Atlassian CEO on the SaaS Apocalypse, AI Agents & What Comes Next

Give people a chat box that can do unlimited power and they're like, tell me a dad joke. In the technology world, their underutilized capabilities are so big, it's almost trite now to say the models are far ahead of the value they're delivering.

0:00

The whole history of software from 1960 until 2022 was you would take a filing cabinet and you turn it into a database. The cool thing about everything that's happening in AI land is that the filing cabinet can do work.

0:11

The idea I would vibe, code my own workday and then run it is terrifying. However, there is a great gain we are seeing internally in extensibility of softW using things like 5 coding.

0:22

Everyone has been talking about the SaaS apocalypse. Some people call it the catastrophe. Why is there too much fear about this?

0:33

As I've said, not every SaaS company is going to thrive through the next decade. We're not here to defend all of software.

0:38

Obviously, Percy Pricing built software fortunes for two decades. It felt fair. More users, more money. But beneath the logic were very different kinds of businesses. Some seats were tied to work that AI can now do instead of. Others were just a pricing proxy for headcount. And those companies may actually benefit from AI. The public markets so far haven't reliably told them apart. When the SaaS sell off hit, valuations dropped across the board, regardless of whether a company looked more like Zendesk or Workday. That's the gap worth understanding. Companies that survive the transition face a harder job than adding an AI feature. They have to redesign how humans and software work together, where loops belong, when to interrupt, and how much trust an agent has to earn before it acts. Alex Rampell and I speak with Mike Cannon Brooks, co founder and CEO of Atlassian.

0:43

The whole history of software from 1960 until 2022 was you would take a filing cabinet and you turn it into a database. So the first example of this is a company called Sabre Systems, which was started in 1960 by IBM and American Airlines because it took the reservation system, which literally was stored in like vaults of filing cabinets, manned or womaned by lots and lots of secretaries in like the 1950s and 1940s. Airlines have been around for a long time. And then it put them in an early database back when 10 megabyte hard drive probably cost $100 million. And then that's what happened with electronic health records. And the first one was called mops. It was built by Mass General or the first Siebel Systems predating Salesforce, or actually the first CRM was called AX Systems in 1987. So basically every single file name cabinet became a database. And there were benefits to that. But it didn't actually make the world that much more efficient because whereas before you would have a human go fetch you the HR file for Eric. Oh, go to the HR filing cabinet, get me that file. Now it's in workday, but now you have to have a CISO to make sure that your workday doesn't get hacked. You need to have IT people to provision accounts in your SSO to workday. So did the world get that much more efficient? It did. If you have multiple offices now, people can collaborate. You could do complex joins in a database much, much harder to do that on pieces of paper. But that was kind of software from 1960 to 2022 because the filing cabinet couldn't think for itself. And now this is the cool thing about everything that's happening in AI land is that the filing cabinet can do work like QuickBooks can actually accomplish a task by itself versus relying on a human to retrieve the file from QuickBooks in the same way that the human in 1500 would retrieve a file from ye olde filing cabinet from the yield accounting department.

1:45

So it's interesting and it's actually a great segue into of course, what is everyone talking about? The SAS apocalypse. Some people call it the catastrophe, obviously what's happening in the public markets. And a lot of people have different perspectives of how significant it is, what it means. I want to hear from both of you how you interpret what's been going on and more importantly, what it means or how we should make sense of it. Why is there too much fear about this or how should we make sense of this?

3:34

Look, I think the world is trying to work out how to rate or value software businesses in a highly disruptive stage, right? And everyone has hot takes about what the future is going to look like, right? And depending on the takes, you get a version of the future that's either really good or really bad. For all of software, certain companies, certain categories in software, it's a really interesting thing. There's no doubt in my mind that the risk level has gone up. So if you think about from an investor mindset, you're like, this used to be a very stable category, now it's a more risky category. Hence I'm going to step away and watch. And as I always say, investors are trying to work out not necessarily the DCF cash flow model of a company for all profits of history. They're really trying to work out what are other investors going to do? And they're actually betting on what other people think that other people think they're going to do. And right now that it sort of logically makes sense. You have a interesting world where everyone has a version of what future is likely to look like and it seems likely to them. It's pretty disconnected from the reality on the ground. But the answer is always, what if AI can do that in two years or three years? What does that mean? And I think it comes from a very static viewpoint, right? Like that people won't adapt, the world won't change. It's like one thing is going to change and everything else is going to remain static. So you have this interesting world at the moment where businesses like ours are doing very well, right? We've had three great quarters in a row and everybody says so. And then you're like, wait, you know, that used to equate to some value. And it's our job to prove that that's not the case for our business, right? We're not here to defend all of software, obviously, but for our business, we feel very good about the opportunities we have, the data we keep showing, the results we keep showing. And now I always say this as well. It doesn't mean that we don't have to adapt. It's this weird world that, like, we are changing how we work radically and quickly, as we always have, as we've been doing for a number of years. Some part of that, I think, assumes that we won't be able to change, right? There are strategic vectors for sure. And look, the reality is, as I've said, not every SaaS company is going to thrive through the next decade, right? Just like a bunch didn't make it to the cloud, a bunch didn't make it from, I don't know, Windows to the Internet era, whichever era you want to say, no one is going to say. I think that a hundred out of a hundred SaaS companies are going to make it through and be thriving and growing on the other side. Also, we have this version that software kind of dies. A lot of it just ends up as a cash revenue stream. I can speak for us, this is the best thing that's happened to our business, right? We're in a knowledge world. We have tools to play with that knowledge, to act on that knowledge, to do all sorts of other things, to solve the jobs our customers have always hired us for. This logically is very good, but it's up to us to execute that through that transition, right? Which I think we're doing really well. But again, we have to prove that to people over time that the patience part is hard for markets.

