Cadence Design Systems, Inc. (NASDAQ:CDNS) Q1 2026 Earnings Call Transcript

Cadence Design Systems, Inc. (NASDAQ:CDNS) Q1 2026 Earnings Call Transcript April 27, 2026

Cadence Design Systems, Inc. beats earnings expectations. Reported EPS is $1.96, expectations were $1.89.

Operator: Ladies and gentlemen, good afternoon. My name is Abby, and I will be your conference operator today. At this time, I would like to welcome everyone to the Cadence First Quarter 2026 Earnings Conference Call. [Operator Instructions] Thank you. And I will now turn the call over to Richard Gu, Vice President of Investor Relations for Cadence. Please go ahead.

Richard Gu: Thank you, operator. I’d like to welcome everyone to our first quarter of 2026 earnings conference call. I’m joined today by Anirudh Devgan, President and Chief Executive Officer; and John Wall, Senior Vice President and Chief Financial Officer. The webcast of this call and a copy of today’s prepared remarks will be available on our website, cadence.com. Today’s discussion will contain forward-looking statements, including our outlook on future business and operating results. Due to risks and uncertainties, actual results may differ materially from those projected or implied in today’s discussion. For information on factors that could cause actual results to differ, please refer to our SEC filings, including our most recent Forms 10-K and 10-Q, CFO commentary and today’s earnings release.

All forward-looking statements during this call are based on estimates and information available to us as of today, and we disclaim any obligation to update them. In addition, all financial measures discussed on this call are non-GAAP, unless otherwise specified. The non-GAAP measures should not be considered in isolation from or as a substitute for GAAP results. Reconciliations of GAAP to non-GAAP measures are included in today’s earnings release. [Operator Instructions] Now I’ll turn the call over to Anirudh.

Anirudh Devgan: Thank you, Richard. Good afternoon, everyone, and thank you for joining us today. I’m pleased to report that Cadence had a strong start to 2026 with accelerating AI demand and disciplined execution, delivering one of the best Q1s in company’s history. Our record backlog of $8 billion was ahead of plan, reflecting strong customer confidence in our AI-driven portfolio and its pivotal role in enabling delivery of their increasingly complex chip and system design road maps. Given the accelerating momentum of our business, we are raising our 2026 revenue growth outlook to 17% and expect to achieve the Rule of 60 for the first time. John will provide more details in a moment. Agentic AI era is here, and Cadence is leading the transformation of semiconductor and system design.

At CadenceLIVE Silicon Valley 2026, we took a major step towards fully autonomous chip design, pioneering the industry’s most advanced and comprehensive agentic full-flow platform. We introduced AgentStack, the head agent framework for our AI Super Agent, which enables knowledge sharing across the design flow and extend autonomous designs from chips to 3D-IC to systems. Building on our revolutionary ChipStack AI Super Agent for RTL design and verification, we introduced two new breakthrough AI Super Agents, ViraStack for analog and custom design and InnoStack for digital implementation and signoff. Together, these solutions span the entire chip design flow, creating a connected continuous learning platform that brings the industry closer to comprehensive automation.

As the industry begins transitioning to agentic AI, the need for physically accurate and highly mathematical EDA solutions become even more critical. Our agentic AI solutions are built on decades of domain expertise, proprietary data and tightly integrated physically accurate engines, delivering high fidelity results. We continue to view our platform as a 3-layer cake, with accelerated compute and data as the base layer, principal simulation and optimization as the critical middle layer and agentic AI as the top layer. As I’ve said before, we believe the greatest value comes from the tight coupling of these layers, reinforcing each other to deliver much better results. As these super agents invoke our simulation, verification and implementation engines at scale, we expect them to materially expand EDA consumption and drive higher usage across our platforms.

