, not for a single application. That requires a matrix internally, right? We don't have salespeople per product if you want to think about it that way. We have to have people who — the sales, customer success, the FDEs, they're all customer-related motions. So they're organized as per the customers in their geographies, in their size and scale. And then we obviously have to have designers, product managers on particular products who care about the purpose of a product. So you end up instantly with a matrix between how the customers are organized and how the products are organized.
And then we have a giant platform. So we have the technical platform that runs across all the products. So a very large platform team. We then have, you know, finance and talent and other things that are also broad processes. So naturally you end up with collaborative areas that you have to get together and we manage that through our operating processes.
Do you use the Atlassian platform to run Atlassian?
We do. We run entirely on the Atlassian platform. I used to say we're the number one user, we're not anymore. Of our strategy and operations products to understand what our business is focusing on, what our large scale goals are, where the people, the investments, the technical systems — how they all come together and what is going right and wrong across all of those. So, we are a big believer in our strategy collection to operate a large scale business.
One of my favorite questions to ask enterprise software CEOs is how much they personally use the enterprise software. How much do you personally use your products?
What's your number one feature request for your teams?
Look, our number one — my number one feature request is usually continual UI congruence. I guess that's the category that people would put it in. I'm a bit of a stickler for design and consistency. We want to achieve eventual consistency in design, which we've come a huge distance in the last five years and the last two years we've been truly world class at doing it. That's usually my biggest area where I use a lot of our products on a regular basis and as such are moving around the platform and you're looking for ways where this thing over here happens this way. Hey, they do it better over there and so we're trying to achieve that eventual consistency across a large product surface area.
I use Jira. A web browser is — like I think it's like 96, 97% of Atlassian now uses Jira on a regular basis. I mean it's not on a daily daily basis. So I literally spend many, many, many hours of the day — we are building a web browser for ourselves because we've reached the point that we're like, man, this thing should just be better for knowledge workers. So we've built already, I would argue, the best browser for knowledge workers in the world and it's getting much, much, much better. Keep watching this space. So yes, I would say there's no 24-hour period because it goes by where I don't use one of my products. I'm not sure there's — I doubt there's a six-hour period that goes by where I don't use one of our products at the moment.
I want to talk about Dia in a second because I'm very curious about that acquisition. Obviously we covered the Browser Company very deeply at the Verge. That was a big acquisition for you guys. Setting that aside for just one second. One of the things I'm particularly curious about here is the distinction between the platform and the products that run in the platform. And again, maybe this is just reductive, but it sounds like there's a big layer of capability and the products are expressions of those capabilities in different ways, but they're all running on the same core platform. Is that how you think about it?
Yes, absolutely. We have to build a singular R&D platform — whether that's how we talk to AI gateways, whether that's how chat works, whether that's how automation and identity and logging and governance and compliance and content classification, whether that's how our home, our search engine work — there's many, many more technical capabilities that have to be shared. Then you have all of the next layer up which is — and customers want that to work the same, doesn't matter if I'm in Confluence or Jira or Loom or the service collection, automation shouldn't be different per place in the world.
And that's very expensive. It's very hard. It takes a lot of time but it delivers a huge amount of customer value and loyalty. Most vendors don't try to do that. They literally don't try. Then you have a layer up which is important, which is the consistency of operation and how operations processes can be mixed across applications. So if I open a Confluence page inside of Jira, I want that to feel like a Confluence page, but I want it to be inside of Jira. I don't want to leave Jira to open it if I just want to read it, close it, keep going. So there's a lot of UI layer sort of recomposability if you want to think about it that way.
And then lastly, each application has to do its job. If you blend enterprise software together, you end up in a world of hell. People have tried this many times. You want a tool to do a job. You want to pick up a screwdriver or a hammer or saw and feel like that thing's going to do the thing you hired it to do. The interface is designed to help you do that task. And so that's where there is a difference in the interface layer, the interaction layer between those tools. And the combination of those is where a lot of our design challenges lie.
For example, Rovo is our chat layer — is amazing, right? I would argue it's one of the best, if not very close to the best, enterprise chat products in the world. Our customers continually say that. How come you're better than insert famous brand here? There's a lot of reasons why we perform better in evaluations and testing with Rovo. It's not because we make a foundational LLM, I think, which is the floor of thinking. However, using Rovo inside a product — if you're going to Rovo for the sake of Rovo, awesome. You have a question to ask, you want to search across everything, fine. You go to a particular interface.
If you're inside of Loom or Jira or the service collection, you just want to open up chat, ask a question. You want it to understand the context you're in, but you want that operation to be consistent across the platform. That is what we're striving for to deliver to customers.
One of my constant tropes on the show is that if you describe to me your org chart, I can tell you 80% of your problems, right? The structure of the company leads to some kind of natural politics in some kind of way. The tension between we have products that are designed to do a job and we have a platform that contains the core capabilities is pretty well known, right? It's maybe your product teams are going to desire platform capabilities that don't exist yet or they will desire different platform capabilities from one another. How do you break those ties? How do you make those calls?
Look, we have a number of ways that we are different and we try to do that. First I would say most importantly, we have a platform-centric CEO and co-founder who thinks in terms of platforms. Thinking in Systems is my favorite book — man, I have a copy on every desk. It's the job — you have to put the platform first collectively and then you go solve a bunch of problems. That does not mean genericism wins, but it does mean when you make trade-offs you have to spend the extra effort and time to make it platform-centric, to make it work. And that is a forever task, right?