3:58

Alex, how about you? How do you react to what's been happening? How do you make sense of what's going?

6:55

Well, I hope I'm right in the long run, which is all this stuff is crazy. I think I tweeted about this a few weeks ago where my kind of cursory glance is that there were three different types of SaaS companies, and the public markets couldn't tell the difference between the three. And one is where seats are tied to outcomes. So seats are being used by people who use kind of going back to the filing cabinet metaphor, right? If I'm Zendesk, I'm using Zendesk. And they came up with a very clever pricing model, which, by the way, maybe I can take a step back before I even answer your question, which is there's this great book by Dan Ariely called Predictably Irrational. And I used to give it to all my product managers in my company studied this to figure out how we charge people for stuff. Because it turns out like in. The example that he gives is, imagine you're locked out of your apartment, it's midnight. You hire a locksmith, comes one minute later, lets you in in 30 seconds, says it's 500 bucks. You're like, 500 bucks?

6:58

What the f?

7:49

Like, you just did like 90 seconds of work. You leave him a one star Yelp review, no tip, protest the charge on your credit card. Now imagine parallel universe. Locksmith comes, spends nine hours trying to let you in, goes back to his office to get more tools. Finally, by like, you know, 9:30 in the morning, finally lets you into your apartment. You're so grateful that he spent nine and a half helping you get into your apartment that you give him a $200 tip, leave him a five star rating on Yelp. This is an example that he gives in the book. And it basically means humans are kind of capable and willing to pay for incompetence. Like, it's like a lot of pricing is about fairness. Like, it feels fair that I give that guy more money even though he's completely incompetent than his counterpart, who's super competent, where I'm so pissed that he overcharged me and it doesn't make any sense, but, like, it feels fair. And if you think about how we got to SaaS, like per seat per month, like, when you're giving away, in many cases, it's like the additional cost of provisioning a Seat digitally is like close to zero. Not for everything, but for some things, like, it just feels fair. It's like, oh, you have 500 seats, you pay more money than if you have one seat, even though it's kind of the same thing going on in the background. So the three types of SaaS companies that I think of, great, great oversimplification here. But category one is you have seats, the seats are being used to produce some element of work, but now, oh, like you don't need the seats anymore to produce the element of work. So like Zendesk would be like patient one there where it's like, how many seats does a Zendesk customer need today? If they're using Sierra Decagon or you know, roll their own, it's like potentially zero. So Zendesk, when we talk about the present value of future cash flows, it's like they're imperiled because the per seat pricing, like if Zendesk said we're just going to charge you per seat per month for the current thing, never make a change to our code or our pricing, that revenue stream is 100% going to zero. On the other hand, it could triple, quadruple because they might just move to outcome based pricing and ditch. I mean, it still has to be subject to the laws of fairness and predictable irrationality that we talked about. But you know, something like Zendesk, it could go up, it could go down, but like the default path, unless it changes, going to zero. On the complete other side of that is you might have per seat pricing because it feels fair, but the seats are not tied to an outcome. So like Workday has this great pricing model where like, oh, you're ge, you have 340,000 employees and yeah, I'm going to charge you per employee per month. Why? I don't know, it just feels fair. But those employees that work at GE are not using workday to produce an outcome. So Workday I think is fine. In fact, if anything, and this kind of goes into like, what can you do with AI tools? Well, when you hire somebody at ge, they need to do a reference check and make sure that you worked at the three companies that you claimed you worked at. An HR person has to go look at the file that's in Workday and go call those three companies. Workday can call those three companies. Like an AI tool can do that, but only through the system of record. So you know, something like Workday or like intuit, it's down 45% in the first, like, you know, February 26th or 27th today, down 45%. Nobody's going to get rid of QuickBooks. So, you know, these are the two tent poles is like, you know, per. Seats are charged per month or per whatever, and it's tied to some kind of work. And then seats just happen to be a clever pricing trick, but it's not tied to work. And then there are things that are in the middle, like Adobe, like, yeah, it's maybe you need more seats, maybe you need fewer seats, but it's not as stark as the Zendesk example, nor the workday example. And then against that you have this kind of undercurrent of, oh, I'm going to vibe code everything. Which I think is just preposterous, having been a software developer for a very, very long time. Because the person that I like to cite as my counter example here is my second favorite economist, David ricardo, and in 1817 has been around for a long. He lived a long time ago. But it's like this is where the theory of comparative advantage comes from. It's like you could also grow your own food, you could weld aluminum. But even those are bad examples because it's very simple to grow food or weld aluminum. It's just, I have a comparative advantage filming podcasts with you. I could do that too, but I can earn more doing this, even though I might be more productive than the Plumber. But I should still do the podcast. That's actually less important than the, what I like to call like all the edge cases that lie beneath.

7:50

Right.

12:04

So like I could theoretically vibe code me some workday, but what happens in Indiana if the person leaves and they're on maternity leave? Like all these edge cases where it's just you don't know about them unless you've encountered them in the wild. So a lot of software is just a set of deterministic rules that have been learned from like in many cases, decades of experience. And the rules are not exposed, the rules are, they're kind of embedded and you can't just replicate them, you replicate them through experience. So I think it's like again, there are kind of three types of SaaS in my over simplistic view of the world. And then there is this like, oh, like it's no, like the IP is worthless because everybody's going to buy code their own thing. And I think on maybe for certain subcategories, if it's a very simple task with no edge cases, or maybe you don't need all the edge cases that have been Built in. I think software is going to do great because it's the true systems of record that have sticky software, that people rely on, that have all of these embedded edge. They're going to start adding AI where AI does the work, right? It's like, you know, workday will say, do you want us to do a background check? Intuit will say, do you want us to go collect on your outstanding accounts receivable? You don't have to go hire humans to do that. You go hire your software to do these tasks. That is starting to happen. But when that does happen, the present value of future cash flow, like that's going to go up a lot. Like the, the, the future ca. Like the, the present cash flows are going to go up a lot. And I, I just, it's, it's astonishing to me that a lot of public, you can't tell the difference between these different buckets. And they're not giving any kind. Like they're very excited about AI. But how do you deploy the AI? You have to deploy the AI through software. That's a system of record.