We announced a strategic collaboration with Google to optimize the ChipStack AI Super Agent with Gemini on Google Cloud. By combining LLM reasoning with GCP scalable compute, this collaboration delivers a cloud-native platform for next-generation chip development. In Q1, we furthered our long standard partnership with MediaTek through a wide-ranging expansion across our new agentic AI offerings and core EDA, 3D-IC and system analysis solutions. Physical AI is emerging as the next big wave of intelligence as AI moves into autonomous systems, autos, drones and robotics, and Cadence is uniquely positioned to lead this transition. The addition of Hexagon’s D&E leading structural and multi-body dynamics technologies transforms our system analysis portfolio to a leadership position in physical AI, enabling customers to build and train fundamentally new AI word models by narrowing the critical sim-to-real gap.

At CadenceLIVE Silicon Valley, we announced an expanded partnership on AI and robotics with NVIDIA. By combining our agentic AI-driven solutions with NVIDIA’s advanced technologies, we are accelerating engineering workflows and boosting productivity across chip design, physical AI systems and hyperscale AI factories. Now let me provide an update on our businesses. Our IP business continued its strong momentum, with 22% year-over-year revenue growth driven by accelerating demand of AI, HPC and automotive workloads. Growing complexity of advanced node designs and chiplet-based architectures is driving strong demands of our differentiated Star IP portfolio across interface, memory and foundation IP. We achieved meaningful competitive wins and customer expansions at marquee accounts, reflecting the breadth of our portfolio and more importantly, the differentiated performance of our solutions.

We closed a record deal with a leading global foundry, marking our largest IP engagement with this customer to date and reinforcing our leadership at the most advanced nodes. With strong market tailwinds, focused strategy and expanding customer proliferation, we remain very well positioned for continued growth in IP. Our core EDA business delivered another strong quarter, with revenue growing 18% year-over-year, driven by increasing proliferation of our solutions at market-shaping customers. Our AI-driven solutions, and increasingly, our agentic offerings are becoming an important part of customer renewals and expansions. Demand for our hardware accelerated in Q1, resulting in our best quarter ever, led by AI HPC customers and increasing demand in automotive and robotics.

Palladium Z3 continues to be the gold standard for emulation and drove multiple competitive displacement. Momentum on verification software grew, particularly in Xcelium and Verisium SimAI, And ChipStack generated tremendous customer interest, with a large number of evaluations underway. Led by AI-driven Cadence Cerebrus solution, our digital platform continues to gain share, especially at the most advanced nodes. A global semiconductor design leader significantly increased their Innovus usage and adopted our digital signoff solutions, and a marquee AI infrastructure company expanded their usage of our signoff solutions in their leading-edge ASIC designs. In custom and analog, our AI-driven Virtuoso Studio continued its strong momentum in design migration and layer automation as it gets increasingly deployed by analog and mixed signal leaders seeking greater productivity.

Our System Design and Analysis business delivered 18% year-over-year revenue growth as AI-driven multiphysics simulation and 3D-IC become essential to addressing growing system challenges. We have strong momentum in 3D-IC, where our unified multi-die integrated design to analysis flow is helping customers address their rising chiplet and advanced packaging complexities. We also saw strong momentum in Sigrity and Clarity with multiple memory and advanced IC packaging customers expanding their deployments as they move to higher-speed interfaces. Customer adoption is increasing as they look to address signal integrity, power integrity and thermal challenges earlier in the design flow through deployment of a full Cadence signoff flow. In closing, I’m pleased with our strong execution and the broad-based momentum of our business.

As the agentic AI era unfolds, Cadence is leading the charge to realizing much higher design productivity, increasing design complexity, and the growing need for productivity is creating a compelling long-term opportunity for Cadence. With our differentiated solutions and expanding agentic AI portfolio, I believe we are very well positioned to lead this transition and continue delivering meaningful innovation and value to our customers. Now I will turn it over to John to provide more details on the Q1 results and our updated 2026 outlook.