So it helps to have a CEO — if you think about the referee across all the functions — that's platform-centric. They know where I'm going to land and so we're going to end up there. Secondly, we have design as a very, very senior role. We have a massive investment in design. Our chief design officer sits — reports to me, sits on our executive team. We have three, four, five now chief design officers in the business from other very large businesses, some larger than us. We have a huge investment in design and experience and I think especially in the AI era that's getting ever more important actually.
Much as Claude Code or something would tell you the opposite, I don't think so. I actually think design and the experience of software — if the cost of building goes down, the quality and differentiation is on the experience and the design, and that's not something that's easy to do. That requires a huge amount of taste and judgment. Lastly, I think we aren't afraid to put things together in unusual ways in the organization where we need to solve problems.
So the latest example of that probably is our internal IT and engineering function now reports to our chief people officer and I've combined those two roles, which is unusual — like why is HR running IT? That doesn't make any sense. It actually does I would argue at the moment because of the things we need to do to transform our business internally continually. Which is that if I look at AI and becoming more AI native as a business, I ended up with two piles of projects. A pile of projects that are related to talent.
How I need to change and grow the people we have, get them using these technologies, get them thinking in certain ways. I have a huge talent problem. Hiring, growing, training, changing people is very difficult to be competitive. And I have a huge system problem. I need to change our internal systems to have MCP servers. I need to make sure that everything is operable. I need to make sure that we have all of these system changes so that AI rolls out. How are we managing our token spending? You know, there's a huge amount of system change.
And so I end up referring a bunch of — is this a talent problem or a system problem? And I said, that doesn't make any sense. Usually the answer is this one's 60% talent, this one's 40% system, and this one's the opposite way around. So I've put them all together inside the business so that our system changes to apply AI internally and our talent changes to apply AI internally are in the same function right now.
Which is a little odd, right? The people who deliver the laptops and the people who manage compensation work for the same group, but it actually makes logical sense based on what we need to change about our business at the moment, right? What is this sort of three-year epic that needs to change? So I do think we organize around the problems we need to solve and the goals we have as an organization when we need to.
This is a brilliant segue because I wanted to ask about this executive. You have, I believe, the formal title of your HR person is now Chief People and AI Enablement Officer.
That's quite a title. And the point you're making about the systems of the business needing to change leading to some change in your talent mix is coming true, right? In March, you laid off about 10% of the company. I watched the video you made in Loom about that. And your point there was you're not necessarily replacing people with AI, but you had identified a change in the mix of skills that the company needed and you were going to make these changes proactively.
Walk me through this. What made you say, okay, AI is here sufficiently that the mix of skills is different and what changes did you make to your systems to make losing 10% of your people effective or worthwhile?
The environment we've lived in in technology for a while is continual hiring and growth. The markets have changed on their view on that, right? So you have to say that because one of the ways that we've managed changing businesses over time has been hiring people in different areas, right? If you have red dots and blue dots and you say, 'Well, we need more green dots,' you just say, 'Well, we're going to hire all green dots for a while until we have a blend.' And you kind of you manage it that way. That is no longer a feasible path for us.
Secondly, we have certain areas of the business, right? Our AI —
Wait, actually, can I just ask one question about that? You're kind of describing the COVID era over-hiring that every tech company was doing, and you're saying that's kind of over.
I don't — yeah, I think people hire at the rate of the best decisions they know at the given time and what they are encouraged to do by others. Hopefully they don't look at everybody around them, but I know that a lot of CEOs do look at the people around them and be like, I should do that. I always think those are poor choices. But over-hiring, I don't know — pre-COVID, we were a few thousand people, you know what I mean? So we're four times — I don't know, three times the size we were pre-COVID, probably.
Our business is, you know, again, we're accelerating as a business, right? We grew 30-odd percent last quarter on a 7 billion run rate, which is the fastest we've grown in about two years. So.
I think I'm just trying to understand the 'you can't just hire a bunch of green dots' comment a little more clearly because I've heard from many of your peers, 'Oh yeah, during that period we just hired everybody that we could.' And it sounds like that's not specifically what you were experiencing, but it rhymes in some way.
It does rhyme, but I think there are definitely periods where that is the way to go, right? That's not the current three, four-year period of time, right? So that's the first thing I say because then you need to work out how do we change our skill mix. So we have a huge amount of internal programs to try to do that. We also have certain areas — our AI products and services specifically and our enterprise sales areas are growing very, very fast. They're doing well and we want to invest further in those areas, right?
And so when you have certain areas you need to invest further in, you run into very difficult choices where you're like, okay, I need to invest further in these two areas, I can't grow the overall in a significant way, I have to manage how to transition the organization between that, right? Which leads to a lot of very difficult choices. We are still hiring really hard in those areas, right, in order to change the skill mix, but you also have a question of how fast we need to do that.
It doesn't take away from any internal training programs — I say that in air quotes — in terms of how AI is adopted and used. It's more around learn, play, share, you know, mutual learning. It's less like an L&D in terms of if I'm going to sit down and watch a class on how to do this. But that doesn't take away from that. It's a combination of all these factors as we've massively changed the business to be, you know, truly world-leading at AI product development in the last two years.
There's no doubt that's been a rapid journey and we're trying to move as fast as we can to get there and to stay ahead.
