The SaaSpocalypse that wasn't, with Atlassian's CEO

22:03And 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 live. For example, Rovo as our chat layer is amazing, right?

22:37I 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.

23:13You want it to understand the context you're in, but you want that operation to be consistent. across the platform. That's what we're striving for to deliver to customers.

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24:15Nilay Patel:See how 11 agents can fit into your workflows and help build experiences that your customers will actually love. 11labs.io slash decoder. E-L-E-V-E-N-L-A-B-S dot I-O slash decoder. Support for the show comes from CrowdStrike. AI is already fundamentally changing how companies build products and run their businesses. If you listen to the show, you probably already know this. But while AI drives innovation, every new model and agent also creates new cybersecurity vulnerabilities. That's why CrowdStrike built AI Detection and Response, or AIDR. AIDR helps organizations discover where AI is being used, monitors AI activity at runtime, and helps teams detect, investigate, and respond to threats involving AI systems.

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26:48Nilay Patel:One of my constant tropes on the show is that if you describe between 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? 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.

27:23First, I would say most important, 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 of 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.

28:01They 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 Design 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.

28:41And 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.

29:18A 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? 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?

29:54And I say, 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?

30:31So I do think we organize around the problems we need to solve and the goals we have as an organization when we need to.

30:37Nilay Patel: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. Yes. 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.

31:14Nilay Patel: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 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 manage it that way.

31:52That is no longer a feasible path, firstly. Secondly, we have certain areas of the business, right? Our AI groups and areas.

32:02Nilay Patel:Wait, actually, can I just ask one question about that? You're kind of describing the COVID era overhiring that every tech company are 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're encouraged to do by others. hopefully that 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.

32:37Our businesses, 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 customers are opting for more acceleration.

32:52Nilay Patel: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. 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.

33:27We 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. 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.

34:07It doesn't, I always say, take away from any internal training programs. I say that in sort of 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, I think, 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 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.

34:45Nilay Patel:One of the things I've heard from a lot of CEOs in your position is, oh, the value of our senior people has increased, right? There are more senior software engineer jobless things out in the market right now because that, as you've said, that judgment and that taste is very important and maybe the tools can just build that stuff for you. I'm curious for your view on design, right? The idea that all of our designers will not be enabled to just ship code is very tempting to a lot of people. And then there's a third view that says product managers, roles do everything. And all these roles are going to come crashing into each other.

35:14Nilay Patel:So what mix of skills specifically did you identify? And how are you prioritizing in this sort of classic triumvirate as more and more people get the ability to do all the other jobs? I think it's very easy for people to have very binary takes on these things, right? I find it amusing that if you put an engineer, a designer, 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. Look, we have a lot more product managers and designers who are writing code from prototypes to actually shipping code than we ever have before, that is for sure.

35:57We have a lot more marketers and finance people and HR people who are doing the same thing, by the way. The code that they are writing and shipping builds on two things. One, a platform that has the engineering robustness to be able to scale and deliver to customers a European compliant data resident solution that is necessary, the legal constraints of country X, et cetera. So you have to build a technical environment that that is able to get out to a customer with the required degree of customer safety. That is a non-trivial problem. That's not something you're just going to vibe code. If I'm vibe coding my own solution to something for my household, fine.

36:37It can just work well enough. I'll go fix it if it doesn't. If I'm doing that for a large organization, I need some other controls and rules around it. Secondly, I think the specialization of those areas all doesn't go away. The human judgment required in different areas It's like the edges of the roles are blending. They're able to do a little bit more of someone else's job, right? I have a lot of designers who are able to ship prototypes that are working code prototypes instead of a picture or a fake prototype in terms of a mock-up. That's great. I still see them sitting in Figma a lot and doing that.

37:15I have a lot of engineers who are sitting in Cursor or Claude Code or whatever on a daily basis, writing features and building things, and they're able to do more design than they were before. That doesn't replace the need for the other job. It increases the quality of the overall software. It increases the speed of collaboration between people, if you want to think about it. If they each understand each other's role, they can have a much more fulsome conversation more quickly. The specialized nature of what they do goes up. It becomes more spiky, I think, which is really, really important. The ability of building platforms is going to get overused here becomes more important.