12:04

I think it's a fascinating time for everyone getting to first principles of what a business really does. So like you have all these, these views, right? I, I personally hate the system of record thing because it sounds like, oh, a system of records, just like a database sitting there. It's very static. I put stuff into it and I pull it out and that's it. And that views a business as a set of filing cabinets in a very sort of industrial era kind of world right? Now that was very different than the pre industrial era of a business. So totally it had a value. And I get why we have the term system of record, but it feels a little bit like why we have a floppy disk icon as the save button, right? Where my kids are like, what's that? And I'm like, that's a disk. And they're like, what is that? And I'm like, oh shit. You've never actually physically seen a disk, but you still have this icon. You know what the save button does? And the reason it's questioning this is to me, businesses are a set of processes, they're not a system of record. Like these are all process based systems, right? Everything Alex has just said is totally true. But there are processes like reference checking or other things, and your ability to coordinate a set of processes to happen as cheaply and efficiently and quickly as possible is actually in a knowledge business. Not an industrial era business, but a knowledge era business. Your entire business right. I have 10,000 plus people who walk into buildings every day and bring their brains and walk out and take their brains with them and that's it. I don't have any atoms, I don't have any bits, I don't stamp any steel. I don't even have any filing cabinets. I don't think. Right. And I am all about coordinating the sets of processes. I think most modern businesses probably are right. When you get to. How does that relate to Alex's commentary? I think it's totally true. We have different types of processes within a business. There are what I like to call input constrained and output constrained processes. The customer service example with Zendesk, that's input constrained. Your customers ask a certain amount of questions. How quickly you process those is about your efficiency, cost, speed, quality of running that queue. If you do it 10 times as fast, you don't get 10 times as many questions. Right. Like you have so many customers, there's a relationship or a ratio for every customer. They ask five questions. How can I make them ask less questions or process questions quicker? Right. There's actually a lot in a business that is an input constrained kind of a process. I always use our legal team as an example. Right. Their job is not to generate legal work, it is to answer it. So how many leases do we have, how many NDAs, how many contracts? It's like a fixed total set. And for that work, I'm trying to do it as efficiently as possible. And you have one entire vector for that set of processes. But then I have kind of output constrained work. If I think about anything creative marketing, I would argue software development technology where I can theoretically do an unlimited amount of tasks. Right. I'm constrained by my creativity, if you like, and how many things I can think of to do, how much value I can deliver for my customers. Those are actually where I'll take the efficiency gain and probably do more output rather than limit input within the bounds of making my company profitable and all these sorts of things. The challenge is to look at a business and try to make this analysis from the outside. Because all of your input constrained processes and output constrained processes actually work together to make a business. And they all have to kind of liaise in all these interesting ways. And that's where you see weird pieces of software that are just coordinating, quote, unquote, humans are running processes. And what you're saying about Indiana is totally true because some of those processes have outside rules, we call them laws, governance, compliance. That I have to do in Indiana. I have to do A certain thing for employees. So the processes are both how I want my business to run and how it has to run. And the business is really just a collection of all these processes put together. Like, I'm just saying it's a totally different view from the sort of we have a system of record and a system of action or whatever. And I'm like, that's not how I think most businesses actually run, but it's often how we think about it.

13:48

So I totally. I think that's a great framing. Like, despite the fact that I love, I love intuit, it's like TurboTax. Well, like the tax code is published, right? You can download all of these rules. It's highly deterministic. And then your files are in your, your like messy downloads folder and it's like make those two happen. In that case. It's like one of these bizarre situations where everything is actually transparent in terms of the processes. I think it's actually a quite rare situation where the edge cases are published in like maybe one place or maybe 50 places. But it's like, oh, you just. There are 50 states in the United States of America. Each one has its own tax code. There's the federal tax system, they have a tax code. Go download that stuff and make it work. And there probably still are edge cases and processes that you learn versus, like the real world normally isn't as neat as that. It's just like you learn by doing. And a business has value. I mean, there are a lot of businesses where theoretically, I mean, this is where it's like you would say like all the assets leave every night because they go down the elevator and they go home. Like that's, that's like more knowledge economy type things. But actually these businesses do have value. Like, you know, does McKinsey have value outside of all of the employees that work there? Because that's a knowledge economy business where they produce outcomes and you know, it's tied to labor. It's not like a product. But still, like they're, they're probably like, they probably have some top secret handbook that they use around. How do they hire people, how do they fire people, how do they produce outcomes for clients and so on and so forth. I haven't seen it. And that's actually great that I haven't seen it because I can't replicate it. And it's probably been built over a hundred years. And like, you know, what is it that non digital, non software products do? What is their product? Their product is the accumulated knowledge from potentially centuries or decades. I mean, I love going to Japan and you see like, oh, this noodle store has been around since like 1587. And it's like, yeah, there's probably something going on there. It's like this accumulated set of kind of culture and knowledge and know how besides, you know, here's the recipe list for making noodles. Maybe that is helping us. Making noodles is a little bit easier. Probably not as many edge cases, but I don't know, maybe there are edge cases. Like, what happens if you run out of flour? What do you do? How did the noodle shop survive the great flour shortage of 1623? They probably did something and that's like accumulated in this secret book of know how as opposed to, I'm just going to replicate something where all of the rules are published to the public.

