... from Autodesk here. I get a stand for this session, which is great, because I'll be sitting in those chairs for about six hours today. So, from left to right, we got Sid Haksar, VP of Construction, Mike Haley, SVP of Autodesk Research, and we all know Simon from investor relations. I'm going to turn it over to Simon for the most exciting part of the presentation, which is the safe harbor.
It really is. 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, off you go.
Okay. So maybe we start with the forward-looking statements. Anything to share? No. Okay. Well, let's do intros. Mike and Sid, thank you for joining the conference.
Mm-hmm.
I think, Sid, you were here maybe two, three years ago, but just for investors in the room, if you could remind us of your background and kind of the areas that you're focused on at Autodesk.
Sure. Good morning, and glad to be here. So I'm Sid Haksar. I'm based in Boston. I lead strategy and partnerships for our construction business. I've been with the company about eleven years now. I joined Autodesk in their mergers and acquisitions group. And then the last four years have really gone into the business. So really been at the start of our journey in construction to where it is today.
Great. And Mike, how about yourself? Give us a quick overview.
Sure. Mike Haley. So I'm, as Simon said, the senior vice president of research, so I run the Autodesk Research organization, so around about a 300-person international research group, we're the one group in Autodesk that's. Our mission is to stay sort of five to 10 years ahead of the company and the industries we serve, just figure out kind of, and prepare the company for what's coming next. As part of that, several years ago, we started our AI Lab, and as a result of that, and we'll talk a bit more about this later, I'm sure, I also direct the company's overall AI work and efforts across the company.
Been a little longer than Sid, 25 years at Autodesk this year, so had a long career really focusing on emerging technologies has sort of been the theme kind of of what I've directed and led across Autodesk and prior companies before Autodesk.
Awesome. Well, great to have you here, and I'm sure a lot of exciting things to talk about. Maybe we could kick off on the AI front, because
Sure
... that's the topic of 2025 and certainly been a focus at the conference. So just at a very high level, Mike, how would you articulate Autodesk's AI strategy from here?
Mm-hmm. So across the entire design and make space, doesn't matter what industry you're in, whether you're in construction, manufacturing, or media and entertainment, there is an enormous amount of laborious, sort of tedious work that goes into most things, designing buildings, games, whatever it might be. So there's a massive opportunity to provide automation to those customers to start realizing efficiencies that they've just never been able to realize with traditional software. That's the first thing. Second thing is, you know, workflows and the way the data flows within these large, complex, you know, construction projects, for example, are a great example. Sid works a lot with that. There's an opportunity to start accelerating those, make them more approachable, make them more customizable. So that's, you know, that's another one.
The third one I would say, which is, which is one of the ones that I find, to be honest, the most exciting, is, design software. Design and make software is complex. It's complicated software. Most people that use it find it difficult to learn. It takes a while, and the result is there's probably a lot of people in the world that don't actually get to use the software because it's just so complex. With AI, the barrier to being able to use complex software is being removed.
Mm-hmm.
If you can go in and you can sketch a picture, write a prompt, or do these kinds of things, suddenly you're able to use sophisticated software. So it's almost changing the experience paradigm. So those three concepts are really at the core of sort of our AI strategy across the company.
Just to press on that last point, that means there's a lot more potential customers.
Yeah.
Yeah. Got it. Okay, and maybe talk about where Autodesk is in terms of, you know, a product perspective and in terms of giving that automation.
Mm-hmm.
What, what have you seen in terms of early traction, from, from- I know it's early
Yeah.
but
Yeah. Well, let me give a little history before that.
Sure
. then I'll get to the traction bit. So I mentioned the AI Lab, so in 2018, we established our AI Lab, and the reason we established an AI Lab is not to go and trying to do what OpenAI is doing or what Google's doing. We recognize that the nature of AI in our space is fundamentally different. The data is different. It's 3D geometry of the world, it's physics, it's how buildings are created or products are manufactured in a factory. You know, you cannot take a language model and just apply it to that and hope to get useful results, and what we discovered back in 2018 is that there was not enough places doing that elemental work.
There were not universities, there were not labs, there were not companies doing that work.
Mm-hmm.