37:55So just this week, for example, we shipped, one of our designers wrote an interesting blog post about, we now have an MCP and a CLI for our design system internally. We have a award-winning design system, Atlassian.design. It's very public. We publish all of our design system. I've always said the role of design, 50 % of their job is to make our design system and to keep it being scalable, flexible, and buildable. 50 % of their job is to solve the hardest 5 % of problems in design because the half where they're building the platform, what used to be templates and design documents that are now CLIs and MCPs to do design internally in the way that we want design to work so it's consistent and coherent, that is a really hard problem to build that.

38:40However, they're unleashing thousands, if not almost tens of thousands of engineers to build better designed things when they do that, right? Their leverage is a lot higher in building a design system than it is in solving, help me with this screen, help me with this screen. And it's the same in security. It's the same in customer support and customer success, actually, right? If you think of bug fixing as the platform on which you reduce customer service, more and more of that role is required on building those scalable templates, platforms, ways that the other functions can build on top of. So they can do that thing, right?

39:16If a product manager is vibe coding a feature, as in they're just stabbing around some sort of code generating area and it's an area where it's not infrastructural, it doesn't need to scale, it's, you know, fine. It should leverage our entire design system. Turns out out of the box, that's not very workable. So we've just shipped MCPs and CLIs internally, which are like on V4, that are amazing at understanding our design system. So the designers can evolve that system And the features that are being built will continue to match what customers expect in their congruence and consistency. That gets us huge leverage overall.

39:51But it's a non-trivial thing. It doesn't mean the designers are going to go away, the engineers are going to go away, the product engineers are going to go away. I don't think any of those roles disappear.

40:00Nilay Patel:I'm always joking that Decoder is fundamentally a show about org charts, which means I think I know what the next five years of the show is going to be about because we're on the cusp of some of the weirdest org charts in history in AI. True. Matthew Prince from Cloudflare was just on the show. We talked a lot about his Wall Street Journal op-ed where he said, I'm going to fire all the measurers at the company because I can replace them with AI. They can look at all of my systems and do all the auditing for me. And we're going to have fewer middle managers because what they're really doing is measuring and auditing things, and AI can do that.

40:30Nilay Patel: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. Where are you? Are you in classic org chart? Are you at wild ideas? How is this coming together for you? I'm certainly more measured than that. And I think the reality of how the world will be. I think it's easy to point to extremities. Is somewhere and somewhere a manager with 50 people? Sure. Are they effective? Maybe. People always talk about Jensen. He's obviously very effective. He's a good dude. He runs things in a very weird way.

41:04Is that scalable and repeatable? I'm not sure it's scalable and repeatable. I'm not sure if you took an entire set of large companies and made them all work that way, they wouldn't have worse results as a collective. Part of business is scalability and consistency and able to be understood and managed that doesn't require uniquely talented individuals to understand a very odd structure as they move around a business. I suspect you are going to get more role blending, people able to handle multiple types of skills. I suspect they're going to be more sort of generalists. The edges of the role will blur into the roles around it.

41:44A product manager is able to do a bit more marketing and a bit more engineering, right? And I think that applies almost all across the way, right? Finance people are probably able to do a bit more legal than they were before. They're able to understand the legal frameworks we have internally with agents. and that's great, right? I don't think that changes the roles themselves. I think it may make teams a bit broader. I do agree with Zuck's general direction that organizations will become a broader triangle, a fatter, wider triangle, right? The companies will get wider and hence less deep. I think broadly that's a true direction.

42:19Do you go to 50-person teams? Then a span of control is like three people. Like, I mean, the organizational depth is like three or something. If you're multiplying by 50 at each level, you have 2 ,500 people with two levels of reports. I would question that. So that's probably not an average. I don't know about this term measurers. I don't know that anybody would sit around in a knowledge work job and say, my role, my purpose is to measure things. Right? I just walk around with a ruler and I'm like, oh, that's a foot. I know there are a lot of jobs where that becomes. I know what Matthew's saying.

42:56that becomes the effective output of a role. We're measuring it for a purpose, I would believe. And we are going to work out how to make that more scalable. My probably way of looking at it is a little bit different. I think there are two types of jobs. If you want to simplify an organization, there are supply constrained and there are demand constrained jobs inside a business. So input bound and output bound. Well, what is the constraint on a job? So look at a marketing or a technology function, a creative function, versus a legal or a customer service function. Legal and customer service are input bound.

