Give people a chat box that can do unlimited power and they're like, "Tell me a dad joke." In the technology world, the underutilized capabilities are so big. It's almost trite now to say the models are far ahead of the value they're delivering.
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.
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 software using things like vibe coding.
Everyone has been talking about the SaaS apocalypse. Some people call it the catastrophe. Why is there too much fear about this?
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.
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 of secretaries in like the 1950s and 1940s.
Airlines have been around for a long time and then it put them in an early SQL database or an early database back when a 10 megabyte hard drive probably cost like $100 million. And then that's what happened with electronic health records and the first one was called Mumps; it was built by Mass General Hospital. Those were the first systems predating Salesforce or the first CRM was called ACT! systems in 1987.
So every single filing cabinet became a database and there were benefits to that but it didn't make the world that much more efficient. Because whereas before you would have a human go fetch you the HR file for Erik—like "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 on a database, which is 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 like the cool thing about everything that's happening in AI land is that the filing cabinet can do work. Like QuickBooks can accomplish a task by itself versus just 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 old filing cabinet from the ye old accounting department. So it's interesting.
It's a great segue into, of course, what everyone is talking about right now, or has been talking about: the SaaS 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 or 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?
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's going to look like. 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, or 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 it 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, right? And they're betting on what other people think that other people think they're going to do.
And right now that sort of logically makes sense. You have an interesting world where everyone has a version of what the 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, but what? That's used to equate to some value." And it's our job to prove 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 that doesn't mean—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. There are strategic vectors for sure. But 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 or whichever era you want to say. No one is going to say, I think, that 100 out of 100 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 through that transition, right? Which I think we're doing really well, but again, we have to prove that to people over time. The patience part is hard for markets.
Alex, how about you? How do you react to what's been happening, or how do you make sense of what's going on?
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 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, going back to the filing cabinet metaphor, right? Like 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—there's this great book by Dan Ariely called *Predictably Irrational*. I used to give it to all my product managers at my company. It's like, study this to figure out how we charge people for stuff. Because it turns out people like—and the example that he gives is like, imagine you're locked out of your apartment. It's midnight. You hire a locksmith, he comes one minute later, lets you in in 30 seconds, says it's 500 bucks. You're like, "500 bucks? What the f? Like, you just did 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 9:30 in the morning, finally lets you into your apartment. You're so grateful that he spent nine and a half hours 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 means humans are kind of capable and willing to pay for incompetence. It's like a lot of pricing is about fairness. 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.
And if you think about how we got to SaaS—per seat, per month—when you're giving away in many cases the additional cost of provisioning a seat digitally is like close to zero. Not for everything, but for some things. 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 oversimplification here—but category one is like you have seats, the seats are being used to produce some element of work, but now uh-oh, you don't need the seats anymore to produce the element of work. So Zendesk would be patient one there where it's like, "How many seats does a Zendesk customer need today if they're using Sierra, Decagon, or roll their own?" It's potentially zero.
So Zendesk, we talk about the present value of future cash flows. It's like they're in peril because the per-seat pricing—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 or quadruple because they might just move to outcome-based pricing and ditch per-seat. It still has to be subject to the laws of fairness and *Predictably Irrational* that we talked about. But something like Zendesk, it could go up, it could go down, but the default path unless it changes is 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 Workday has this great pricing model where like, "Oh, you're GE, you have 340,000 employees. 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 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 if you're the system of record. So something like Workday or Intuit—it's down 45% today, February 26th or 27th—nobody's going to get rid of QuickBooks.
So these are the two tentpoles: 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. Yeah, 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 long time. Because the person that I like to cite as my counter-example here is my second favorite economist, David Ricardo. In 1817—he lived a long time ago—this is where the theory of comparative advantage comes from. It's like, you could also grow your own food. You could weld your own 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 a podcast 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 less important than what I like to call all the edge cases that lie beneath, right? 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—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, in many cases, decades of experience, and the rules are not exposed. 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 oversimplistic view of the world, and then there is this idea that the IP is worthless because everybody's going to vibe code their own thing. I think 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 cases—they're going to start adding AI where AI does the work.