So we had to set up our own capability. So that's a lot of what we've been focused now on for the last seven, eight years, is really building a level of competency around AI in this sort of unique space, right, of sort of data and these kind of workflows, right? So what you're beginning to see from us, and you saw this if you watched last year at Autodesk University, we announced a feature in Fusion called Auto Constrain. I'll can go into the details of that later, but it's a thing that just helps you in your design process, but it's an entirely AI-driven, using these modern sort of AI technologies. And what's fascinating is we launched that feature. We announced it, and then we launched it in February. So we're, you know, we're what?
Eight, nine months into sort of launching that, and we've just seen complete takeoff of people just loving this feature. Not only that, we've actually... It's the first of these really significant AI features. So we're improving this thing constantly. We're getting the feedback now of how customers are using it, what they like, what they don't like about it, when's the AI getting it right, when's the AI getting it wrong, right? And you're bringing that information back in, and you're self-improving the. And we've just seen radical improvements in the quality of the tool itself, right? So this is the one of the main differences with AI tools. It's not like traditional software, where you build a feature, you put that feature out there, and it's the same feature until you roll out the next piece of software.
With AI, that thing's constantly getting better. So we've I mean, just in the last eight months, I think we've rolled three different versions of that AI already, and it's the improvement is radical. So that is then further accelerating customer acceptance of the feature. So we're seeing something like 80% acceptance of AI predictions within that tool. So when it comes and says, "I think you probably want this," 80% of the time, they're going, "Yep, no, it's pretty good. I'm good." Which, for a tool that's only been out for seven months, and it's our first AI, is pretty incredible.
Right. So I guess it sounds like you're seeing a lot of uptake, initial of.
We're seeing a lot of interest, a lot of uptake. You know, one of the things I always look for with AI is, I mean, AI is a little bit of a game, right? Just, I mean, 'cause these are probabilistic systems. They will make errors at times. We all know that when we use
Mm-hmm
ChatGPT and these kind of things, right? So what you've got to understand is what is the customer acceptance of those errors? Because there, there's a balance between how much efficiency is this giving it, giving me in my work, and how many errors am I getting, and what's the sort of trade-off? So you've always got to figure out, like, what quality do I need to get the AI at in order to hit that sort of minimum threshold of sort of usefulness, and what is the expectations of the customer? And you can't go and ask a customer that question. The only way to find this out is to actually build the thing
Mm-hmm
and sort of iterate and experiment. So that is why I'm very excited about it, is what we're seeing now is actually a pretty high acceptance rate because they're getting such a big advantage.
Right. And when customers use the customers' uptake, and you see the usage of AI, what is that? What have you seen in those customers? Are they, you know, spending more time in Autodesk? Are they, you know, using more consumption? Is there any kind of downstream impact that-
Yeah
... you've seen?
Yeah, so we're definitely seeing more consumption.
Yeah.
So the result is, I mean, all of this stuff is AIs that we are providing now as services. So I mean, this is clearly. I mean, we'll probably end up talking about this later, but I mean, this is directly taking us onto a path of a consumption business. I mean, we have
Mm
consumptive parts of our business today, but relatively speaking, they're not as large as they're going to be in the future. And again, like I said, so what we are seeing, I mean, for example, on the average constraint sort of thing, you might find somebody spends maybe a week setting up, you know, a bunch of complicated constraints for a complicated design. With this tool, that's now an hour, right? So when you see that sort of type of acceleration, I mean, the fact is, what people start doing is they will actually do more because now they can make more products. They can make more versions of a product. They can try more things out.
Mm-hmm.
We end up almost, as a company, winning on both sides, right? They're actually using our tools more to do more things, and, but they're also using a consumption kind of model from us.
So let me just jump in there. So I find it useful to think about AI as a continuum, not a fixed point. So you start at the sort of smaller model end, where you're essentially doing feature automation. That's what Auto Constrain is.
Mm.
One way of verbalizing that is that it's essentially a small model, which means it's a relatively low compute, which can happen on the desktop.
Mm-hmm.
What that means is that you can deliver that functionality through the product and monetize it like a traditional subscription product, add value, capture it through price, and that's what we're doing with Auto Constrain, basically. As you move on to workflow automation, so a particular workflow, you're going onto a bigger model
Mm
and that compute will likely migrate to the cloud, and you can't put high compute, cloud, variable cost cloud computer
Right
through a subscription price point, otherwise you blow a hole in your margin. So that model will start, migrating towards a consumption.
Consumption.