17:38

Or maybe like Intuit again, this is where I think it's so fascinating. It forces us to rethink our businesses. Right. Is Intuit filling out the tax code for you, or does Intuit know the tax code as well as anyone else can? What they're helping is you to take your life data, your understanding, your. They're asking you the right questions. Intuit's almost more like a McKinsey. It can be considered that way. It's their process and their special ability is how to ask you the right questions to fill out the tax code rather than the filling out of the tax code.

20:02

Yeah, right.

20:32

And all these businesses are having to look at Maybe I have 50 processes internally that I think are my secret sauce and unique. Maybe only 20 of them are. But now I have to really consider which of those processes are actually unique and which are, which are not because we haven't had to think about it in that. In that manner before.

20:33

I think it's also kind of a question of how, like, there's this Goldilocks zone probably of like, is it worth doing yourself versus not like, if you take this kind of like third. Not third rail, but kind of this independent variable of. Should I now cloud code myself some X? Well, if it's like 99% of my cost and like my business is going to fail because this evil company is overcharging me for software, it might make sense. If it's like a dollar a year, it probably doesn't make sense. And then not all systems of record are the same. So, like, you know, I kind of think of a system of record as like the atomic unit of something for a business. Like, it could be calendars or a system of record for time or I don't know. ERP is a system of record for inventory. Like you have all these different systems of record. But like the, the example I was giving somebody is if I have an office in Miami that I don't go to very often and there's a system of record for conference rooms. There is a system of record for conference rooms. It's like Google Calendar. Like, am I willing to change that system of record? Yeah, because it's like my Miami office, they only get there once a year. Like, who cares? Versus like this is something that touches my revenue. It's not that expensive. Am I really going to grow my own food for something where. I mean, actually this is the cool thing about like farming, right. If you kind of take that metaphor, it's actually a lot cheaper to go to a restaurant if I just want like one hamburger versus like get myself a cow and feed the cow and wait till it's just a lot of food is actually cheaper if you consume it in a restaurant because of comparative advantage and economies of scale. So there probably are systems of record where it's like, there's some where outside of any of the factors that we're talking about, they're more susceptible just because they overpriced or they're just not as valuable in terms of what it is that they're storing and keeping records for. I mean like Carta keeps track of cap tables for a lot of companies. How often do you access your cap table? Not very often, but it's super valuable. You can't F that up, right? Like it's. I'd probably rather use Carter for that than like. And they only, they don't charge me that much money. Like, sure, I'll use Carta. And it's not, it's not like a daily use kind of product. So it's not even like that dimension.

20:50

I think the vibe coding thing is so fascinating to me because yes, there's someone in software, they're like, oh, people are going to vibe code all these replacements to tools. I'm like, the idea I would vibe code my own workday and then run it is terrifying. Like, have some really smart engineers. Firstly, I have other stuff for them to do. Secondly, I'm like, wait. I feel like that has way more downside than upside for me, however. And so that's the sort of replacement theory. There is a great gain we are seeing internally in extensibility of software using things like vive coding. So most of these applications are highly configurable, customizable, you know, in Our case all the way through to true extensibility. You can write pieces of software apps that run on top of our platform that have all sorts of different areas and lots of customers do. But those customers need to put a technology team on doing that job. Their ability to quote, unquote, vibe code, extensions, customizations, very tailored applications to their very specific use. Case of something. I want an app for the Miami team to do conference room booking. And Miami has some weird HR policies. So that app needs to look at workday and this and that. It's used by 20 people. I probably wouldn't have been able to afford to put the IT team internally on building that because the bill would have been too big. But now maybe I can build that. Right? But that uses workdays data and rules around the world underneath. But it just gives me a very custom interface for, I don't know, the person on the front desk in Miami to do something very specific to what they need. That is super powerful, but it's not a replacement for poor workday. I feel like Anil is like the butt of a lot of these conceptual examples. That's really powerful, right? That actually makes Workday stickier in the enterprise and more valuable because you can build all these applications on top, which is the power of AI and vibe coding and creativity to make it more tailored for what I need. But we're going to have to be really careful about these sort of layers of stability and rules and process versus customization. Right. And you could argue, I don't know, OpenClore and stuff is an example of building very personal apps. Just for me. Most of those people aren't software developers. They're building apps that work just for them on top of their Gmail or something else. Right. But it still uses Gmail as the Rails. They still go to Gmail to read their email and do their email. But they build some specific thing for themselves to solve a problem they have. And probably only they have a couple of them maybe turn into companies. Most of them are just solving some stuff that they needed themselves. That's it. And that's great. That's really powerful.

23:03

That's why I'm curious about maybe I'd call it my bucket. 2 of this pricing fairness, where the back end is not the front end. So if you think of Salesforce, they charge for licenses. Like I think we have 600 people at our firm might have 600 Salesforce licenses. I'd never logged into Salesforce, but I bet we pay for me. But I use the output of it Sometimes because it actually is the system of record, not to overuse that term firm, but it stores like all of our relationships. But I am like part of a table in a relational database. So it's like, you know, I'm user ID number 422 here and then whenever I meet with a company like, oh well, like user ID422 is matched in this other database. But we really just want to pay for a database. So like in a world where the front end is not the backend, I mean that's the thing. It's like for workday, I kind of think they've come up with a very clever pricing trick trick undersells it. I mean I think it's a powerful pricing paradigm that feels fair. It's like the more employees that you have. And why is that fair? Because GE has more profits than a 10 person company. GE is going to pay more for this thing. It's still a drop in the bucket. It's totally within the Goldilocks zone of pricing. I don't think anybody's going to vibe code that they're going to add all this AI revenue. But most importantly their pricing feels fair. Whereas for these things where it's like the front end is somewhat divorced from the back end, that one is, I don't know what's the fair format for pricing? Like what will happen to software pricing? And obviously if nobody's going to vibe code their own thing and there's not going to be any competition, then pricing will stay unchanged. But you can imagine a world where people are building things on top to read from the database, right? Because I mean a system of record has a database represented that's like the abstraction layer beneath everything. Will the pricing, will there be any pricing pressure on any of these categories? And for me I think it's like if the front end is not the back end, there's more susceptibility than if they're like very, very tightly intertwined. Like QuickBooks is used by small businesses, they don't have seats. It's like the owner of the business just logs into QuickBooks. So the front end kind of is the back end versus Salesforce. Where you can imagine nobody gets rid of Salesforce, but maybe they have fewer seats because they need fewer front ends, but they really still need the back end desperately. They're not going to go, you know, they're not going to eliminate or do anything with the back end.