John Wall: Thanks, Anirudh, and good afternoon, everyone. I’m pleased to report that Cadence delivered excellent results for the first quarter of 2026, with accelerating momentum and broad-based strength across all our businesses. Robust design activity, coupled with our solid execution, drove 19% year-over-year revenue growth and 45% operating margin for Q1. First quarter bookings were ahead of expectations, resulting in a record backlog of $8 billion. Here are some of the financial highlights from the first quarter, starting with the P&L. Total revenue was $1.474 billion. GAAP operating margin was 29.3%. Non-GAAP operating margin was 44.7%. GAAP EPS was $1.23, and non-GAAP EPS was $1.96. Next, turning to the balance sheet and cash flow.

Our cash balance was $1.407 billion, while the principal value of debt outstanding was $2.925 billion. Operating cash flow was $356 million. DSOs were 67 days, and we used $200 million to repurchase Cadence shares. Before I provide our updated outlook, I’d like to highlight that it contains the usual assumption that export control regulations that exist today remain substantially similar for the remainder of the year. For our updated outlook for 2026, we expect revenue in the range of $6.125 billion to $6.225 billion; GAAP operating margin in the range of 27.5% to 28.5%; non-GAAP operating margin in the range of 43.5% to 44.5%; GAAP EPS and in the range of $4.39 to $4.49; non-GAAP EPS in the range of $7.85 to $7.95; operating cash flow in the range of $1.875 billion to $1.975 billion, and we expect to use approximately 50% of our free cash flow to repurchase Cadence shares in 2026.

With that in mind, for Q2, we expect revenue in the range of $1.555 billion to $1.595 billion; GAAP operating margin in the range of 28.5% to 29.5%; non-GAAP operating margin in the range of 44.5% to 45.5%; GAAP EPS in the range of $1.07 to $1.13; and non-GAAP EPS in the range of $2.02 to $2.08. And as usual, we published a CFO commentary document on our Investor Relations website, which includes our outlook for additional items as well as further analysis and GAAP to non-GAAP reconciliations. In conclusion, Cadence is off to a strong start for the year. We are raising our 2026 revenue outlook to approximately 17% year-over-year growth. As always, I’d like to thank our customers, partners and our employees for their continued support. And with that, operator, we will now take questions.

Operator: [Operator Instructions] And our first question comes from the line of Charles Shi with Needham.

Q&A Session

Yu Shi: Anirudh, I think I have a pretty high-level question, but this is probably top of the mind for a lot of investors. We obviously learned agentic AI is probably good for EDA, good for license consumption, et cetera. But we’re still hearing some concerns around AI’s ability to actually write the software, and there are some doubts around whether AI can actually write better EDA base tools like base tools, I mean, Virtuoso, Innovus, those kind of tools. So — and obviously, there are always many EDA start-ups happening at the same time. And so the question is, is AI’s ability to write software worries you about the defensibility of the EDA base tool business? Obviously, once again, we understand agentic AI is good for consumption of the base tool business, but I want to get your thoughts.

Anirudh Devgan: Yes. Charles, thanks for the question. So I mean, there are multiple parts to this. Of course, I’m super excited about agentic AI applied to chip design and EDA. And your question is more specific to the base tool and whether AI can write those base tools. So first of all, I’m very confident in our position in the base tool and our competitive advantage, okay? And just to remind everyone, I mean, we have about 15,000 people now in Cadence and about 10,000 are in R&D. We have more than — half of them have advanced degrees. I think more than 1,000 of them have PhDs from the top universities. So we will, anyway, deploy AI internally like we are to write our software better. But I’m not worried that some other party will be able to write any better base tools.

So — and our competitor of the base tool is anyway best-in-class, and I don’t see any reason that will change going forward, okay? Now what I’m super excited that we launched in CadenceLIVE is the agentic part and the interplay of the agentic tools with the base tools, the AI orchestration combined with physical accurate base tool. And that creates new opportunities for us, both in terms of TAM expansion. Because what agentic AI allows us is to sell products in spaces we didn’t have products before, like RTL generation, verification plan generation. And those products, I think will be consumed more on a subscription plus consumption model. So this is an entirely new category for Cadence. And then in turn, like you said, agentic AI will drive more of our base tools.