As you know, we already have a significant
Mm
consumption business
Correct
with EBA and Flex. Those two workflows will likely remain human-led, for the foreseeable future. But then, when you go on to the next automation, which is system automation, where you're essentially doing cross-workflow automation, you get to a point where the complexity and size is beyond the scope of a human brain, and you will start bringing in significant amounts of machine consumption of the data as well. So, and that will also be a consumption model. So you have that sort of continuum, based on the size of compute
Mm
and also on who is leading, the consumption, a human or a machine, and also then the frequency. So because humans have to sleep, machines don't. So, you know, machines will work twenty-four hours a day, three hundred and sixty-five days a year. So that, those are sort of some of the axes that you can think of with AI.
Right. And how should we think about Autodesk's roadmap and what customers need to do to kind of progress along that?
Mm-hmm
continuum?
Yeah. So I mean, this is, you know, change doesn't happen quickly in most of our industries, so you know, there has to be a bridge, right? We can't throw the switch and absolutely every piece of software suddenly becomes a magical AI thing, 'cause, well, first off, the technology's not ready yet for that, but nor are our customers. So the nice thing of starting with the sort of, well, kind of first stage, the small AI, like what Simon's talking about, the sort of automations that I've been talking about, too, those are not fundamentally changing the work. Like, even the sort of constraint thing I was meaning
Yeah
you're still constraining a drawing. You're still making a drawing. The overall process is still the same. You've just kind of shrunk the whole thing, and you've taken out a lot of tedious work. So our strategy has been to really start to introduce those kind of tools all across our portfolio. That begins to get our customers used to using AI. It starts to build trust in AI as well, because let's face it, AI is a new thing. There's a lot of things going on in the world where people are, "if I'm gonna trust this thing, what does it mean to use AI?" So now they're starting to use it, starting to use it in these narrow contexts that don't overall
Now, what you will see us do, like again, like Simon said, as we move more to the system side and start thinking of workflow and then systems, you will see us gradually begin to expand the aperture, right? And though the nature of those tools will become more transformative on people's workflows. But that's still to come. We've got to create a bridge from where we are today of traditional software.
Mike, do you want to perhaps, as we were talking about last night at dinner
Yeah
you give us sort of things that you might be able to come in, in the future, of how you input stuff and how it works?
Yeah. So I mean, again, like I said, I mean, the idea of, you know, how people use software is really significant. And I mean, I think what you're gonna see in the future is abilities for people to, well, first off, leveraging your historical data. I mean, this has been a classic problem in manufacturing and construction, and actually media entertainment as well, for that matter. Wherever everybody starts a new project and all the historical information is archived away somewhere, and it's just not used. With AI, you actually have the ability to constantly mine all of that data. So you now start a new project, it's mined all your previous projects, and goes, "That looks a heck of a lot like the building you built seven years ago.
Do you want to bring in a bunch of the material information, cost estimates, you know, subs you used?" Bring it all in, right? So, I mean, that, that's one example of the sort of transformative effect of how this is sort of being used. But also, like I said, on the user interface side, the way people actually physically interact with the software, the way teams are brought together, is going to fundamentally change. So the ability to just sit down and use natural language with your software, have a user interface that is dynamically produced, that is giving you exactly the features you need for the job you're doing today.
You don't have to go and spend a month learning Maya, which is an incredibly complicated software package, to do a very simple little animation which you could knock together in a few hours. So it's again, it's fundamentally changing the accessibility of the software, which, to Simon's point, actually, I think is very exciting because that actually expands our market as well. So there's again, so that's why I say there has to be a bridge, because you can't go straight to this future vision of like, it's whiz bang, you know, all the software looks great. You know, there's going to be these new kind of interaction paradigms. You've got to find a path of value for our customers to get there, right?
Mike, Mike, if I may just add, with construction, we kind of break it down into a very simple... Our business is very, very simple. And you've got to talk to the end user in very simple ways. So we think of it really three, three ways: augment, automate, and analyze, right? So when you talk about automate, this is the agents that we're building
Mm
to do very mundane, manual tasks. When you think about augment, this is where we can use generative AI capabilities, whether it is drafting up an email or having a spec assistant read something and give you all the information that you need, and then you can review that, versus you having to look at a specification manual and extract the information. And then the final piece, which is analyze, which is really around predictive analytics, to what Mike said, we generate so much information, so how can we start to drive correlation, with certain events that are happening? So I think AI just unlocks this opportunity for us to really ultimately drive productivity out in the field, but then also mitigate a ton of risk in our business.
