Okay.
Excellent. Well, hey, good morning, everyone. Welcome to day one of the Barclays Tech Conference. My name is Saket Kalia. I cover software here. Really honored to have the team with us here from Autodesk. We've got Mike Haley, SVP of Research, as well as Simon Mays-Smith, Head of Investor Relations. We've got about 30 minutes together. Let's take the first 20-25 minutes to go through some fireside chat for the first 20 or 25 minutes, which I know is gonna be fun.
Mm-hmm.
And then we'd love to make it interactive. So any questions that you've got, just pop up your hand and we'll get a mic around. So, with that, Mike and Simon, thank you so much for being with us here today. Simon, why don't you kick us off with a Safe Harbor?
Yeah.
Yeah.
Yeah.
This is our Safe Harbor, so we may make forward-looking statements during the course of this presentation. Please refer to our SEC filings for information on risks and other factors that may cause our actual results to differ materially from these statements. Okay. Cool.
Absolutely. Now we can talk. Well, listen, guys, thanks or thanks a ton for coming. A lot of great stuff to talk about here at Autodesk. But maybe Simon, just to level set for us before we dive in with Mike, can you just recap a few of the most important points you wanted us to know coming out of the last call, just so that we're all on the same page?
Yeah. And this is an unrehearsed perfect segue for Mike as well. It's really sort of three things. It's first, the business is performing pretty well, both in absolute terms and compared to almost all of our competitors. Secondly, we gave some talking points really for what things to think about for next year. And those basically fell into the three buckets. One is that we will continue to think about macro and potential disruption from sales and marketing optimization when we set guidance in February. Secondly, that we will have incremental headwinds from the New Transaction Model to margins, which is why we said the path to our fiscal 2029 goals would be non-linear.
And thirdly, we reminded people just of the math of the new transaction model, oh, sorry, the switch to annual billing transition, which has given us tailwind to billings growth and free cash flow growth in fiscal 2025 and 2026. And obviously, that process is pretty much done by the end of this year. So growth in billings and free cash flow will be normalized, more normalized in fiscal 2027 than it has been in 2025, 2026. All of those things we said before.
Yep.
So that was the second thing. And then the third thing is just banging the drum that we are years ahead of our competitors in AI, thanks to the work that Mike and others in the company have been doing. And we're pretty excited about it.
Yeah. Absolutely. That's a good summary and there's a lot to dig into there, as well, but Mike, maybe to.
Yeah.
Maybe to start with you, this is your first Barclays Tech Conference with us, hopefully the first of many.
First of many, hopefully.
Yep. Absolutely, but maybe for those of us that are meeting you for the first time, can you just give us a little bit of a brief background on yourself and your mandate in your current role?
Yeah. Sure. So in my role, I lead up Autodesk Research. So Autodesk Research is around about a 300-person organization in the company who we are the only organization inside Autodesk whose fundamental mandate is to stay 5 to 10 years ahead of the company.
Right.
So our job is to de-risk technologies in a safe, quick, rapid environment and to find new investment opportunities. So we are doing everything you can imagine, from AI to robotics to new forms of user interfaces and across all of our industries, architecture, manufacturing, and media, right? That's why we have a significantly sized organization. Now, coming to Simon's point, coming out of that work, we started doing AI work in my lab in 2009 and we've been gradually expanding and growing that.
In 2018 is when we created our AI lab, which is kind of like our own DeepMind kind of organization, right, where we're doing very, very deep work on not on generic AI that everybody else is working on, but on AI that is specific to geometry, to shapes, to structures, to products, to buildings, in other words, the things our customers are building. So that's where we've been laser-focused now for seven years. So what you're now beginning to see from us is the other part of my job, which is leading our AI strategy across the company. So I lead all of our generative AI work. We have a large cross-company investment in this right now. We've been heavily focused at taking this technology that we developed in the lab and bringing it to market right now.
We announced a bunch of that at our Autodesk University conference this last year, which I'm happy to share later. But it's a really exciting moment for us. What I'll also just mention is that the cycle time these days between research and product is shrinking. You know, I've been doing research for a long time.
Definitely.