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—the present cash flows are going to go up a lot. And it's astonishing to me that a lot of public market investors can't tell the difference between these different buckets. 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. 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 views, right? I personally hate the "system of record" thing because it sounds like, oh, a system of record is 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 industrial era kind of world. Right now, that was very different than the pre-industrial era of a business. So 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 kid's like, "What's that?" and I'm like, "That's a disk." And they're like, "What is that?" and I'm like, "Oh, you've never physically seen a disk, but you still have this icon."
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, in a knowledge business—not an industrial era business, but a knowledge era business—your entire business.
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, which I think most modern businesses probably are.
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? There's a ratio. For every customer, they ask five questions. How can I make them ask fewer questions or process questions quicker?
There's 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. 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 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 where I'll take the efficiency gain and probably do more output rather than limit input within the bounds of making my company profitable.
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 work together to make a business and they all have to liaison in all these interesting ways. And that's why you see weird pieces of software that are just coordinating, quote-unquote, humans 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. 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. 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 run, but it's often how we think about it.
I totally think that's a great framing. Despite the fact that 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 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 transparent in terms of the processes. I think it's a quite rare situation where the edge cases are published in maybe one place or maybe 50 places. There are 50 states in the USA. Each one has its own tax code. There's the federal tax system. 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. There are a lot of businesses where theoretically—this is where it's like you would say all the assets leave every night because they go down the elevator and they go home—that's more knowledge economy type things. But these businesses do have value. 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 it's tied to labor, it's not like a product, but still, 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. I haven't seen it and that's great that I haven't seen it because I can't replicate it and it's probably been built over a hundred years.
And 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 love going to Japan and you see like, oh, this noodle store has been around since 1587. There's probably something going on there. It's like this accumulated set of culture and knowledge and know-how besides "here's the recipe list for making noodles." Maybe a bad example because making noodles is a little bit easier. Probably not as many edge cases. But what happens if you run out of flour? How did the noodle shop survive the great flour shortage of 1623? They probably did something and that's accumulated in this secret book of know-how as opposed to replicating something where all of the rules are published to the public.
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 for 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.
Yeah.
Right. 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 unique and which are not because we haven't had to think about it in that manner before.
I think it's also kind of a question of how—there's this Goldilocks zone probably of "is it worth doing yourself versus not?" If you take this independent variable of "Should I now Claude-code myself some X?" Well, if it's like 99% of my cost and 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. I kind of think of a system of record as like the atomic unit of something for a business. Like it could be calendars are a system of record for time, or ERP is a system of record for inventory. You have all these different systems, but the example that 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. It's like Google Calendar. Am I willing to change that system of record? Yeah, because it's like my Miami office, they only go once a year. 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—this is the cool thing about farming, right? If you take that metaphor, it's a lot cheaper to go to a restaurant if I just want one hamburger versus get myself a cow and feed the cow and wait. It's just a lot of food is cheaper if you consume it in a restaurant because of comparative advantage and economies of scale.
So there probably are systems of record where they're more susceptible just because they're overpriced or they're just not as valuable in terms of what it is that they're storing. 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? I'd probably rather use Carta for that and they don't charge me that much money. Sure, I'll use Carta. It's not like a daily use kind of product. So it's not even like that dimension.
I think the vibe coding thing is so fascinating to me because someone in software is like, "Oh, people are just 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. I 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 vibe coding. Most of these applications are highly configurable, customizable, in our case all the way through to true extensibility. You can write pieces of software, apps that run on top of our platform, 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—I want an app for the Miami team to do conference room booking and Miami has some weird HR policy, 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 Workday's data and rules around the world underneath. But it just gives me a very custom interface for 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 Workday. Poor Workday, I feel like Aneel is the butt of a lot of these conceptual examples.
That's really powerful, right? That 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, OpenColor 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. But it still uses Gmail as a Rails. They still go to Gmail to read 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.
That's why I'm curious about maybe I'd call it my bucket two of this pricing fairness where the backend is not the frontend. If you think of Salesforce, they charge for licenses. I think we have 600 people at our firm. We might have 600 Salesforce licenses. I've never logged into Salesforce, but I bet we pay for me. But I use the output of it sometimes because it is the system of record. Not to overuse that term, but it stores like all of our relationships. But I am like part of a table in a relational database—it's like I'm user ID number 422 here, and then whenever I meet with a company, like, "Oh well, user ID 422 is matched in this other database." But we really just want to pay for a database.