43:36If I deploy out to AI, I don't suddenly get a lot more contracts, right? Like the number of contracts coming in, if I'm looking at NDAs or leases or trying to understand the company's legal position in certain areas, I can process potentially that work faster with AI and through systems, you know, I can be more efficient in how I do that. That is one set of roles that you can look at almost everything in a business process and say that's input bound. I'm going to manage that differently than things that are output bound. If I think about marketing or engineering, unless I run out of roadmap items, which I've never heard any engineering team in my history of 25 years of doing this, where they're like, actually, we've reached the end of the roadmap and we'd like some more ideas of what to do next, please.

44:17right said no one building software ever that has a large customer base their output their creativity is not is bound by their own initiation and their ability to organize groups of people and processes and everything else which is what the elastin platforms do that is their limiting factor it is not the human ability to create ideas and so it may be that you have less input bound functions and areas as a proportion of your total employee base and you have more creative output bound functions, you know, how much is this human ingenuity and creativity and imagination, right? A possibility. I think that's likely the way things shift and trend over time.

45:03I think that's going to take much longer than people think. We're big in technology. Next year, everything will be like this and it takes 10 years to happen, but it will happen. I think I'm very fond of this term. I keep meaning to put up a rant online about it. I think we used to talk about information asymmetry, and I'm stealing another friend's quote here, but we used to talk about information asymmetry and how much certain people know things, and that is their source of power or advantage. I do think we're moving to, he said it, and it really hit me like a bolt. We're moving to imagination asymmetry.

45:36We're moving from information asymmetry to imagination asymmetry, where your ability to create and think is going to be far more important as a competitive advantage for your team, your job, your business, than your access to information, your control of information, your understanding of information. That part is going to be cheap. That's what LLMs are really good at. What they're not good at is imagining things, right? They didn't decide to start Atlassian. What business has been started by an LLM? Zero in the world, ever, right? They got made of control to try and start a business.

46:08Nilay Patel:Anthropic has run a failed vending machine for several years now. Come on. But it requires humans to say, you know what we're going to do? Let's fail at running a vending machine today, right? They were probably sitting in front of a vending machine cursing the thing when it didn't drop their candy bar. And they said, hey, I'm going to do this better with AI, which is probably the way that most of those companies think about every single problem, right, as they're staring at a vending machine. It required a human being to say, let's go do this. And it required them to write a bunch of stuff to go create the vending machine operation code and try it.

46:37And, you know, we all learned a lot as a result of this scientific experimentation.

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50:21Nilay Patel:Let me ask you the other decoder question, then I want to apply all of this to what is broadly happening to enterprise software and SaaS. The other decoder question asks, everybody, how do you make decisions? What's your framework for doing that? I would say I make decisions by talking a lot. That's what people internally would truly say. I find the need to discuss things from different angles very important. I like to let other people talk. I like to get all the information together. I do believe there's obviously a time where we need to say we need to stop and just move in a certain direction.

50:56I think the more important part for me than how I make decisions is I'd really like to explain decisions to people. You know, at Atlassian, we have a value. We open company, no bullshit. and I tell people all the time, that does not mean transparency. We don't owe you as an employee all of the details behind every decision. We don't want to, all the sausage making is not useful. It's overwhelming to people. It doesn't help us move fast. We do owe employees clarity on why we are making a decision and an explanation of said decision. And I think that's one of the most important things managers can do for training, for helping their talent grow, is not we're going left, but hey, we thought about it.

51:39We looked at the different options. We considered straight, right, left. Here's the pros and cons. We're going left. This is the reason why we're going left. Often, people don't do that because people don't like the decision, right? And I say that all the time. When you talked about our talent mix changes in March, we have to make a lot of hard decisions to run this business. I want to make sure that we are always explaining those decisions to people in the clearest and simplest way we can, why we've made a decision and what the decision is without any dressing. And I'm really don't like these corporate emails where I read the whole thing.

52:13I'm like, so what are you saying exactly? Like, you've got a lot of words here. I'm like, here's what we're doing. We need to change our skill mix. Here's why you should have the right amount of empathy to explain decisions of, you know, especially hard ones, but you also should not expect people to necessarily like it. Like we make a lot of hard decisions where I'm sure 60 % of the company, 70 % of the company think it's a great decision. 40 % think it's a terrible decision. We build trust as leaders by explaining our decisions and being clear about why we've made it and how we've made it. And hopefully we improve decision-making across, right?