It would often take a decade for the new technology. It can be a matter of, you know, three to six months now, you know? So the role of research in actually developing product and getting it out to market is becoming different, which is a pressure I'm feeling, but it's a good thing.
So two things to add to that, sorry, which are important. The fact that time to market is a process, and that muscle takes time to build.
Exactly.
And just putting that together, building out that expertise is one of actually our barriers to entry and why it's gonna take time for our competitors to replicate it. 'Cause building that research breadth and depth and then making it work faster is really hard to do. The second one is that centralization is critical. A lot of companies are letting a thousand flowers bloom across the company, huge mistake. We're getting very inefficient AI investments.
Inefficient. Yep.
Not inefficient, yeah, not focused investment as well, so having that centralization and focus, putting the resources behind it is very important.
That is. And that cycle time in particular is something that I hadn't thought about.
Mm-hmm.
Maybe just to build on that a little bit though, Mike, and maybe to dig into some of the individual areas of Autodesk's business. I wanna start with the architecture piece.
Sure.
of course, anchored by your leading tool, Revit.
Mm-hmm.
Maybe the higher-level question I wanna ask is, how is generative AI gonna change the daily life for an architect in your view, if at all?
Yeah. Yeah. I know. Look, I think it's gonna be a radical change. Is it all gonna arrive in one night? No, it's not. But I would break it into sort of three categories of change. So the first one is we've been pushing this idea of sort of outcome-based design in architecture for a while, which is really about thinking of what you want the ultimate product to be at the end of the day. I want it to be low cost. I want it to be low carbon. It has to exist within this existing city infrastructure. There's all of these broader mandates that are known at the beginning of a project, but it's hard to actually compute them. It's hard to actually use those in any meaningful way at the beginning of a project.
It's much easier to simulate and everything later on to make sure it complies to those, but that's often too late. So AI is able to help people early in conceptual design, right, the very beginning of the creating a new structure or building or infrastructure to understand the impact and the goals that you wanna go after. So that's the first thing. The second thing is the actual way you operate and interact with each other in the architecture space. This might be an architect working with a customer, for example, who's trying to understand what a building might look like, a new foyer in a hotel, what's this gonna look like? Or it might be working with engineers, and they're sharing information with each other.
Archi, the whole architecture and building practice is renowned for it being difficult to communicate with people.
Mm-hmm.
It might be due to different representations of information or different skill levels of the people you're talking to. AI is beginning to eliminate that. It's beginning to be able to produce renderings instantly of what the architect's talking about or take the structural engineer's information and automatically transform it into the architect's information. So now they too can talk to each other much like a language translator. That's the second one. The third one we see is really around what we call automation. So at least 70%-80% of any design work, and this is not just true of architecture, this is true of manufacturing as well, is not creative stuff. It's doing pretty much repetitive kind of tedious work. Like, for example, producing plans, like 2D actual blueprints.
You go to a construction site, there's still somebody walking around.
Oh, I've seen those too.
With E-size rolls of paper, right? You know, never mind the iPads.
Yeah.
So producing those is not a creative endeavor 'cause the 3D model already exists, but it's tedious. It takes weeks to produce those. Let AI produce that for you. So there's a whole set of workflows that are gonna literally become automated overnight because they're not creative things. You know, they're time wasters effectively. So just those three alone, you can imagine how those play out across architecture.
Yeah. That's super interesting. Simon, maybe the follow-up for you on that point. I mean, in application software, we've all sort of debated about kind of seats and, and value, particularly this year. Maybe to Mike's point, as Autodesk sort of enhances that, that productivity of an engineer or of an architect, how did the, how do you think the economics here could change? I mean, could there be fewer seats, but maybe more value that Autodesk earns? I'm curious just in general how you think about that.
Yeah. So the way we're sort of thinking about it is on the sort of seat side, we think that the seat-based model will be around for a long time for the foreseeable future. So think about it as sort of access to the.
Yeah.
To the kingdom sort of thing, a sort of base layer of functionality and that will be driven by really two things. One is that there is more work to do than there are people available to do it in our industry, so if you go.
Yeah.