Mm-hmm. Yep.
Yeah.
Exactly.
I'd love to sort of bring in the competition angle, too, as it relates to AI. I mean, certainly, you know, a lot of software companies are under pressure, just around some of the existential and disruptive concerns around AI. You know, I think vertical software, design software, particularly where the Autodesk of the world feels a lot more insulated from that. But, I mean, you talked about kind of this idea of simplifying, you know, the user experience and gaining more users. What makes you confident that that simplification isn't going to invite more competition over time
Yeah
and that, you know, you can hold on to what
Yeah
what is a really dominant competitive position?
Yeah, yeah. I'm sure there's going to be more competition over time. You know, but I actually think the competition's probably going to come from different places. I think our traditional competitors, what we're seeing in the work that we're doing, so the lab that I mentioned that we established. I mean, we are the world's leading publisher of scientific information in the space of AI for design. I mean, we have published hundreds of papers in this area, which just shows our leadership. If you go to the average conference, we're just all over the place in it. That has given us a technology advantage, which none of our competitors, frankly, right now seem to have.
Our position in the market as well gives us the ability to not just develop that technology, but now to bring it to our customers, to start that loop that we talked about before, sort of self-improvement, understanding how customers use it, and then, of course, you've got to have data to do this. Our customers entrust us with their data, so these systems are built using their data for them, right?
Mm-hmm.
So, I mean, there's so many bits that are difficult to replicate. If you want to make AI at scale in the industries we are at, if you don't have data, if you don't have customers, and you don't have the skills of how to build that unique type of AI, it's very difficult to enter the space. So right now, we've got a lot of startups. There's a lot of startups doing kind of interesting things. They have some skills, they don't have data, and they don't have customers. Very, very, very difficult for them to get to any sort of meaningful kind of scale kind of quickly, right? Two, you have our traditional competitors out there. Most of them have been pretty slow off the mark of sort of building the kind of skills.
We're beginning to see them do it now. They're now beginning to build up AI Labs and things, but, you know, we've been at this now for eight years, so we've got quite an advantage over a lot of them. And then finally, you get the big players, the hyperscalers out there. And for them, I think you were sort of alluding to this, Tyler, I mean, this is the nature of the vertical business. It's, you know, there's some sort of safety in being a vertical business. But I will tell you also, over the years, I've done a lot of work with the hyperscalers. Their understanding of our industries is negligible because they, their entire play is to be a platform. They always want to make everything incredibly horizontal, but by definition, we're a vertical.
Our customers are looking for specific solutions to specific problems, so it's very hard for them to actually understand all the nuances, and by the way, I will just tell you, the nature of data in the design space is so much more complex
Mm-hmm
than almost any other industry out there. The nature of the data that makes up this building we're sitting in right now
Right
is incredible. So just the ability to actually grok that and understand it, and be able to build products around that, is a really unique capability that typically only sits within companies like ours.
Right.
Mike, how long do you think it would take a competitor to get to where we are today?
I think if you had a well-funded competitor that had all the bits and pieces, the data and everything, it would still take them probably four or five years to get to where we are right now.
Yeah.
Talk about the rate of change as well
Yeah, so,
that we're achieving now.
Yeah, so what's kind of interesting, you know, with AI, there's an entirely new platform and approach to this, right? So it's not that we just all of a sudden built AI, and it's all the same people doing the same thing that they were doing before, it's just AI stuff. With AI, the entire product life cycle, the entire platform technology, everything that's behind the scenes that's going into building this, is different. So not only are we building AIs right now, we're building different infrastructure. We're building different operating models and processes, and that's what we're refining. That's this is sort of like the tip of the iceberg kind of thing, right? What most people are seeing is that tip.
What they're not seeing is the large iceberg under the water, because what that is, is this is the accelerated computing initiatives that we've put in place. We've got a compute infrastructure, for example, so we run on Amazon, whereas Amazon, we largely put all of our stuff in the Amazon Web Services cloud. Amazon offers their own AI layers, their own machine learning stuff. We've built our own on top of them. We don't use Amazon stuff because our stuff is 70% more efficient than theirs, but we figured that out. We figured that out over the last four years because we want to do this stuff at massive scale, right? We've figured out our own development practices.