So I feel pretty good about this kind of 3-layer framework we have talked about and confident going forward.

Operator: And our next question comes from the line of Jason Celino with KeyBanc Capital Markets.

Jason Celino: Maybe just a clarifying question. So I noticed that the operating margin guide is coming down by a little bit. Curious if — like what are the main drivers of that, John? I know we’re layering in kind of the Hexagon acquisition, but on like an absolute basis, it’s relatively small layering in that OpEx. So maybe you can just help us understand the guide on the margin?

John Wall: Yes. Sure, Jason. Thanks for the question. What you’re seeing there is primarily the impact of including the Hexagon design and engineering business in the current outlook. The strategic opportunity there is very large, but the 2026 P&L reflects the timing of integration that we announced in the press release when we closed the deal, that we expect $160 million of revenue this year. That’s in the guide now. We expect it to be dilutive to the tune of about $0.28. The margin impact on the $160 million is kind of in the 5% to 10% range. But the dilution comes from — because we paid 30% of the acquisition price in shares and 70% in cash, so the interest component on the — or the lost interest income on the cash causes a lot of the dilution impact in the short term.

We’d expect it to be accretive in 2027. The — yes, so I think the way to think about it is financially, 2026 is an integration year. And the guide includes the acquired cost base, the financing impact, the acquisition-related integration costs and kind of near-term dilution. And that’s why revenue moves higher, while EPS and operating margin are lower than the February guide. So yes, $160 million. And I think in Q1, the impact was slightly less on the EPS that we had about $20 million of revenue from Q1 from Hexagon. So only about $0.01 kind of dilution impact. So EPS would have been like $0.01 higher if we didn’t have Hexagon.

Operator: And our next question comes from the line of Vivek Arya with Bank of America Securities.

Vivek Arya: Anirudh, in the last year, all we have been hearing nonstop are different news about chip shortages and growing kind of price of chips and just the pricing power that many of your customers have. And my question is, what affect do shortages and the fact your customers have more pricing power, what effect does that have on their engagement with Cadence. Does it restrict chip starts? Does it shift them towards higher ASP products? Just what impact do semiconductor shortages have on your growth and engagement trajectory? What has changed? And what are you observing in your customer behavior?

Anirudh Devgan: Yes. Thanks, Vivek, for the question. So I would say a few things. So first of all, I mean the environment is pretty healthy, both for the system companies and semi companies. So that’s always good. Like you know, I mean, some of the hyperscalers and AI semi companies who are already doing well last year, but now the memory companies are doing well, even analog companies are doing well. So we, of course, want to see our customers doing well, and that creates a positive environment for engaging, especially with these new solutions we have. So that’s actually a pretty marked improvement over the last 3 to 6 months. So that’s number one. Number two, the shortages it doesn’t directly — I mean, the customer is still committed to long-term R&D road maps.

And sometimes, they may like do — like I’ve seen in a few cases, the customers, for example, may do multiple foundries or nodes to make sure there is capacity at a particular node or foundry. So that would directly lead to more design activity for us. So in general, if the customer is healthy because the revenue is going up, they will do not only more in the current designs to accelerate them, but also may start new designs. I think that’s the second thing, I would say. And third thing, which is more exciting for us is, as we have these agentic solutions, it can give more productivity for our customers, and we can deliver more value ourselves. And the more value we deliver, the more opportunity we have to capture part of that value. And the customers are very open to those discussions as there is more automation.

So we are actually — like I mentioned, there’s a lot of engagement with ChipStack and also the new AgentStack, InnoStack, ViraStack. There is no pushback at all. If we can deliver productivity, the customer is more than willing to engage. So that I would say, Vivek are the — at least the 3 broad areas I see in the current environment.

Operator: And our next question comes from the line of Jim Schneider with Goldman Sachs.

James Schneider: I was wondering if you could maybe unpack your commentary on the agentic solutions, specifically around your indication they would drive increased consumption for base tools. Can you maybe talk a little bit about the pricing for those tools, how the agentic solutions are being priced specifically? And then on net, how — if you could frame for us maybe how you might be able to capture more revenue value overall on net between agentic and conventional licenses?