And speak to your local department of transportation, ask them how much work they have to do, how much money they have to do it, and you'll see there is a huge mismatch between those two things. And then multiply that across the economy and across the globe. So there is much more work to do than there are people available to do it. So not there's pressure for more people, not less people. There just isn't the resources for those people. And secondly, as we expand into adjacent verticals, you know, our whole strategy is to connect workflows end to end in the cloud. So as we expand into construction, as we build our business in operations and maintenance and similar in manufacturing, similar in media and entertainment, we are adding new people and seats into our ecosystem. So our addressable ecosystem is actually expanding over time.
But in addition to that, as we build our AI business and deliver more value to our customers, so as we talked about at Investor Day, from task automation through workflow automation to system automation, the amount of value that we can deliver to our customers will significantly increase. And I'll talk a bit about that in a second. And also, the marginal cost will increase. And so we, they will be more consumption pricing just naturally because of that to capture that value and also to reflect the marginal cost from compute, of doing that. The value that we deliver, it really has two elements to it. The first one is around the machines can do stuff. And it'll, I guess that's the other important point is as you move up that value chain, the lead consumption will be a machine rather than a human being.
’Cause human beings just cannot handle the complexity as the models get bigger and bigger, basically, whereas machines can. So machines bring two new types of TAM to us. The first one is that they can do stuff that humans can't. So they can, to Mike's point, they can see stuff in the conceptual design phase, you know, that, you know, two years after construction that a human just being would not be able to conceive of and to bring that value into the process in a way that humans cannot do. So that is new value we can deliver to the customer that we capture through consumption pricing. And the second way is that machines create new monetizable time, because they work 24 hours a day, 365 days a year. They don't sleep. They don't go on vacation.
So it is essentially creating time that doesn't exist today. So new value, new times, new TAM to us captured with consumption.
That's interesting.
Sorry, just another thing, which you didn't ask but is important, is that there's.
Please.
The consumption will be some form of metering. To be honest, we don't precisely know what it is.
Mm-hmm.
It could be some form of token.
Got it.
It could be some form of value delivery. It could be some form of API monetization. We're not quite sure. What we do know is that Flex is basically built as a sort of universal front end. So we can plug in any sort of metering system on the front end of Flex and then have the back end essentially deliver a consumption experience through that. So we're so a lot of the work we've been doing over the last few years.
Yeah.
Has been getting us ready for this.
Yeah. Absolutely. That's really interesting in terms of how to monetize time and how that might become a bigger vector.
Mm-hmm.
Mike, maybe from just a product perspective.
Yeah.
In architecture.
Mm-hmm.
What can customers leverage now in terms of generative AI from Autodesk? And, you know, to Simon's point, just around sort of the different industry clouds, do these customers eventually need to move to Forma?
Yeah.
Right, in order to eventually kinda gain.
Yeah.
Gain those capabilities?
Yeah, so it's actually surprisingly little-known fact. We actually built an early AI tool into some of our software more than 10 years ago. You know, we have something called Construction IQ, which sort of computes.
Oh, Construction IQ.
Compute, yeah. You know, so the reality is it's actually been a bunch of stuff for a while.
Mm-hmm.
Our Innovyze, our water products have a bunch of AI in them for doing floodplain analysis and all of these kind of things, so this is all over the place in the products today. Now, what we are doing, what we launched at Autodesk University this year is our new assistant, Autodesk Assistant, which is now available across all, is becoming available across all of our products, so across industries, but it's specifically in things like Forma and Revit and these kind of products, right?
Okay.
So that is enhancing your experience across a multitude. It's the agentic sort of environment, right, for doing a bunch of stuff. Now, that is also being enhanced by the work that our team has been doing that I mentioned in the beginning. So what we also announced at Autodesk University is our Neural CAD for buildings. And that's what I was referring to earlier on in these large foundation models that you've been building.
Yep.
That can actually think in terms of buildings. They're not thinking in language. They're thinking in terms of buildings. Right? So they can compute buildings. You can interact with them, and they can literally create structures and things for you, right? Now, what we did is we showed that available in Forma because that was very much part of the sort of conceptual design workflow that I was talking about before, the rapid exploration, that kind of thing. But you can expect to see these features, many of these features also beginning to appear in products like Revit as well because depending on where they are in the lifecycle, like I said, some of these are like automation features. If they relate to documentation generation or deep design development, they're gonna be inside Revit.