25:37

It depends on always. Like I think your fairness and optics in pricing are really, really important people understanding what they pay for and feel like what they pay for is relates to their usage in some broad way. Right. I would say that a 10,000 person company paying for workday, the 20,000 person company probably plays twice as much plus some discount because they're buying more because they generally have twice as much complexity of stuff and they see that as fair. That's what you mean by like it seems reasonable that I would pay by employee for my HR system. I think the question with a lot of these things is, you know, what, what processes? When we talk about front end and back end as an example, it's not a database. It's a database plus a set of processes. We used to call it business logic when I was growing up. Those business logics are not irrelevant. So in the world of why does a business have them? Because it runs as a collection of processes and they want standardization of process to some level. Right. So that two teams work the same way so someone can manage them, understand them, track output. You know, I don't know if I have a bunch of car factories. I want to track the total amount of cars in and out consistently across them. The business logic where it gets baked in is somewhat where the value is because you may need. And again, maybe a 16Z is not a great example of a Salesforce customer, right. That actually has a huge amount of sales going on. In terms of traditionally the processes you bake into that for your sales teams are totally valuable to you and you would think that's a fair way to pay. The question is your sales adjacent teams, the sort of collaborator rather than the core user, how much do they need those processes and how much do they not? So I don't know. I assume Salesforce, sales cloud, I guess we're talking about sales cloud has an MCP server. That MCP server doesn't go to the database. It probably involves your processes and the rules on the way through. So the question is, someone sells adjacent, I don't know, they're in marketing or they're in customer success or something like this. If they need those processes and governance and controls and rules and you know, hey, we only do X for customers in Japan, we do Y for customers in this area, that sort of stuff. Even their MCP server is going to need an account. Whether the customer thinks that's fair, that's a different question. Right? It's just the challenge of like how does that get priced? I tell you, because we get this all the time talking about consumption based pricing, usage based pricing, outcome based pricing. There are a lot of categories where that makes sense. I definitely do not believe that it will be the majority pricing manner for all software, for all SaaS based software. Because when you talk to customers, they hate it. They really hate it. Where asterisk it is not related to the value they consider that they put in. So I have usage based pricing for Splunk. If I send them twice as many logs, I pay more money. I get it. But the logging is up to me, right? I can log more or I can log less. I can yell at teams where I'm like, hey, how come you're logging so much? This is expensive. And, and you know, are you using these logs? I can control the amount of data I put in. Same with storage in S3 or something. Canonically I put in a gigabyte, I put in two gigabytes. Fine, right. The problem is those are relatively transferable and controllable by me as a customer. A lot of the examples people give of either outcome or consumption based pricing are not in control by me as a customer and not exchangeable. So the AI token world, the AI credit world is really, really difficult for customers because they're like, I don't really understand what this casino token you've given, casino chip you've given me is right? I can take a gigabyte from AWS and go put it in Azure. And I know how much they're going to charge me because the gigabyte is kind of constant. When I have these AI credits, I'm like, I don't know if your credits are the same as yours or the same as yours. And by the way, you keep adding features which chew up my credits because my users use them. And I'm like, wait, I don't know what they're doing with those credits. Like it's not the company choosing to use them, it's the vendor adding like features that make the software better. That seem to just happen, right? I can 10x my customers credit usage overnight by adding a whole bunch of stuff like hey, I built these great summaries for you. And they're like wait, I didn't do that. So I think the outcome based usage, when you talk to customers, they want seats probably because today they understand it. And secondly they've been burned by a lot of this consumption base that the bill just goes up massively. And they're like wait, how do I control this? Right? It's pretty adjustment. It will be certainly present in a lot of categories. You know, we have a bunch of areas of our business at Atlassian that Are you would argue consumption based pricing or literally just consumption based pricing. But we try to stick to areas where customers do twice as much stuff, they get twice as much value, they pay twice as much money, and it's in their control. A lot of these other things aren't in their control. And the last example of outcome based pricing is those outcomes are also dynamic. So the problem with say customer service, where I've saved you, you know, you used to spend $20 on customer service. With our tool, you'll only spend 10. That's a great sales pitch in year one. In year two, the customer goes, but I only spend 10, now I want to spend 5, otherwise you didn't deliver any value. And the vendor goes, well, if you took me out, you'd be spending 20. And it's like, wait, but I don't spend 20, I spend 10. So like my ability to save you money each year is difficult from an outcome basis. Right. I'm eliminating tasks, I think also like