Anirudh Devgan: Yes. Thanks for the question. So I think the opportunity is significant, I believe, and especially with agentic because what — and this happened over the last, let’s say, 6 to 12 months, in my opinion, and more so in 6 months is — not only the agentic tools have evolved, but agentic tools are able — we can embed skills in them so they can do a lot more automation. For example, we launched ViraStack, which is analog automation. Analog has been a long problem to automate, right? It’s very difficult to automate. But now with these agentic flows and skills, we can automate that. So what does that mean in terms of pricing or how these things are consumed? So first of all, like I said, this kind of automation was not possible before.

So all this work used to be done by the customers themselves, right? And in that case also, I talked to one big customer. Like, for example, they said for analog or even for digital, every new design, they require 2x more engineers. And anyway, it’s not — it’s like unrealizable headcount growth because they can’t hire 2x more engineers every time. So the way we plan to monetize and the early signs are positive is that, first of all, we’ll sell new tools that we never sold, which is more like this was manually done by customers like doing analog design or doing RTL. So that will be priced as a subscription plus consumption model, very similar to other kind of leading AI tools. So that’s a completely new category for Cadence. And that will kind of bend the headcount curve for our customers, but the expected headcount curve was never realizable anyway.

So this is the history of automation, as you know, in EDA. We always need to do that. But this time, we can do that with the agentic kind of AI flow. And then once the agent runs, like when a user designs a chip and this is pretty common, right? Like let’s say that chip has 100 blocks, just to keep it simple. And there are 100 engineers, 1 engineer is running 1 block. So 1 engineer will run like 1 or 2 experiments, he or she, to see which settings or which design is better. But when the agent runs those blocks, they may try 10 or 100 variations of those things. And anyway, AI does a lot more exploration than a human would do. So not only agent can give more productivity, it by nature runs more of the base tools. So that’s why if you look at — our usage of base tool is going up pretty significantly in this kind of environment.

So this is the 2 ways. And in those environments is a traditional business model, but — in the base tools, but there will be more demand for it. And then the new business model, which is more automating which was manual with agentic flows.

John Wall: Yes. And I would just add, Jim, that what we saw from Q1 is — I mean, the overall pricing environment has improved. Pricing obviously remains value-based with us. We provide tremendous value to our customers, especially with our agentic flow, and we stand to benefit from our customers’ success in that area. Also, any shift that you see from customers’ labor spend to automation, that’s likely to be irreversible and likely to accelerate over time.

Operator: And our next question comes from the line of Siti Panigrahi with Mizuho.

Sitikantha Panigrahi: Great. I want to switch to the IP business. Anirudh, you talked about IP entering now third year of strong growth. Could you give an update like what you saw in Q1? And are the HBM, LPDDR6 and all that remaining still the key drivers? Or — and the new foundry like Rapidus, Intel Foundry, are they contributing meaningfully to the IP demand yet? And John, just to clarify also on your EPS guidance, you said $0.28 dilution, but you lowered only $0.20. Just want to clarify that your organic basis, you raised by $0.08 EPS.

John Wall: I’ll take the last part first. Yes, yes, we did. We raised by $0.08.

Anirudh Devgan: And Siti it’s a great start to the year, okay? And not just in IP across the board. And I was looking at with our team. I think this is one of the strongest raises we have had in Q1. We only gave you guidance in February. So 2 months later, I think this is one of the strongest raises we have had. Now all the businesses are doing well and especially IP is off to a great start, okay? And I think it will do well going forward from what I think I see. And there are at least 3 big reasons in my mind for IP growth. And like I said, it’s the third year now. So we don’t like to talk about things too early. But after 3 years of strong growth, I think that is a good trend. So the first thing is our IP quality and performance is just better.