If they relate to conceptual design, they will be inside something like Forma where you need them then.
Yeah. Absolutely. I wanna move to construction, right, so we talked about architecture, right?
Yeah.
The next piece, of course, right? And Simon, maybe it's for you. I mean, picking up off of architecture and moving to Autodesk Construction Cloud or ACC, can you just remind us what you've said on just the growth rates of that business? I feel like it's been very healthy and certainly reflecting market share gains. Maybe that related question then would be, why do you feel like ACC is gaining market share?
'Cause they're awesome.
There we go. Next question.
I mean, the short answer is it's the effectuation of the strategy and it's working, which is that we put in place a bunch of acquisitions a few years ago. We re-platformed it onto Build. We're doing the same thing in pre-con. The Build is the site business. We then extended our relationship with our large integrated design build companies, the high end of the market. We've been building the on-ramp in pre-con.
Mm-hmm.
Which is the bit that connects design with construction. And then we lit up our channel a couple of years ago, to go into the low end of the market. And what we talked about at Investor Day and at AU is we're also launching a sort of much more easy-to-use version for the site business as well, over time as well. And what that is then slowly doing is squeezing into the sort of middle bit of the market, which is where some of our, you know, vertical competitors operate a bit more.
Yep. Yep.
And so what you see, the result of that, and also we've rapidly iterating as well and getting better, and therefore we have reaching site, feature parity and then feature superiority. We are where we're coming head to head for a greenfield business. We're winning much more than we're losing. And then we're also beginning to get into, you know, some of the renewal businesses, some of our competitors as well. So that's quite fun too, and then also replicating that across the globe, adding in new applications like pay apps to build out the ecosystem and expect us to do more of that over time as well. So in terms of growth, we don't precisely split it out, and actually, as we integrate the construction business more tightly with the design business 'cause that's our strategy.
Yep.
It doesn't make sense to talk separately about a design and construction business. It is a connected workflow end to end in the cloud. But, you know, as a rough proxy, you know, the growth of the make business is the best rough proxy you can see externally. And in terms of new customer additions, obviously we're continuing to add a nice number of new customers too and certainly better than a number of our peers.
Yep. Absolutely. Certainly shows. Mike, maybe just on that point. I mean, construction, you know, meaning out on in the field, right?
Yeah.
Like, it's such an intense workflow, right?
Yeah.
Just in terms of the communication, all the changes that are happening.
Yeah.
Maybe a similar question we talked about with architecture earlier, but how do you kinda think about the opportunities for leveraging AI in ACC?
Yeah. Yeah. So, I mean, again, you know, like I mentioned Construction IQ, right?
Yeah.
I mean, there was a tool that, you know, we realized, you know, nearly 15 years ago that we could understand the patterns of behaviors for subs and start to, you know, we used to call it the weather report. You arrive at the construction site every morning, and it tells you where you need to put your attention on the construction site. People loved it, right? That was an early tool. This was pre the sort of deep learning era we're in now.
Now what we're seeing is, just like you said, there's so much information produced in the average construction project that just wading through and getting an AI that can actually help you focus on what you need to focus on to bring summaries, to detect changes, to predict issues that happen, this is where we focused on right now, so much of it building on everything that Simon said, all these tools, all this platform that we've built out, this gives us the sort of data nexus, if you want, to now apply AI to begin to sift through that, to give you what you need, so that our customers are loving that.
When we show them just simple integrations of language, large language models and vision language models into that that can go through a bunch of material and say, "Looks like a detected a, that picture's a leak in the basement. Something's going on. I found this other document that was published at around about the same time. You might wanna look at all these things together." Just a single occurrence like that can save a company, you know, hundreds of thousands of dollars. So just being able to bring AI on top of that massive, massive amount of data that's constantly changing.
H uge opportunity, and I just wanna connect two things that Mike said, which is that, essentially the AI within a vertical, a silo, and the value that that's what he was just talking about.