28:02

from a sales perspective. I've started two payment companies and it was really. I used to. This is why I know workday is I envied them. And I would talk to my sales team about workday because they know from the outside in how much money they make from GE. They're like, okay, GE uses PeopleSoft. They have 330,000 employees. Maybe we charge them $4 a month, but probably $5 per employee per month. This is how much money you make from that account. And it's so it's so much easier to scale a sales team if you're selling a software product or anything. By the way, if you know that company will pay us $3 million versus like, you know, we, when we were starting a firm, we signed up 1-800-FLowers. We have no idea how much we're gonna make from them. And it turned out like, you know what really made the business work? CASP Mattress company. It's like what, like this stupid matlab. But it's like you just don't know. And you think like you get like a big deal. Like we got Walmart. Didn't really work out that well in the beginning. We get Casper the mattress company. Oh my God. Incredible. Workday has the, it's predictability in both directions. Right? It's predictably for the spender of the money, which is the customer, but it's also the predictability for the management team knowing that you should spend your time trying to sign up George and not sign up a 10 person company. Because GE is bigger than a 10 person company. Whereas it's crazy in Internet land where it's like Stripe might make more money from a 10 person company than GE. And I guess you, you could get to like higher levels of predictability there. But like when you have outcome based pricing or consumption based pricing or something, I mean consumption based pricing is not bad per se, but if you don't know from the outside in how much you can make from an account, it just becomes exponentially harder to scale a sales, sales and marketing team because you just as an entrepreneur.

33:33

One thing I want to go back to sort of deal with how you guys are adapting in this era. Can you share more about the biggest ways in which that's manifested for you and you know, how it's made you change your business?

35:21

Look, I think the way that we think about it is we look, we sell collaboration tools that solve human collaboration problems, right? In lots of different areas. Service teams, broad business teams, hr, finance software teams, like lots of different types of teams by different sets of apps from us collections and sets of apps. Fundamentally they're all collaboration problems that involve a lot of text. So this is really good for us. What are those people doing is probably the important part. Right. The technology world often runs to we're going to reinvent everything and that's the way of the future. And that generally is true in the medium to long arc of time. Our challenge is always we have a lot of customers that work in today's manner, today's workflows, in today's set of apps. And they're not, they're very smart, they want to get to tomorrow, but they also have to move a lot of people. So when we're building AI features and I can give examples of any of these, we need to understand what that technology is, how it can help us. That's how we think about it. Firstly. Secondly, what fundamental platform componentry do we need to build for whatever that future will be? Because this stuff's accelerating so fast. Right. So that's how we got to our AI gateway and the teamwork graph and the enterprise compliance and controls. You have to separate that out from the features you're building for customers in a given app, then have to build features for customers that they use. Right. So where do you put those features? What are those features? A whole bunch of them are in existing workflows to help the customer do that existing workflow faster, better, higher quality, more efficiently. Those tend to be very unexciting from a magic point of view in terms of what sells a, you know, a 30 second animated GIF on. On X. But they're incredibly exciting from the customer because they can use them today. Like, their existing way of working just got better. They're like, this is amazing. Like, they rave about that stuff. And in the AI world, I'm like, but that's pretty simple. And it's like. But it actually helps them today in a massive way. I tell people internally though, and you can give an example in service or. Anyway, that's not enough because you also need to use their existing workflows with new apps or look at new workflows and be able to handle that as well, right? So we have to do all of these things. So if you look at, you know, Jira's canonical example, you know, in the service collection, in our, in our HR and IT service management products, summarizing a ticket is something we can do way better than we ever could because there's a lot of existing workflows we have in an enterprise, maybe a 4, 5, 6 people work a ticket internally to try to resolve a problem. The fourth person that shows up, there are a whole lot of attached files. There's a lot of conversation, There's a lot of different things going on. They would normally have taken 30 minutes to like read it all and understand what's going on. So then they can bring their expertise to bear on the problem. Literally just summarizing that. And it's not a simple stick it into, you know, an LLM and get back summary you have to be very careful about. The context is so powerful for them, but they haven't changed their workflow on iota. It's still Alex saying, hey, Eric, can you come help me with this ticket? Eric shows up. Eric has to bootload his brain with all the things. So that's like an existing workflow where we can use LLMs just to make that customer way better. And they love it, right? They rave about all these types of features, but they're very simple. They're usually not agentic. Then we can say, cool. But that service workflow, we need to put agents in at various spots, right? And most people are taking a workflow and finding, you know what, this step trips us up a lot. This costs us a lot of time. Can we make this step faster? And that's absolutely something that we have to provide agent frameworks ourselves. We have a pretty great agent framework that uses all the teamwork graph and all the context you have. And it's pretty simple. It's pretty very affordable. Or you bring your own agent framework, right? Most Businesses, I think will have three to five large scale agent platforms running internally. And they say, hey, I use AgentForce for this or I use Gemini for this. Great. Bring that agent and we'll pop it in the workflow here and I'll make that work. Right? We have to be able to do that. But you're still all in the existing workflow world. You're just doing the old task and then doing kind of a new and efficient task. But in the existing workflow then you get people like, what if the service ticket didn't exist at all? Right. So you're reimagining whole categories of software to new workflows. And we have to help our customers make it across that gap because they don't generally have one service team, they have hundreds. Right. And if they have hundreds of different service desks running, they might say these 20 are gonna work in this new way, these. But they have to manage them all. So I guess we're trying to bring data in the teamwork graph together with this. And also from a customer driven lens, I think that often gets left out here, right? We're trying to take them five years into the future. It's our job to actually get them one year and two years and five years into the future simultaneously, which we're trying to do. And the last thing I'd say is we're investing a lot in design. And I think that always in any AI conversation gets left out because there is a lot of foundational design to do in how this works. Right. We're seeing the first elements of this. But if I look at the mobile era, the first set of apps were kind of just canonically taking desktop or web things and sticking them in a phone. And then we evolved new patterns of interaction and experience. Right? Not even the visuals. How do we use these things? What were push notifications for? They didn't exist at the start, right? Drag to refresh is like a very obvious, simple example. That's a pretty canonical design pattern that generally it's successful here and it gets moved across. But the whole, like, how do I use my mobile and my desktop together? How do I move back and forth? We have so many design challenges to solve that actually help people to understand what's there. The average customer, we have, the average user, they don't want to understand. If AI doesn't exist for them, that's fine, but they want the outcomes of it, right? They don't need to know all of the technical detail. It's our job to hide them and just give them the answer they're looking for or make a task more effective or efficient. I feel like in the technology world, sometimes we get so obsessed by, like model quality, you know, it's. It's almost trite now to say the models are far ahead of the actual value they're delivering now. The underutilized capabilities are so big. A part of that equation is actually design and experience. Right? How do I get this? Give people a chat box that can do unlimited power and they're like, tell me a dad joke. Like, it's like unlimited power, but it does. It's very hard to help them utilize that power, which is where a huge amount of our challenge goes in terms of bringing agents and all the power of them into workflows and collaborative loops and having humans and agents work together.