We have a new team, just the performance just — because these things are standard-based IPs, right, like DDR or PCIe. So the spec is same. But if our power area is better than the competitor or what the customer can do, then they will buy our IP. So the most promising thing to me is because of the strength of our R&D team, our PPA is better, and that is leading to a lot of competitive wins at pretty significant major customers. And I highlighted some of them in CadenceLIVE. So these are like really big kind of marquee names. So that gives me strength that the team is operating well. So that’s number one. Number two, our portfolio is expanding, like we have highlighted with like HBM. And some of it is organic, some of it is acquired, like HBM, we acquired from Rambus and then we improved it.

But UCIe, which is a critical chip-to-chip technology was all developed organically, okay? So the second reason is that our portfolio is expanding. The third reason is these new foundries, okay? And it’s very encouraging to see. Of course, we want to make sure we are best-in-class in TSMC, which is the leading foundry. But now there are at least 3 other major foundries with, as you know, Samsung, Intel and Rapidus at advanced nodes and then Global and others at mainstream nodes. So the amount of design activity with AI and number of increasing foundries requires more IP. So that’s why I’m actually pleased to note today like in the prepared remarks that we had a pretty significant IP deal, one of the largest ones at a leading global foundry, okay?

And just to clarify, that is not Intel, okay? We are actually pleased with our discussions with Intel, with Lip-Bu and team on 18A and especially on 14A. I think Intel realizes they need to invest more in 14A and this time, be more ready because the availability of IP and EDA solutions as 14A is critical as they go talk to their customers. So we are making very good progress with Intel, and we’ll have — soon, we’ll have more to say on our engagement with Intel. But I’m also pleased with this engagement with the other global foundry. So overall, IP growth seems robust and I’m very pleased where we are. And we’re already always very strong in EDA. But historically, last few years, we have not done as well in IP. But right now, I think we are very well positioned and also well positioned in SDA.

Operator: Our next question comes from the line of Joe Quatrochi with Wells Fargo.

Joseph Quatrochi: Maybe just to kind of follow up on the discussion earlier on EDA. I mean, I guess when you take a step back and you think about EDA’s share of R&D expense and clearly, we’re seeing an acceleration of R&D expense across a number of different companies. How should we think about EDA’s contribution to that or percent of that? And where could that go given the value maybe you’re providing from AI? Because we’re also seeing, right, memory costs are increasing, things like that, that also need to flow through that R&D line.

Anirudh Devgan: Yes, good question. And we have to observe it closely, right? You know us, we’d rather like print things than kind of predict what will happen because it’s better to show than to. But as you know, historically, we have said EDA used to be 7% of R&D and now it’s more like 11% of R&D. So it has gone up and R&D spend itself will go up significantly. But I think there is a real potential, especially with agentic AI for that 11% to go up. And all the big CEOs I talked to, they are not only willing, they want to see that happen. They want to invest in more automation and compute to make it happen. So I’m pretty sure right now, I think it will go up. Now how much it will go up, we will see, right? But I think there is a meaningful opportunity for automation to be a higher percentage of R&D plus R&D itself to go up.

Operator: Our next question comes from the line of Ruben Roy with Stifel.

Ruben Roy: John, I want to go back to the operating margin discussion. It’s great to see that you guys are targeting Rule of 60 by the end of the year here. Just thinking about that, though, it’s driven on revenue acceleration. Obviously, we’ve got the Hexagon integration costs here. But how are you thinking about the operating model relative to operating margin as you get over $6 billion in revenue? Does the operating model look a lot different than it did at $5.3 billion? Is this sort of 43% to 45% range, how we should be thinking about the operating margins? Or — and I ask that because, obviously, you’re investing in agentic AI and other sort of new product areas. Just wondering if you can give us a little bit of an idea of how you’re thinking about the operating margin structure at this revenue run rate longer term as you integrate Hexagon.