But also what he said earlier with Forma on the conceptual design phase is essentially what AI can do.
Gotcha.
is bringing what I call good enough simulation much earlier into the process.
Mm-hmm.
So that the way I think about it is it means that you start off pointing in the right direction much more, much more precisely, which means that you then avoid problems later, which costs you money and costs you time.
Absolutely.
And so if you're just in a vertical silo, you will never be able to do that.
Mm-hmm.
Yep.
That's right.
Whereas if you can connect the data with AI in the cloud using and then use AI to make those workflow and those systems automate, the automations, that's really what we're talking about. That's also, that's when I'm talking about creating new value, that's some of the things we're talking about.
The holy grail here is something called constructibility. When you're designing, it's the software needs to understand what is constructible and how to make it constructible, and that's, that's our North Star.
Yep.
That's what we're heading. You know, we've talked so much about product and AI. I think one of the things that has is helping with this, right, is actually the agentic model shift, right? Because.
Mm-hmm.
Now it creates such a so much of a closer tie-in between Autodesk and the end customer, right? Obviously, the partner's still very important, but I think there's a little bit more of a direct connection between Autodesk and the customer, and so I'd love to shift to this topic a little bit and maybe start with you, Simon, 'cause I think we're getting into the later stages of that shift. Maybe the question is just for some of us that are kinda fine-tuning our models, how should we sorta think about that revenue benefit as we go into next year?
Of course, I don't expect you to give a numerical answer exactly, but what are some of the breadcrumbs that you've given, as we think about sorta that underlying growth rate of the business, which I think has been pretty healthy, plus whatever mechanical items might be on top of that?
Okay, so just to sorta press on what you said, the way you phrased it, 'cause you said too, so it was, you know, pointed to the changes we've been making over the last few years, and with apologies to some of our long-suffering channels who've traveled with us through these changes, but the reason why we are, one of the reasons why we are years ahead of our competitors in AI is not just because we've been doing a bunch of work on the model, it's because we've been doing a bunch of enabling work to enable us to do it at scale without blowing a hole in our margins, basically.
Two of the key components of that are going more direct to your customer, which is, as you said, the New Transaction Model, but also, or partly to the New Transaction Model and also building a sizable and automating the back end of a consumption business. If you don't have those two things in addition to a subscription mechanism, then you're not gonna be able to profitably do AI at scale.
Right.
And so I know it's been painful to do, but it's one of our causes for optimism. The reason it's causes for optimism is that very few of our competitors have done any, let alone all of those things. And they're gonna have to do all of them, and they're gonna have to do them quickly. Otherwise they're gonna be in trouble. So that's one of our causes for optimism.
Mm-hmm.
That we've done those hard things, and they've already done them. As it relates to, sort of just numbers, you know, we haven't precisely said, so we've told you that by fiscal 2029, the difference between our goal of 41% and stripping out the new transaction model is about 400 basis points, and you know that we've already, by the end of this year, done 300 basis points of that headwind has come through by the end of fiscal 2026. So there's 100 basis points of headwind to margin still to come. And just math, because of the way the contra revenue, as you know, flows through into sales and marketing, a big chunk of that will come through next year. So that will allow you then to estimate what the margin headwind is next year.
And then if you gross it up, that will allow, enable you to do some math around what the revenue tailwind is, for next year. So that's probably the easiest answer, the way to answer the question. We haven't given any underlying guidance for next year. All we have said, just to repeat what I said at the beginning, which is that we will be mindful of what impact the economic cycle could have on the business, next year. And we will be mindful of the potential disruption from our sales and marketing optimization when we set guidance next year.
Absolutely. I think that's very thoughtful, and of course, maybe bringing the topic of margins back a little bit. I mean, you know, Mike, I think Autodesk went through an optimization exercise of its workforce earlier this year.
Mm-hmm.
Part of that, I think part of that exercise was to enhance resources and kinda growing.
Correct.
parts of the business like generative AI, so maybe the question for you, and it's a bit broader.
Mm-hmm.
What are the areas that you wanna be investing in 2027 as you think about?