We have model operations groups that, like I said, with my example around Auto Constrain, are constantly working to improve the models, figure out how a customer is responding to that thing, how do we make the model better? So these it's kind of the non-sexy stuff, if you want
Mm-hmm
you know, sort of behind the scenes, but this is the stuff that really counts. This is what makes you good at doing these things. And this is the... to what I, my answer to you, Simon, this is what really creates the difference. It's not just that we know some magical, you know, formula to how to do this. We've done the work.
Right.
We've hit all the bumps along the road. We've figured out what it needed to be smoothed over.
Okay. And I think maybe last question on this topic, because it's obviously we could talk about this all session, but you know, the importance of the data, right?
Yeah.
And the data model you highlighted, but for, you know, for Autodesk, help us understand where we are just from that data model and cloud transition perspective, right? Because I know this is something that was announced at AU, you know, around the Fusion and Forma and whatnot, but feels like we're still on that journey. So is that still kind of a precursor or a roadblock to AI adoption?
It's far more of a precursor.
Yeah.
So you know, I mean, I will tell you, the AI stuff, the sort of order of operations is sort of cloud happened, then we realized we had to do the data thing, then you have the AI thing, sort of in that order.
Mm-hmm.
Right? You can't do the data thing if the data's not in the cloud, but once it's in the cloud, you've got to get that data organized if you want the AI to really be sort of effective, and, you know, and by the way, you're not just doing the data thing for AI. Getting data organized and sufficiently granular across our industries is incredibly important, so let me explain to you what I mean, so across every one of our industries, again, you always have multiple parties involved in every single project, and data is going to flow between those folks. They all need to work on different aspects. If you're an engineer working on the HVAC system in this building, you don't need to be accessing the entire building.
You need to be working on the HVAC system, but that's not how it's historically worked. There's a big file and a big bunch of data that represents the building, and now I don't know what Sid's going to be doing this or this, or Simon's going to be doing with this, and it's really hard for me as a project administrator to be controlling that. So even something like that, what I want to be doing is, if Sid's my HVAC guy, I want to say, "Hey, Sid, here- I'm just going to take out the HVAC system, send it over to you. You do your thing." And I know that he's got the access he needs, but it's sort of isolated. Now, to do that, you can't do that in a traditional file-based kind of workflow. That just doesn't work.
You have to basically take this massively complicated building and disaggregate it down into these little, sort of smaller, granular pieces, and then feed them back out. So that's fundamentally what we've been doing. We've been looking at ways that we can take these representations of these complex things in the world and break them down into these elemental components. That then allows me to move those elemental components around very efficiently. I can share them with people. I can translate them into other systems. I can create automatic workflows that says every time Sid moves the ductwork, it's going to let the architect know that he now needs to maybe shift a wall or change a soffit or something like that.
Mm-hmm.
Right? So, having that granular sort of data set is incredibly important. It's also incredibly difficult.
Mm-hmm.
Because, like I said, I was saying earlier
Mm
that the data in our industry is unbelievably sophisticated data. So this is a journey. We've been on this journey now for, gosh, at least five years, sort of building out this stuff. We will be on the journey for several more years. The results are showing. You're seeing it now. If you go to AU, like you said, we've been talking about it a lot at AU. We've shown you customer examples of our customers that's beginning to leverage these data models. So as it becomes more and more expansive, you're seeing more and more transformation. But to your point, Tyler, at the same time, what that's doing is that's creating all of the data flows and everything we need for the AI side as well.
So it's really been a great enabler for us, but it's enabling a broader-
You'll see more at AU, and
You will see.
Yeah.
Yeah. Yeah, and I, I'll just say, you know, AU this year is going to be a big one.
Mm.
We will be speaking about a lot of the AI things that I'm sort of been talking about, us working on.
Yeah.
So if you're there, don't miss that.
Couple weeks away, right?
Couple of weeks away.
Yeah.
Yeah, we're prepping for it.
Awesome. Sid, and maybe this conversation is also for Mike, but just on. We get a lot of questions on Autodesk M&A strategy. Obviously, there were press reports about one of your Boston companies. I know you're based in Boston, but how do you think about Autodesk's M&A strategy?
I know you've been in that as well.