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I love the skeuomorphic point. Well, it's first, it's like you had pieces of paper. The early web was just like a webpage. That's why it's called a web page. It's like eight and a half by 11, right? And then mobile. Oh, we'll make it a tiny web page. And then it turns out if you don't just go into the skeuomorphic world, but you just think from first principles and take advantage of the power of the device, do all sorts of other things. It's like, you know, the scroll to refresh, right? Like the pull down to refresh. That was a new concept that came from mobile. Right? So I was thinking about this the other day. Have you tried Nano Banana 2? Yes. It's really good, right? So one of my colleagues just said, hey, for an American tourist visiting Japan, make an infographic about what to do and not to do. And it's like it. One shot, something that's amazing. How do you edit that output?

42:07

Right?

42:54

And that's where it's like, you know, it feels very. It's like, well, you could edit the text, you could edit the graphics, you could just one shot something new or, you know, what. What is the state? I guess this is. My question for you is like, what do you think the state of the art is or should be and how. Have you been thinking about this? Just because you mentioned design for editing the output of the AI output, right? Because they're. They're like the. They're the classic. It's like, oh, I'll use a GUI and click here and change that. But it feels like that's very skeuomorphic.

42:55

I would zoom out two levels from that to answer that question. Because it's a Great question. First is customer trust is really hard in these areas, right? When you go talk to users, you sit down, you do research with them, you sit, you ask them questions, you ask the five whys. They're very scared of AI. Not because of its power, because it does stuff. And they're like, hey, how do I know that was right? What did it do? Right? It's like the idea that oh, don't worry, my AI bot's gone and sent 15 emails and manage your inbox. Your inbox is empty and you're like, okay, did it. I don't trust it yet. Like, so I have a trust question on generally AI doing things really quickly. To gain trust, it has to come back to you and say, here's what I'm about to do. You sure you want me to do this without being annoying? That like just effing, go and do it. So like that's a whole design question. How often is it, how do you build trust with any of these tools? The second is, does it have enough data? Right? So much of AI is one shotting things sit on X, you'll see a thousand like, hey, this is the magical prompt incarnation Harry Potter spell that does this. Like runs you a one person billion dollar business. Just put this prompt in and paste it. And like that's like kind of ridiculous because the reality is you also have a lot of iteration on the data side, right? One shotting things is really useful, but. But you often need to go back and edit the output and the input, right? I'm not very good. I've used this example for a while where you say, hey, go write me an essay for my homework. It'll spit out an essay. And you're like, wait, no, no, it's a history class. They're like, oh, okay, well that's totally an essay. And like you're actually changing the input and somewhat. This is chat like iterations. But if you've ever tried to do that image editing with chat iterations, it's super frustrating where it's like, oh no, you changed the thing I didn't want you to change. And you go and bad. You're like, ah, so like there's an input, design and experience problem. Part of that is how do I have the right amount of context? And then there's an output and iteration problems. Our teamwork graph can access largely all of your organizational knowledge. It's insanely accurate. It's got great search, it's got amazing relevance. And you're like, sweet, I have full organizational memory now. The teamwork graph knows that I used to Write code in 2002, and it knows that because it has this insane memory. And I'm like, it's actually not useful. Don't use that to answer any query I give you. Other than one thing. Mike used to be a developer. Maybe a bad one, right? It wouldn't get hired nowadays anywhere. But maybe that helps in explaining something to me in a way that, oh, you have a computer science degree. I can help explain it to you in this way. But I don't want to know all that information. Why is that? Input challenge. Do you kind of see all these boxes at the moment where it's like, search the web, don't search the web. Search my organization, don't search my organ. Like, you're asking a user to make all these choices they don't quite understand. That's not in a design flow, right where it says, hey, this question, I suspect you want me to do this and that. Is that correct? You see that a little bit in deep research, but it's a bit frustrating. And it leads to this whole, like, man, I've got 17 different agents running off and doing stuff. And I'm like, it's like, the problem of having a lot of interns, we like, the problem with having 50 interns is you get a lot of work done. The problem with having 50 interns is they ask you 50 questions a minute, and you're like, all you're doing is answering questions for interns. So there's an input problem of experience that you really need to solve. Then you get to the iteration problem, which in a corporation is much more difficult, right? Because we gave this great example of brainstorming, where it's not usually one person brainstorming. So in our whiteboard and confluence, you can bring in agents and say, hey, I want to brainstorm about this topic. They are really good at going off and getting all the information from your organizational knowledge through the teamwork graph and coming back with a really good brainstorm. And we get better and better at drawing it and putting the cards in right places and everything else. If you just take that randomly and say, go, you lose human input and trust. So actually, usually what happens then is, we've got a bunch of data, we're going to have a meeting, we're going to get people together, we're going to go and say, well, what do we all think? Add our intuition, the brain matter, which of these are useful or not useful? And then that information has to go back into some other agentic Loop to say cool. Now we've kind of voted. Although the voting is like the output of a human process, then you're going to go and do something and then we're going to work out what to do and did we do it correctly and all these things. It's as you said, it's very non deterministic in the quality of output. But it requires, I think this human agent loop. Right? And getting that right is a design problem. Too many loops, it's frustrating. Not enough loops, you lose trust and it just happens. And so we see that we just shipped agents in JIRA in a lot of ways. So you can like assign work to an agent and it goes off and does stuff. And when we test it with people, they're like, well, what's it doing? I'm like, do you want to give us a thousand steps? And they're like, why are you telling me all this crap? I'm like, wait, because you said you know what it was doing. And so there are lots of design challenges with just bringing them into workflows and back to the business processes. Like the, I don't know, the security team is involved in a lot of places. The accounting team, the finance team, there's lots of places. Like even in sales, finance usually has sign off on a deal or someone in finance does. How do you do that and make that workflow better where you're just assigning to agents. You need to be very careful about the experience. How does it come back? When does it come back? Is it frustrating? Does it come back in a new way? Can I interrogate what it's doing right now? Like our agent? First or third party agents, but running Endura, if they're off doing a task, you can chat to them while they're doing the task and say, what are you doing? Which helps you build trust in the short term we believe. But in the long term, if you trust it, this particular agent doing this task, man, it's got to write the last 20 times. The odds are right. It's good. I'm just gonna ignore it. These are all, I would argue, a fundamental foundational design and experience problem. They're not a technology problem. Right. They're getting millions of people who use our apps every day to trust this and the gains they get. And removing the blank box, I can do unlimited things for you, which just leads to paralysis. I think for, for most people it's,