So in a world where the frontend is not the backend, for Workday, I kind of think they've come up with a very clever pricing trick—"trick" undersells it, it's a powerful pricing paradigm that feels fair. 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. And 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 the frontend is somewhat divorced from the backend, that one is—I don't know what's the fair format for pricing? What will happen to software pricing? 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, because a system of record has a database abstraction layer beneath everything. Will there be any pricing pressure on any of these categories? For me, I think if the frontend is not the backend, there's more susceptibility than if they're 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 frontend kind of is the backend. Versus Salesforce, where you can imagine nobody gets rid of Salesforce, but maybe they have fewer seats because they need fewer frontends, but they really still need the backend desperately.
It depends on—fairness and optics in pricing are really important. People understanding what they pay for and feeling like what they pay for relates to their usage in some broad way. I would say that a 10,000 person company paying for Workday, the 20,000 person company probably pays twice as much plus some discount because they generally have twice as much complexity of stuff, and they see that as fair. That's what you mean by "it seems reasonable that I would pay by employee for my HR system."
I think the question with a lot of these things is what processes—when we talk about frontend and backend, 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. Why does a business have them? Because it runs as a collection of processes and they want standardization of process to some level so that two teams work the same way, so someone can manage them, understand them, track output. 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.
Maybe a16z is not a great example of a Salesforce customer that 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 collaborator rather than the core user. How much do they need those processes and how much do they not? I assume Salesforce 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 sales-adjacent—they're in marketing or they're in customer success—if they need those processes and governance and controls and rules, even their MCP server is going to need an account.
Whether the customer thinks that's fair, that's a different question. I'd 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 SaaS 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.
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: "Hey, how come you're logging so much? This is expensive." I can control the amount of data I put in. Same with storage and S3. I put in a gigabyte, I put in two gigabytes. Fine. 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.
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 chip you've given me is." 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." And by the way, you keep adding features which chew up my credits because my users use them. It's not the company choosing to use them; it's the vendor adding features that make the software better that seem to just happen. I can 10x my customer's 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-based pricing where the bill just goes up massively and they're like, "Wait, how do I control this?"
Right, will take some adjustment.
It will be certainly present in a lot of categories. We have a bunch of areas of our business at Atlassian that are 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. The problem with say customer service where I've saved you—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 spent 10. Now I want to spend five, 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. I'm eliminating tasks.
I think also from a sales perspective—I've started two payment companies and I envied Workday. 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 much easier to scale a sales team if you know if that company will pay us $3 million.
Versus when we were starting a firm, we signed up 1-800-Flowers. We have no idea how much we're going to make from them. And it turned out, you know what really made the business work? Casper, the mattress company. It's like, what? But you just don't know. You get a big one like Walmart and it didn't really work out that well in the beginning, and you get Casper the mattress company—oh my god, incredible. Workday has predictability in both directions; it's predictability for the spender but also for the management team knowing that you should spend your time trying to sign up GE and not a 10-person company. Whereas it's crazy in internet land where Stripe might make more money from a 10-person company than GE. When you have outcome-based pricing or consumption-based pricing, 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 and marketing team.
As an entrepreneur, one thing I want to go back to—you dealt 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 how it's made you change your business?
Look, I think the way that we think about it is: we sell collaboration tools that solve human collaboration problems, right? In lots of different areas—service teams, broad business teams, HR, finance, software teams. 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 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 very smart. They want to get to tomorrow but they also have to move a lot of people. When we're building AI features, we need to understand what that technology is and how it can help us. 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.
Then you have to build features for customers that they use. Where do you put 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 30-second animated GIF on X, but they're incredibly exciting for the customer because they can use them today. Their existing way of working just got better. They rave about that stuff.