52:48I would say decisions get to my desk when they're presidential decisions. I think it was Obama that called it that, where it's 51-49. That's why I made it to my desk. If it was 90 % certain what we should do, it should not have made it to my desk in the first place. I only get hard decisions. That's a well-operating company. I think any leader should be in that situation. If it's an obvious decision, why did it make it to you? So that then requires more thought and more explanation about those decisions. I think that's probably the most important part than any sort of framework we use to make them.

53:21Nilay Patel:So let me put all of this under some pressure. You've described Atlassian as a platform, a lot of an organization's decision-making and data and capabilities in that platform. What are we going to do next? How are we going to do it? Is it working? The entire point of the SaaSpocalypse was, well, we don't need your beautiful user interface. We don't need your design surfaces or congruence of design across products. All we need is your data. And if I unleash codecs upon your data store, it won't even matter that it's the same platform. You can have 15 different vendors and the AI will create itself some middleware and read and synthesize all the data.

54:03Nilay Patel:And that'll be great. And like actually your business can leap ahead in meaningful ways. I think my favorite example of this from a previous era was I always had Daniel Dines from UiPath on the show. and instead of modernizing your healthcare billing system, he would just build robots that would use Windows 3.1 for you. And this is like a very early version of what I think every AI company is selling right now, which is instead of modernizing your backend and spending all that money on a migration, our robots will just use your horrible old databases and present to you a new user interface. That's one version of this haspocalypse.

54:37Nilay Patel:I see it coming for a lot of businesses. How do you expect to change all of Atlassian against that? Because a lot of what Atlassian sells is upgrade, right? Upgrade the whole platform. Use one product and use all the products. Use the whole platform. Modernize your entire business. And the other answer is, well, it's like$200 a month and you can just token max your way into the future. You're talking about the headless sort of world, I guess. I say that headless is brainless. It is literally the same thing. It is submission in slow motion if you're a software vendor to do such a thing. And I say that very carefully because, again, it's one of these answers that requires nuance.

55:20We have one of the most used MCP servers in the world. We've just shipped a giant upgrade to it, rewritten it from scratch. It is massively used on a daily basis by millions of people, and it's amazing. MCP servers, we've shipped three or four CLIs that are also used incredibly heavily. the ability to have interoperation of tasks is very, very important for customers to get jobs done. And I totally agree with that. However, and people who use our MCP server and our CLI, they actually grow faster. They create more issues in JIRA. They grow their seats faster. We said this publicly on earnings calls, I think it's like at least 5 % faster.

56:00They grow their ARR at twice the rate of customers who don't use our CLI and MCP servers. So the customers using these things are growing their Atlassian spend faster than they have before. So that world I totally believe in. When I hear the word headless, though, I think that is the only world that we're going to exist in. Like we just sell an MCP server. That is the brainless part about it. Because then your software really has no value. If you truly just have a database, fine. If your interface sucks, and I think a lot of enterprise software has horrible, horrible, horrible interfaces.

56:34Nilay Patel:This is why I ask all the enterprise CEOs if they use their own products. It's really just how bad does your interface suck? Yeah. Well, I'm a big fan of recording the Loom and being like, guys, this doesn't make any sense. And I believe if you do that, that yak shaving over and over again, you end up with amazing interfaces. And that's why tens of millions of people wake up to our software. The investment in design and experience that I talked about is the antithesis of that. I actually think the world is very different than those views. We'll see in a few years time who's right. The way the Atlassian platform works is not to be a single platform.

57:02I am probably the only CEO that goes into customers, massive large-scale customers, and says, do not centralize everything on my platform. I do not believe that is a viable strategy for a CIO. I don't think it is. Our platform is highly connected. It always has been. One of the things that Jira, Confluence, Trello, the service collection do is they interact with many, many, many, many other applications. You look at any work item in Jira that's been going for a while, it is full of links. links to Workday and Salesforce and ServiceNow and Figma, links to source code, links to Google Drive and Teams.

57:38Links are what build our context graph, right? The reason we have the best context graph in the world is because we have a lot of links to start with. And we have had a 10-year history of having to understand how to be collaborative with other applications, right? Our applications are by nature, highly connected to the rest of your IT world, the rest of your technology world. We don't want to be a control tower. I think it's a stupid analogy. I think every CIO has been told, just trust us, we'll deal with all the problems. It's never worked. I don't think it ever will. The reality of technology is you're going to have more -

58:08Nilay Patel: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? You have now a powerful, intelligent orchestration layer that will finally realize the stream. Three parts snake oil, two parts bullshit. I think the reality is it will help all the applications if built well to work better with each other. There's no doubt, like look at Figma and Atlassian. We have a deep partnership. We use each other's MCP servers a lot and we can build better solutions for marketing teams and designers and technology teams between the two platforms than we ever have been able to before because we can understand each other's data in ways that we never have been able to before and we can solve workflows on different ends.