I mean, basically to repeat what Andrew said on the call, which is it's the same as it's always been, which is we're investing in the future to accelerate growth in the business, in adjacent verticals, primarily, in terms of, relatively large amounts of capital. You know, think in construction, think in enterprise, et cetera. Or small amounts of capital, you're typically doing, you know, tech tuck-ins or features like, you know, Paya pps in construction, et cetera. But that's also in the context of a broader capital allocation strategy of investing significantly in R&D, in things like AI, for example, and also, repurchasing shares and bringing the share count down gradually over time.
Mm.
And in terms of size, as Andrew said, it will range from anything from, you know, $200,000 into the billions of dollars, but it will not be in the tens of billions of dollars.
Right. Right. Okay. And Sid, let's talk about the construction side of the business. Just high level, what are the biggest challenges that you're hearing from the construction clients today? And, you know, how's Autodesk helping them?
Yeah. So I think I spend a lot of time talking to customers as part of probably the funnest part of my job. The three things I think they unanimously keep hearing, one is obviously, you've probably heard this as well, labor shortages. There's a big inflection point over the next three years, where you're going to have almost 40% of the labor force retire, and we're not being able to hire people fast enough. Though I do have a thesis that with all the AI we're chatting with Mike and Simon about it, with all the AI starting to take away some of the jobs, I do believe there'll be a renaissance of the craft force.
Mm.
So, I do believe people will start to go back into
Mm-hmm
trade schools and actually build stuff.
Coders to construction.
I think there's going to be, you know, because you can't... At the end of the day, when you install an HVAC, you still need people to do that. So I think that's a big piece. I think supply chain has been a huge problem for companies. They're having to really reassess their supply chain and their agreements, and they're getting squeezed on margins. And then the third piece, I probably would say, is interest rates, especially when dealing with owners. I think some segments are more sensitive to others. Obviously, multifamily residential has taken a big hit, because, obviously, developers are reassessing
Mm-hmm
you know, their P&Ls, their pro formas, in light of
Right
increased capital costs.
Right. And you know, last call, the obviously pretty strong beat and raise across the board. You know, there was specific call-outs to, you know, I'd say, the areas of strength in the design and construction space, whether that's, you know, infrastructure projects, data centers, right? So I'd just be curious. Clearly, there's, it's a mixed bag out there. You talked about, you know, multifamily homes, you know, commercial real estate broadly is under a lot of pressure. But are you kind of seeing those positive factors, you know, maybe greater outweigh those headwinds or perhaps more than you expected? Or is it kind of a wash at the end of the day?
I think backlogs for our customers are still pretty strong. I'll say there are certain pockets that puts and takes, but I think the areas that we're seeing that outside of data centers, hospitals, education, schools are seeing a lot of growth, advanced manufacturing. So whether that's, and obviously, a lot of the reshoring that's happening-
Mm.
That's benefiting our customers. And then weakness or areas. And then, obviously, we are seeing on the public side, transportation is roads, highways, bridges are seeing some good traction there. So overall, I think, yeah, the outlook generally looks. You know, when you start to put that all in a blender and see how it all comes out, it's still very positive.
Mm
... for our customers.
Right. Got it. And you, you'd expect sort of those backlog numbers. I mean, they've held up for years. I mean, you're not seeing any signs of those kind of compressing or-
No
- deteriorating?
No, I mean, so we hear. And I know one of our competitors talks about a macro impact. Our customers, we're not seeing that, to be honest.
Mm.
So when we talk to them, we still see optimism. Obviously, there are headwinds. They're navigating a lot of it as things fundamentally out of their control, as I said, with supply chain, and they're navigating with that, with their stakeholders, with the owners, in terms of how they structure their fee arrangements. But I'll say there's a lot of work to be done. Labor is in short supply.
Right
... so.
Speaking of competition, how would you assess the Autodesk product portfolio on the construction side relative to your competitors? Clearly, it's been, you know, a lot of work you've put into the product suite with the various acquisitions over the years. But do you feel like it's at parity or better at this point? Just kind of highlight that.
Yeah
... differentiation.
I will say we have done a lot of acquisitions. There has been a method to the madness. On the outside, it may just look like there's a lot of companies we've been picking up, but we've been doing it in a very, I'd say, sequential and deliberate way. You know, it's been a six-year journey for us. And where we stand today, we feel extremely confident. It's really fun and rewarding to see customers of incumbents coming to us wanting to change. Because, you know, construction, change management is a big deal, so it needs to be a lot better, and it needs to be a lot cheaper for someone to make the switch.