Yeah.
Kind of that quicker product cycle if you will.
We're not giving away too much.
Yeah.
Competitive investment.
Understood. Understood.
Now, look, we began the journey in investing in platform several years ago, as you guys know. That has been where we've been putting a lot of our money, and that is where we're gonna continue putting investment, right? The platform is becoming increasingly more important in this world of agent. You'll be seeing all these agentic workflows that have appeared in this last year. If you don't have a platform, agentic workflows are meaningless because agentic workflows literally require a platform in order to function, so for us, pivoting to all of these agent-based architectures and offering agent things, we've been able to respond to this really quickly because we had a platform.
Right.
If we had 180 completely separate products with no platform behind it, we would've been toast, right? You know, continuing to invest in platform is gonna become important. Underneath that, though, data, of course, our data systems, if you don't have proper data flows, none of this stuff works, and it's the non-sexy stuff, but you've gotta get this kind of stuff out now.
Foundational.
It's foundational. Our customers expect it. So we've been working on it. We've made incredible progress. It's empowering a lot of the AI stuff that my teams are doing. And then, of course, the AI stuff. So, I mean, what you will see next year is increased investment in AI, all the different platform aspects, and then it's specifically around the sort of agentic ecosystem as well. You're gonna see a lot of investment certainly on the technology side. And.
I'm so, excuse me, so just to talk a bit about data and why that's important and how hard it is, you know, if you have.
Yeah.
On-prem data versus cloud data and all that.
Yeah.
Data accessibility.
Yeah. So, you know, I mean, there's sort of two aspects to making AI work, right? One is the sort of data side, and one is the actual physical infrastructure side you need to run AI. On the data side, you know, our customer has put an enormous amount of data into the Autodesk ecosystem, which is fantastic. So we, you know, we get to provide their data back to our customers to learn from their data, provide services to our customers, do all of this kind of work directly and make it very, very easy for them. So that's one part of making the data equation work. So where we standardize data models, that makes that flow a hell of much more easier. That not all of our customers' data is necessarily in our cloud either.
So, you know, this is where our APIs and our platform becomes incredibly important so they can now leverage our, they can still leverage our tools without necessarily having to commit to putting everything in, that the data is still flowing, you know, through our platform. So getting that entire data ecosystem right, both in the cloud from a sort of standardized architecture and then also providing these APIs is incredibly important. So that's what we've been putting in place. So that's the sort of data side of it. On the infrastructure side, the other thing we've been doing is spending a good chunk of our time on the AI side is actually building out all of the infrastructure for large-scale AI training and then, of course, for large-scale inference.
As we deliver these things, that's where you see your costs obviously climb because that's proportional to transactions and users, right?
Right.
I mean, we're an Amazon house. All of our stuff is on the Amazon architecture. I was at Amazon re:Invent two weeks ago. We are doing things on Amazon that nobody's doing right now, certainly in our space, because we are able to scale these models. We are able to train them at levels that you know, even just three years ago, we couldn't imagine being able to train things this sufficiently. But it's been a journey. We've been on the three-year journey now to really get this infrastructure in place now.
So now the good news is we have it in place, which is why you're gonna start seeing more and more AI models come out from us, not because we're suddenly inventing more, but because we've actually got this infrastructure that enables us to just pre-train more models as we go.
So that's the front end of the research getting, you know, coming down, then the productizing of it gets possible.
Right. That's right.
Right. Definitely a theme in both of your answers in terms of, "Hey, we've done a lot of the hard work that a lot of our competitors haven't.
That's correct.
right?
You gotta start that early, right?
Absolutely. Yeah. Absolutely. For sure. Simon, I wanna go back to the model, right, and just maybe to put a bow on the margin discussion. One of the things that frankly I've struggled with a little bit is, as I think about the model, is kind of the shape of margin expansion between now and fiscal 2029, right? You mentioned the target of 41%. And as you said, as Janesh said, you know, that path should be non-linear. But he's also said that fiscal 2027 should really bear the full brunt of the new model, which of course implies lower margin. So I just wanted to ask how you kinda thought about it because I kinda thought about the revenue impact should sorta be incrementally less next year, and therefore the margin impact would be incrementally less.