58:52If you're sitting in a deep design, you're sitting in Figma. It's a visual experience. If you need that to be tied to work and then understand how to find that, that moves back into our world and we want to make those journeys as seamless as possible. We want to be not a control tower. We want to be a subway station that is highly connected. We want to be Union Square. We want to be Town Square. We want to have a lot of lines going through us and we want to make that experience for our customers the best that they can. So I think it's 98 % was our stat on the earnings call. More than 98 % of people using our MCP server also use the user interface.

59:30And that is not changing, right? The ability to do work faster doesn't mean I just stop using it. If you invest and have a good interface and experience and you're solving high-level problems. If you look at the features we're shipping around, agentic uses, right? So how do these harnesses, Claude, Codex, and everything else work inside the Atlassian world and vice versa? Now, one world is I open up Claude. I say, hey, go work on this work item. It uses our MCP server to go and get the work item. It uses our teamwork graph to go and get all the context around the work item that is necessary and related.

1:00:06And that gives it the best chance of doing that job the cheapest possible. That is certainly a way that people are going to work. And Claude's going to move the work item along to the next stage. Awesome. There are a lot of other people who start in Jira and will say, hey, send this work item to Claude. And then on the board, it tells them the work item's running and going. They're in an interface that is built for the task, back to the saw and hammer and sort of screwdriver analogy. It's built for the task they're doing. It needs to understand, hey, Claude needs your attention. Inside of Jira, you can open Claude, say you need to send it off.

1:00:38Most people don't use Claude. Most people don't use Codex. Every customer I go to who has a whole bunch of harnesses running. They're going to have internal ones they're built. They're going to have, you know, two, three, four vendors. Most people aren't making a choice. They're all amazing. They're all leaping over each other in progress. Our job is to bring all that together into a coordinated surface, to understand your agentic sessions, your human sessions, your work, to connect all of your enterprise applications into a giant graph, and to give you phenomenal answers across the whole thing.

1:01:10That requires a lot of interface investment. That is hard. That takes time. That takes great design and experience, folk. That takes taste-oriented product managers and engineers. That is a non-trivial challenge. And I do think a lot of people are avoiding that challenge. And I don't think it'll end well for them. I don't think the world is going to live in a CLI as an end tool. I think... You don't think the chatbot is the final user interface? I don't think chatbot is the final answer, no. I think we learned this in 1970 with the CLI. we don't know. We're all learning what the future interface is.

1:01:48It doesn't mean that chat's not going to be a part of it. It's just, where does this sort of world work? You know, you talked about Dia. Dia is a good example where we're trying to build an AI-driven experience for knowledge workers that is based in the world they understand and still magical and different, not based in taking them out of the world they understand to a chat-based interface.

1:02:13Nilay Patel:So Dia is the browser. You acquired a company called The Browser Company, I think, for a little over$600 million. That was a year ago. We spent a lot of time with The Browser Company when they were working on their products. And at first, they were building a consumer product. And then the notion that what they were building was more like an operating system came into focus as we covered Dia in their product strategy. And most of those conversations and most of that coverage, most of which was done by my colleague David Pierce, was happening simultaneously with Google search traffic dropping, right?

1:02:47Nilay Patel:And the very notion of the browser as an information document viewer was changing in big ways. If you think the homepage of Chrome opens to Google and that's going to take you to a bunch of web pages, well, actually, Google has a lot of ideas in between there that is changing traffic on the web. And yet at the same time, the web is an application environment, is a thing that can make AI agents vastly more capable because the applications all live somewhere that they can go address them. It was just obviously explosive at that time. You and I are talking, it's like a week or two into meta launching Muse.

1:03:21Nilay Patel:And the power of Muse is that Mark Zuckerberg has given everybody 8 gigs of RAM and 8 gigs of storage in the cloud with a web browser. And now you have an agent that can just go use a web browser for you. And this is very powerful, and Amazon is furious about it and has blocked this web browser. Do you have a dia because you need to control the application layer, or is it something bigger than that? I think two things are important to understand. When you get technological disruption, one thing that happens is we all move up the interface stack a level. Not a lot of people are competing about DOS or operating systems anymore.