Mm.
We are in that state now, where we are definitely. You know, a lot of our customers, we talk about this. You start in Autodesk, you start in our authoring tools, our modeling tools, but then there are breaks along the life cycle of that project. A lot of it was because we were not ready yet with a comprehensive end-to-end solution. We are today.
Mm-hmm.
So it's a lot of fun being in my seat now, because we have had to do a lot of blocking and tackling over the past year, few years, until we got our platform ready. So Construction Cloud now is resonating really strongly, and I'm sure you'll be seeing a lot of the wins that we have of customers that are coming to us from some of these incumbents with whom they've been for several years.
Yeah. And just as we think about the growth trajectory of Construction Cloud, you know, we don't get perfect disclosure on it every quarter. We know it's largely within the Make business, but, you know, maybe it sort of gets dragged into AE, AEC as well. But how would you just sort of frame that growth trajectory? Are there accelerants that you see ahead, whether it's some of this macro positivity on the infrastructure side, competition, you know, pricing, AI monetization? Just help us understand the puts and takes on that growth rate.
Yeah, I'll take it. I mean, it's obviously... So in terms of size, just because it's complicated, because it's, like, overcomplicating it, part of construction is in the design business, but most of it is in Make.
Yeah.
But if you took the design bit of construction and put it in Make and took the non-construction bit of Make out, then you'd end up with roughly the same size as Make.
Mm-hmm.
So the construction business in total is about the same size as Make. You know, it is by far the largest part of the Make business. So at some point, the construction business has to grow in the zip code of the Make business, which is why we say.
Mm-hmm.
So it's, you know, in that sort of, you know, Make is growing around 20%
20%. Mm
. and construction has to be somewhere in that zip code. And then the other important thing is we haven't seen any deceleration in growth. You know, actually, construction business, you know, grew a fraction faster in Q2 than it did in Q1 on the sort of revenue side. So, you know, continues to grow very, very nicely and is doing very, very well. And in terms of the drivers, you know, Sid, do you want to sort of talk about that in terms of, you know, how we're keeping the business chugging along?
Yeah, so I think international for us is a big driver.
Mm-hmm.
I'll just call that out there, right? India, I'll just say, so India is the third-largest construction market globally now. We've got a really good presence there. I think we think of that market as a very big market for us. The Middle East as well, we're seeing some tremendous traction there. So international is going to be a big focus. The other place, if you just think about our journey, we've gone from effectively being a point solution, right? Our acquisition of PlanGrid. PlanGrid was a point solution for the field. If you think of today, Autodesk Construction Cloud, and within that, Autodesk Build, which effectively gives you both the field workflows but also office workflows.
We are now coming into a market, and when you think of the market, it's very big, but then you think of the ENR 400. They're like the 400 largest general contractors that has had some level of penetration by one of our customers, right? Competitors. And those are long sales cycles when you start to come in there, but once you, once you get into that space, those are large AS
Mm-hmm
you know, ACV deals, right?
Mm-hmm.
So we do think we are seeing a lot of good momentum in the ENR 400, I'd say ENR 1,000, broadly.
Mm-hmm.
The other area that we're seeing some really good traction, if you think about it, is owners. So owners are becoming a lot more accountable. We think of them as digital drivers. Historically, they've had a very hands-off approach, but now with everything that's happening, and fundamentally, at the end of the day, if a project is delayed, they're the ones that are paying for it at the end of... You know, when all is said and done. So they're taking a lot of more accountability, so we're driving a lot more traction with owners.
Great.
You'll see some, you know, announcements coming out soon enough in terms of the wins we are having with these owners. Then the final piece for us is really the specialty contractors.
Mm-hmm.
We don't talk enough about them, right? They're the ones that are actually making the physical product, and they are already in our design tools, heavily using our Revit for modeling purposes, and so capturing them with purpose-built solutions. I think then that enables us to capture that entire ecosystem of stakeholders on a project.
Great. Well, I think we are out of time. Really appreciate the discussion, and looking forward to going to Autodesk University, and then Investor Day. You also have
Yeah
... coming up next, next week, so you'll be busy.
October 7th, not next week. Oh, my God.
Yeah. Next, next month. Sorry, yeah. All right, thank you very much.
Thank you, everyone.
Thanks, Sanjay.