I tell people internally though, in service, 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. Jira is a canonical example. Summarizing a ticket is something we can do way better than we ever could. In enterprise, maybe five or six people work a ticket internally. The fourth person that shows up, there are a lot of attached files and conversation. They would normally have taken 30 minutes to read it all to bring their expertise to bear. Literally just summarizing that—and it's not a simple "stick it into an LLM and get back summary," you have to be very careful about the context—they haven't changed their workflow an iota. It's still Alex saying, "Hey Erik, can you come help me with this ticket?" Erik shows up, Erik has to bootload his brain. That's an existing workflow where we can use LLMs just to make that customer way better. They love it, but they're usually not agentic.
Then we can say, "Cool, but that service workflow, we need to put agents in at various spots." Most people are taking a workflow and finding which step trips us up or costs a lot of time. Can we make this step faster? We provide agent frameworks ourselves—we have a pretty great agent framework that uses the Teamwork Graph and all the context you have—or you bring your own agent framework. Most businesses will have three to five large-scale agent platforms running internally. "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. You're still in the existing workflow world, just doing an old task in a new and efficient way.
Then you get people like, "What if the service ticket didn't exist at all?" reimagining whole categories of software to new workflows. We have to help our customers make it across that gap because they don't generally have one service team; they have hundreds. If they have hundreds of different service desks running, they might say these 20 are going to work in this new way, but they have to manage them all. So 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 get them one year and two years and five years into the future simultaneously. And the last thing I'd say is we're investing a lot in design. That always gets left out because there's a lot of foundational design to do in how this works. Mobile era: the first set of apps were just taking desktop or web things and sticking them in a phone. And then we evolved new patterns—push notifications, drag to refresh. We have so many design challenges to solve that help people understand what's there.
The average user doesn't want to understand if AI doesn't exist for them, that's fine, but they want the outcomes of it. They don't need to know all the technical detail. It's our job to hide them and just give them the answer they're looking for. In the technology world, we get so obsessed by model quality. It's almost trite now to say the models are far ahead of the actual value they're delivering. Part of that equation is design and experience. Give people a chat box with unlimited power and they're like, "Tell me a dad joke." 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 into collaborative loops and having humans and agents work together.
I love the skeuomorphic point. Early web was just like a "web page," eight and a half by eleven. And then mobile, "we'll make it a tiny web page." And then it turns out if you just think from first principles and take advantage of the power of the device, you do all sorts of other things—like the pull down to refresh. I was thinking about this the other day—have you tried NotebookLM?
Yes.
It's really good. One of my colleagues said, "Hey, for an American tourist visiting Japan, make an infographic about what to do and not to do." And it oneshots something that's amazing. How do you edit that output? That's where it's like—you could edit the text, the graphics, you could oneshot something new. What do you think the state-of-the-art is or should be? How have you been thinking about design for editing the AI output? Because the classic is like, oh, I'll use a GUI and click here, but that feels very skeuomorphic.
I would zoom out two levels from that. First is customer trust is really hard in these areas. When you go talk to users, 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 and what did it do?" The idea that, "Don't worry, my AI bot has gone and sent 15 emails and managed your inbox," and you're like, "Okay, did it? I don't trust it yet." So I have a trust question. To build trust, it has to come back to you and say, "Here's what I'm about to do, are you sure you want me to do this?" without being annoying. That's a whole design question: how do you build trust?
The second is: does it have enough data? So much of AI is oneshotting things. On X, you'll see "this magical prompt Harry Potter spell that runs you a one-person billion-dollar business." That's kind of ridiculous because the reality is you also have a lot of iteration on the data side. Oneshotting things is really useful, but you often need to go back and edit the output and the input. "Go write me an essay for my homework." It spits it out and you're like, "Wait, no, it's a history class." "Oh, okay." You're changing the input. This is chat-like iterations.
But if you've ever tried image editing with chat iterations, it's super frustrating where it's like, "No, you changed the thing I didn't want you to change." So there's an input design problem: how do I have the right amount of context? And then there's an output and iteration problem. Our Teamwork Graph can access all of your organizational knowledge. It's insanely accurate. You're like, "Sweet, I have full organizational memory." Now, the Teamwork Graph knows that I used to write code in 2002. It knows that because it has this insane memory. And I'm like, it's not useful! Don't use that to answer any query I give you other than "Mike used to be a developer." Maybe a bad one—wouldn't get hired nowadays—but maybe that helps in explaining something to me by saying, "Oh, you have a CS degree, I can explain it this way." But I don't want all that information.