1:03:54in the modern internet world, one way to move up the interface stack is to move towards the browser. Features that were inside of applications moved to being inside of the browser as we increasingly learn and operate that way. I think second, the biggest thing that's changed, and usually people's habits change is what happens. Knowledge workers, yourself, myself, everybody, we spend 80 % of our lives in a browser. but we're not reading articles all the time. We are sometimes. We're not reading webpages. We're using applications. We're using Gmail and Google Calendar where we have Slack open. We're using the Atlassian platform, all of our applications and Salesforce and we're watching videos like Loom and we're interacting with those videos back and forth.

1:04:47That set of tabs is no longer a set of webpages. It is a set of documents and applications that have massive power underneath them. We're trying to rebuild the web browser in a totally new form for knowledge workers who live in applications, who have their browser open 80%, 90 % of the day as their front surface area and want a better way of managing those applications and getting work done across those applications. The browser has some incredible power to be able to do that. It requires us to understand SaaS apps very deeply, which we do. It requires us to understand enterprise security very deeply, which we do.

1:05:25It requires us to not chase the consumer audience. Chrome, Safari are built the way they are because they have to cater from five-year-olds to 100-year-olds who are doing less work-related knowledge tasks and are doing more browsing and entertainment and other tasks. That doesn't mean that someone who's a knowledge worker in a corporate job isn't looking at YouTube or Facebook sometimes, but it's not the purpose of their job. So we believe we can build a better browsing experience, a better work experience, right? One of the pitches we had for the product is we're not building a browser. We're building a doer, right?

1:06:05It's a totally different thing. We're helping you do things. We're not helping you browse passively. And hence why tabs and split screens and pin tabs and groups and AI and dynamic tab groups, live tab groups as we have of the documents you have is a great example, right? Our documents live tab group India shows you the documents. It will automatically open and close tabs if you want to think about it that way for you across any content application. Google Drive, Confluence, SharePoint, any application you use that has documents. If someone comments or shares a document with you, the tab automatically gets opened by your web browser.

1:06:43And if you're done with it somewhere else, it automatically gets closed by your web browser. Because that is a set of documents I'm currently working on. Same for pull requests or anything like this. Then we add AI over the top. AI lets us change how we use these knowledge applications in insane ways. The browser has your entire history of what you've done. It has skills. It has an ability to run AI. You can use Dia to make Minesweeper. And you can use Dia to make Minesweeper out of the tasks that you did in the last week. And it'll go start a computer underneath the browser. It'll run a bunch of code generation.

1:07:21It'll create an application for you that plays Minesweeper based on the tasks you have completed in the last week. Now, I don't think that's a very useful application. But the fact that it can do it is amazing. When you apply that to what you can do. So the most popular feature of DIA is the morning brief. People are blown away and often suspicious about how we can understand what you need to do today with a fidelity and a design quality and an experience that makes it a joyful experience when it pops up every morning. Mine opened about 45 minutes ago. That is a design challenge. that is an amazing team of designers that are thinking about our most used application surface area and reimagining it for knowledge workers and for the AI era simultaneously with access to every SaaS application that you have open on your computer in a way that corporate security is going to say, this is a good idea and I trust it.

1:08:13And that is the mission we embarked on a year ago. The browser company embarked on three years ago. And it's usable today. It's coming out of Windows any day now. It's in beta on Windows. GA is very soon. And it's used on the Mac by a huge number of people on a daily basis and only increasing.

1:08:31Nilay Patel:Let me just push you a little bit on moving up the stack to the next level of user interface. You could argue that already happened with web browsers, right? Windows was disrupted by the very presence of the web browser. The iMac was only successful because the most interesting thing you could do with a computer at that time was go on the web. And so the presence of the web browser on the Mac allowed a bunch of applications to move away from Windows binaries to being web apps and then cloud apps and now productivity apps in the cloud. The web as an application environment is probably more vital and important than ever before, right?

1:09:06Nilay Patel:It is the thing you're describing. It's where all the applications live. And a custom web browser designed to do work is very important. At the same time, I would just say, like, didn't that already happen? Are you actually moving up the stack or are you just building a more custom web browser? Or is AI changing that in some other way? AI is changing that. AI is giving us the ability to help you understand what work you have to do, not just your browsing history, right? One of the most powerful things about AI that I think people don't broadly understand is an LLM's ability to generate content is kind of well-known.