43:28

it's, it's an open question, right? It's like, because it's clearly like it's not the yesterday version of like click your mouse here and it's not the today version of just do a new prompt. It's like both. It's like the, it's. It actually is like a. As long as humans are involved in some way, shape or form, which I firmly believe they will be, is these tools serve humans. You need to be able to get your head into the model, both from a trust perspective and from an iteration perspective. And it's a design problem. And I don't think nobody's quite nailed it yet, or I don't know, maybe they have, but it feels like we're at the very, very beginning of this process of coming up with a better design for modulating. Not even modulating, but just kind of editing the one shots which impressive as they are today, it's just like, that's not going to be. I just, just don't believe. It's just like, ah, Harry Potter spell incantation. I'm going to steal that phrase. That's a good one.

49:45

I think one thing, one interesting example is just writing documents is something we all do so naturally. And there is a huge design challenge and experience challenge which I can describe with AI document writing. But secondly, there's also a huge like people learning challenge. So like we sometimes forget pretty much people in technology know what a prompt is and what it does and what the LLM's doing in the background. You go to people in the broad business world, they don't have time to learn all this. They kind of probably know what ChatGPT is. They don't quite know how it's working. And the reason it's a design challenge of document creation is we have a whole set of features which we call Create with Rovo, which instead of writing a document by giving you a blank page and just starting to write, okay, I got a heading, I put some text in, I put another heading, I put some text in, I put a table, et cetera. Writers, we've all been trained for decades in knowledge worker to write a document that way. With Create with Rovo, you can literally say start with a prompt, right? Hey, I want a document that roughly does this or looks like this shape. Give me a template and I'll spit out a template. You can say, hey, I want a document. Can you go off and research this, that and the other and bring it back. But most of those documents, the research is actually a small category of tasks. It's like, help me get started with my document in some way. Teaching users that they should start that way is really really hard. Once they're running, though, they now have two panes, right? They have 75% of the screen is the document itself and 25% is a chat window. Think of Microsoft Word without a toolbar. But with chat only. Now I can type text in, I can edit it, I can change it. And you need to say, hey, you should be totally comfortable. Change everything on the left, but you can do operations on the right, like, hey, I want you to add a new section that goes and researches other stuff and put it after, like, the summary and it'll go and do that. Trying to watch. Power users are like, this is amazing. And they're like moving back and forth and they're getting the whole paradigm and they're like doing things. And they can write commands like, you know what, make every heading blue, which you can't do in Word. And they're like, bang, it's all blue. And they're like, this is cool. I can kind of give it commands across the document and I can go get more information and I can like, hey, can you resummarize it quicker or man, how do you think they can ask questions like, how do you think the board is going to read this document as a board member? Is it simple enough? And it'll give you information in chat that you may say, cool, go, action, out, or don't. It's a completely different paradigm to writing a simple document, which is just at the end of the day, you know, headings and bullets and text and stuff. And when you watch power users, they love it. Normal people, like regular business users who are very smart, they're like, so I just type on the left. That's all I do. I'm like, well, yes, it's a whole paradigm shift, I suspect, as we get more of these tools and experiences, just like mobile two years from now and five years from now, that'll be very standard. They'll all say, yeah, I get how to do this, right? Maybe the first time someone looked at Excel, they were like, wait, where do I type the paragraphs or something? And you're like, oh, no, you have to think differently about it now. It's just like, oh, yeah, I get Excel. I know how it works. That's the experience challenge we have, I think, to go this power and put it into something as simple as writing a document with all my organizational knowledge. Like, oh, yeah, I get the maths of why that's possible. But now help me actually help people do it. Massive amount of challenge there. Match amount of excitement, right? It's when they get it, they're like, this thing is amazing, but it's going to take us a lot of time to get get the experiences correct for people to learn.

50:38

That's a great place to wrap. Mike, thank you so much for coming on the podcast.

54:02

It's been an excellent discussion in no worries, guys. Hope it was.

54:05

Yeah, it was great meeting you, Mike.

54:08

Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X16Z and subscribe to our substack at a 16 zone. Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details details including a link to our investments, please see a16z.com disclosures.

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