But I just wanna make sure I tie out my thinking with how.
Yeah. So I think your understanding of it is what we intended to communicate, which is that the impact of the new transaction model is cumulative. So by definition, the big impact, when you finish the transition from the pre-revenue cost into the sales and marketing cost, that is when the maximum impact is.
Correct.
We will be largely done with that. I mean, it'll continue to build.
Right.
But the transition will be largely done by the end of the year. So in terms of absolute number, it will be the biggest. But in terms of the increment year on year, that is much smaller than it was in 2027 than it was in 2026 versus 2025.
Yes. Yeah.
So, I think you're both correct in your understanding of how we've but I know there's been a misunderstanding that yes, it's the increment that people care about, and that is smaller next year.
Okay. So then maybe to summarize that, I mean, it seems like the dollar impact of the transition really. I mean, forgive me if I use the word peak, right? It should really kind of, you know, kinda peak next year in terms of the dollar impact, but the incremental margin impact, to your point, it's been cumulative, right? So.
Correct.
We should be thinking about.
Correct.
Okay. Understood.
Yeah.
Got it.
The peak cum incremental is this year, and then the dollar is next year. Yeah.
Understood. Understood. Very clear. Maybe I wanna just to ask one last modeling question then I wanna wrap up with Mike. I mean, just to shift to billings here, Simon. I mean, you know, we'll be finishing the invoicing transition, I think, in Q1 of 2027. So, it sounds like we should see still billings pretty healthy through Q1, but I think with long-term deferred only kinda mid- to high single digits of total deferred at the end of the year, should there be anything that really drives a difference between kinda underlying revenue and underlying billings growth going forward? Or should those two kinda start to approach each other?
I mean, the short answer is yes. That is, once you get rid of the noise, the New Transaction Model and the billings transition to annual billings, yes, billings and revenue growth should start bearing much more relationship to each other. You then get the usual noise around different cohort sizes and.
Yeah.
You know, a bit of FX in there, but substantially, thank God, billings will become much less, well, everything will become much less noisy.
Yeah.
Yeah.
Correct. Absolutely. Absolutely. Well, listen, in the last minute that we've got left, Mike, I've got a fun question for you.
Yeah.
I mean, just as we think about, you know, that cycle time is a really interesting point that I hadn't thought about, right? Would just be the amount of product development that you could be doing over the next year.
Mm-hmm.
When we're sitting here on this stage in a year from today.
Mm-hmm.
What are some of the milestones and the innovation that you wanna be talking about?
Yeah.
As we look back on calendar 2026?
Yeah. Yeah. So, you know, what I wanna be able to do is look across our industries and have the actual designers, the architects, the engineers understanding what AI tools actually look like in the future.
Mm-hmm.
Because the problem is today, most engineers and architects go and look at ChatGPT or Gemini and go, "Oh my goodness, you're gonna make a product with a big green button that says make a building. I'm gonna put in a prompt and it's gonna do that." Believe me, no architect or engineer wants that.
Yeah.
Nobody wants that. In fact, it doesn't work.
Yeah.
'Cause these things are way more complicated, as you can imagine, than that. So, but what's happening is nobody is yet producing the tools, but we are now beginning to produce those tools.
Mm-hmm.
So if we're sitting here next year, I would love to have this sort of suddenly have this vibrant ecosystem, much like we have in the public around things like ChatGPT, but in this case, it's not ChatGPT. It's a bunch of incredible AI features that are enabling architects, engineers, and designers to start creating the future world in ways that they've never been able to before, and they're leaning into these tools because they're not replacing their jobs, they're making them more efficient, they're making better products at the end of the day, and they're building on that sort of success. It's fundamentally transforming the mechanism of those industries. That's where I wanna be next. It's a big goal, but that's where I wanna be.
It sounds like it'll be a fun year though, so.
It's gonna be a fun year.
With that, I couldn't think of a better way to end. Mike, Simon.
Yeah.
Thanks so much for being with us here today.
Thanks.
Thanks, Mike. Appreciate it.
Appreciate it.