1:09:41I can say, tell me a dad joke and it spits one out, right? It streams out tokens. What is more important, I think, for application vendors like us, people who are trying to make tools and products and solutions for customers, is the ability to read text. Not write it, but read it. Because we can understand meaning for the first time ever. We can understand what a page or a comment or a piece of text is about. Then we can make application-level choices, which we never could do beforehand. Word didn't know what document you were writing. When it said, it looks like you're writing a letter, it was like really shithouse and it was a terrible experience.

1:10:18Now it can actually tell with super high fidelity, are you writing a letter, a PhD? Is it a high school essay or a university essay? And it can adapt and change its interface. The ability of DIA and AI is to understand what it is that you are browsing, what it is that you are doing, to be able to reach into applications to get content and to pull it back to give you tools to manage your day. So the morning brief looks across your calendar and your messaging tools and your document tools to tell you what it is you have today in a very intelligent manner, the way it would be if you gave me all your content and said, spend the days.

1:11:00Now it reads millions of words in order to generate that. And it has a lot of smarts to give you a very simple experience of the most important things you need to do today that are totally different from anybody else on the planet. That requires a lot of AI to do, and it requires connections to all of your SaaS apps. The browser's logged into all those apps, so it can do that, right? And it happens locally. It doesn't happen in the cloud. It happens on your machine. That is something that we could never do before to understand, hey, of these 10 documents you looked at yesterday, here's the one you need to do today because I know in your calendar, you got to do this tomorrow.

1:11:38you got to do these things, right? This is the most important thing for us to do today. And let me help you get started on that task. Let me draft some notes for you. Let me help you with that document. Let me help you do things. That is something that was never, ever possible beforehand. Lastly, it understands the people you interact with. People are left out of so many of these conversations and equations. You can think of Deer as having a mini CRM that is constantly updated based on your browsing habits. It's a weird analogy, but it's effectively what it's doing. one of the many things it's doing inside the browser, which is why its answers are so good.

1:12:11It knows that you and I don't know each other very well. It knows I know my head of engineering extremely well. So if that person asks me for something, because it's maintaining a constant context graph of people relationships across every single one of your SaaS applications into duplicating and normalizing and using AI, all of that helps to understand what tasks are actually important to me, right? But it can look at LinkedIn and see who you are and say, man, that interview prep you've got to do today, you should put that up your stack. Neil is an important person. It does all of that for you.

1:12:46Nilay Patel:Can you put that in the Dia system prompt so people know when they come to talk to me? Just hard code that in there. We'll see if we know some people. It's hard. And the experience, none of that should need to be known by somebody using Dia. The experience should just be a beautiful, joyful way to start your day. We want to leave you with more sense of calm, control, comfort about your day, not stress about AI, right? Leave that to us to do really well. And we just give you the answers that you need from your tools and applications. It also does not mean you use less of those tools and applications, right?

1:13:24I don't use Dia and stop using Slack. I don't use Dia and stop using Confluence. it's helping me understand which thing do I need to do next by understanding what's inside those things right by cross-referencing with my LinkedIn and my Salesforce and my Microsoft Teams conversations and meeting transcripts but it's doing that all for me in the morning and during the day to be able to say here's what you have to do next here's the most important thing and providing an interface to allow me to organize and group those tasks in ways that but hopefully bordering on joyful. And I say that they're beautiful and simple experiences, right?

1:14:01And there's so many lovely touches in Deer that just make it a joy to use every day. So we're pushing that mission really hard.

1:14:10Nilay Patel:It seems very obvious to me that you have a very clear view of how AI should work in the enterprise and how work should get done using your tools, your tools enhanced by AI, your tools expressed in your browser. There's focus there, and I appreciate that. On the other side of the house, on the consumer side, my argument has been, well, the reason people hate AI and they're mad about it is a little bit because the doomers have said there's a 10 % chance we're all going to die and all of our jobs are going away. But there's also no great products, right? There's not as much coherence and focus as I'm hearing from you in the enterprise context.