Why is that an input challenge? You see all these boxes at the moment: "search the web," "don't search the web," "search my organization." You're asking the user to make choices they don't quite understand. That's not in a design flow where it says, "Hey, for 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. It leads to this problem where you've got 17 different agents running off doing stuff. It's like having 50 interns: you get a lot of work done, but they ask you 50 questions a minute and all you're doing is answering questions for interns. So there's an input problem of experience.
Then you get to the iteration problem, which in a corporation is much more difficult. In our whiteboard in Confluence, you can bring in agents and say, "Hey, I want to brainstorm about this topic." They are really good at getting all the info from the Teamwork Graph. If you just take that randomly and say "go," you lose human input and trust. So usually what happens then is we've got a bunch of data, we're going to have a meeting, get people together, and say, "What do we all think? Add our intuition, the brain matter." Which of these are useful? And then that info has to go back into some other agentic loop to say, "Cool, now we've voted, now you're going to go and do something." It's very non-deterministic in the quality of output but it requires this human-agent loop. Getting that right is a design problem: too many loops is frustrating, not enough loops you lose trust.
We just shipped agents in Jira. You can assign work to an agent and it goes off and does stuff. When we test it, people are like, "What's it doing?" If you give them a thousand steps, they're like, "Why are you telling me all this crap?" I'm like, "Wait, because you said you didn't know what it was doing!" So there are lots of design challenges with bringing them into workflows. Security, accounting, finance—they're involved in lots of places. How do you do that and make that workflow better? You need to be very careful about the experience. How does it come back? Is it frustrating? Can I interrogate what it's doing? Our agents running in Jira—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. Long term, if it's got it right the last 20 times, you're just going to ignore it. These are fundamental foundational design and experience problems, not technology problems.
It's an open question, right? It's clearly not the yesterday version of "click your mouse here" and it's not the today version of just "do a new prompt." It's both. As long as humans are involved, which I firmly believe they will be because 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. I don't think anybody's quite nailed it yet. We're at the very beginning of coming up with a better design for modulating or editing the oneshots. I just don't believe it's just "Harry Potter spell incantation." I'm going to steal that phrase.
I think one interesting example is just writing documents. There is a huge design challenge and people-learning challenge. We sometimes forget: people in technology know what a prompt is. You go to 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. The reason it's a design challenge of document creation is we have a whole set of features which we call Create with Rovo. Instead of writing a document by giving you a blank page and just starting to write—heading, text, heading, text, table—which we've all been trained for decades to do, with Create with Rovo you can literally say "start with a prompt." "Hey, I want a document that roughly does this." It'll spit out a template. You can say, "Go off and research this, that, and the other, and bring it back."
Teaching users that they should start that way is really, really hard. Once they're running though, they now have two panes: 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, edit it, change it. And you can do operations on the right like, "Hey, I want you to add a new section that goes and researches this other stuff and put it after the summary." And it'll go and do that. Watching power users, they're like, "This is amazing," moving back and forth, writing commands like, "Make every heading blue," which you can't do in Word and bang—it's all blue. "How do you think the board is going to read this document? Is it simple enough?" And it'll give you info in chat that you may action out or not.
It's a completely different paradigm to writing a simple document. Normal people—regular business users—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 two years from now that'll be very standard. Maybe the first time someone looked at Excel, they were like, "Wait, where do I type the paragraphs?" and you're like, "Oh, no, you have to think differently about it." Now it's just, "Oh yeah, I get Excel." That's the experience challenge we have—to take all this power and put it into something as simple as writing a document with all my organizational knowledge. Massive amount of challenge there, but also massive amount of excitement. When they get it, they're like, "This thing is amazing." But it's going to take us a lot of time to get the experiences correct for people to learn.
That's a great place to wrap. Mike, thank you so much for coming on the podcast. It's been an excellent discussion.
Yeah, no worries, guys. Hope it was useful.
It was great meeting you, Mike.
Great meeting you.