1:14:47Nilay Patel:Do you think that there's some great consumer AI product in the offing that has the kind of focus that you have in enterprise? Because that seems like the missing piece for me. Yes. If you look at the history of technological disruptions, the products that came through the disruption often aren't linearly related to the disruption. If you think about the smartphone era, and we all pick up our smartphones 100 times a day, don't tell me you don't. When the iPhone came out, we were all excited by little bits and pieces. We did not think, man, that camera thing is going to lead to this Instagram phenomenon.

1:15:21We did not think the GPS is going to lead to Uber and so I can take transportation, right? The products that came as a result of the technology shift, the iPhone is an amazing business, don't get me wrong, but the services and changes to life didn't come from the iPhone. They came from the apps that it enabled, like Uber and Instagram and whatever else you like on the mobile phone. It can be a game, it can be anything. Those take a while for people to play with the form factor to understand the bits, right? Uber didn't come along until the iPhone 6 because it all needed to kind of get better, right?

1:15:56And then someone's like, wait, we can take a car with a phone and it'll just show up. Yeah, we can show you where the car is and we can build enough trust and we can build a whole service layer. So I suspect one part of it is in AI. We need to keep playing and Muse, I know the Muse team pretty well. That's an amazing example of an application. There are many, many more of these things. There's a company that one of my kids loves called Talent, I think. which makes these sort of Hollywood AI agent chatbot things with personality that someone from technology is like, ah, that's just open claw with some skin on it.

1:16:31And kids are like, this is a character that I can interact with, right? Like it's an amazing psychology Hollywood game character. These side of applications we're just starting to see. It's going to be an incredibly exciting five years of those sets of applications, right? And I suspect it takes way longer than we think to understand, get to GPT-6, get to understanding of memory and harnesses and putting all these things together. And it probably won't be open claw. It'll be some version of how an agent surfaces in a certain area of life. So yes, the products are still coming in the consumer space.

1:17:04Secondly, I think the no part of the answer is that AI is not a product. It's a technology. It's a piece of technology. If it just makes everything about your life a bit better, you won't even notice you're using AI. And actually that's the beauty of it. You can use Dia all day long and not realize how much AI it's doing. You're just like, hey, the morning brief thing, it's really good. I look at it every morning. It's one of the most highly engaged features we've ever built. And it just tells you here are the three things I think you need to do today. Here's what your day looks like. Want me to help you prep for this thing?

1:17:37And you're like, yeah, help me prep for that thing. You don't need to know it's AI. We don't tell you it's AI. We're purposefully not trying to hide it. We're not secret, but we're also, you need to have a great experience. You don't care there's an LLM and tokens and gateways and massive amount of technology going on behind the scenes. You just want to know, what are the three things I have to do this morning? And if you build trust in it, that's great. The hard part when you mentioned the data centers and stuff is people want those experiences and they're going to be very joyful and very contented and more calm and happy about the experiences they're getting, they're just not going to be using AI, right?

1:18:15I tell this to people here all the time. Every time you take a photo on the iPhone, there is an insane amount of AI processing going on to make that photo look good. Insane amount. And photos look amazing on my phone compared to what they did 10 years ago and 20 years ago. Do I need to know that? Nope. I want to point at my kid, snap a photo, and I want it to look good. That's great technology. when it disappears. That's what it should be in the enterprise or for consumers. There's no reason people in the enterprise should put up with shitty technology. I think that's consistent, but it's not going to be necessarily knowable that I'm using AI.

1:18:51It's just going to be an amazing product experience that says, DS says, hey, here are the three things you got to do today.

1:18:57Nilay Patel:Even beginning to describe the conversations I've had with the Apple camera team about the use of AI in those photos is a full other hour of the show that we do not have time for. Mike, this was a great conversation. What's next for Atlassian? What should people be looking for? We have a massive set of announcements in, I'm scared to say, a bit over a week, 10 days. We have a biannual conference, our Team 26 EU in Amsterdam. In 10 days' time, announcements in every collection. You will no doubt see lots of AI features that are magical and disappear. You don't need to know that you're using them.

1:19:35DR launches, T-Mail Collection launches, a lot of fantastic Loom features. We haven't talked about Loom, massive in the AI era, so we have so much on Loom's agentic use, some of which has been online. Kudos to the partnership with the Cursor team who built some amazing Cursor and Loom features. So really exciting stuff coming up in 10 days' time.

1:19:54Nilay Patel:Well, we will have to have you back very soon to talk about what happens with enterprise software in just one year's time, because I'm confident it's going to change. Thank you so much for being on Decoder. Thank you for having me. Thanks, man.

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