John Wall: Yes. Sure, Ruben. Thanks for the question. Yes, I think when we look at our like organic incremental margin, it’s closer to 60% these days than 50%. And as we get our arms around these acquisitions, it typically takes us 12 to 18 months to improve the profitability up to kind of something close to our expectations that Cadence that — and I would liken the profile to the way BETA. So in ’24 and ’25, you kind of had an operating margin profile where we had the dilutive impact of the BETA acquisition in ’24, but then margins improved dramatically in ’25 as we got the synergies and we got the benefits of making that more profitable. I would expect a similar pattern for ’26 and ’27 when it comes to Hexagon. We have a slight headwind in the short term, but there’s plenty of opportunities to improve the profitability there.

And also with the benefits that we’re seeing in terms of customer engagement accelerating on the agentic AI front, I think there’s even more opportunities to stretch that incremental operating margin going forward.

Operator: Our next question comes from the line of Harlan Sur with JPMorgan.

Harlan Sur: If I take your 2Q guidance and look at your implied second half guidance, the average quarterly revenue run rate in the second half is actually slightly below the 2Q level. Is there some lumpiness in the Hexagon business in the second half, maybe moving customers to multiyear license agreements? Or is it due to some lumpiness in the core business, maybe a more first half weighted hardware or IP shipment profile?

John Wall: Yes. Thanks for the question, Harlan. Yes, sure. Look, the first half is very strong. And the second half, I’d describe as containing appropriate prudence. The — your comment on Hexagon’s D&E business is correct. They are more kind of first half weighted in terms of their profile. When I looked at last year’s revenue for Hexagon, I think Q3 and Q4 were their worst 2 quarters of the year. They tend to have a lot of early year kind of dated contracts. But overall, I think the second half — I mean, it doesn’t — Hexagon doesn’t impact the first half, second half that much. It’s really — I think we had such — as Anirudh said, Q1 guide represents one of the highest raises we’ve had at this time of the year. And we normally like to wait until we have 2 quarters under our belt to raise the guide.

We couldn’t help but raise the guide given the strength of Q1 bookings and the strength we saw across the board. So we just wanted to wait until July to update the second half.

Operator: Our next question comes from the line of Lee Simpson with Morgan Stanley.

Lee Simpson: I just wanted to ask about physical AI. I mean you’ve made some pretty good acquisitions. You now announced collaborations, especially with NVIDIA. So I’m just trying to get a sense for the momentum here and what really is still the early years in this breakout. And I think, in particular, the take-up of your emulation tools, especially as it relates to closing the sim-to-real gap in robotics and probably even self-driving chips as well, whether or not that’s going to really lead to an outsized value capture for Cadence? And when do we actually see this in the numbers as well?

Anirudh Devgan: Yes. Thanks for the question, Lee. So I mean, like I talked about it forever now that we look at this thing as a 3-layer cake, right? And there are multiple slices of the cake and the first slice was data center AI or infrastructure AI. And the second big slice is physical AI. And of course, I’ve said this for 5 years now, where I believe physical AI will be bigger than data center AI by a long chart because you’re talking about like trillions of dollars of product opportunity. And it will reconfirm the data center layer — data center slice because to deploy, for example, an AI model in the car, you need to train it on the data center anyway. So I think it will even help the data center slice. Now for our portion, yes, we made this acquisition we are super excited about, and we have this training flow for word models and also more complete simulation environment.

So what is exciting about Hexagon is with a combination of our previous technologies like Millennium and Cascade and BETA, we do have finally a complete solution for physical AI in the middle layer kind of principal simulation and optimization layer. And then that can be used to do these word models, which will be different in the top layer. But the other thing I want to emphasize, apart from the SD&A and the AI part, that physical AI itself will drive a lot of silicon design. So it is also good for EDA and IP. And this is — you’re starting to see that. Of course, companies like Tesla mentioning that they don’t have enough silicon because of physical AI. So physical AI not only is good for SD and AI, it is also really good for silicon. And it also is in the sweet spot of Cadence because Cadence always had both analog and digital solutions, and that’s why we’re always good with all the major semiconductor companies for automotive and now with all the system and OEM companies for automotive.

And as that translates to drone and robots, it will also turbocharge the silicon business. That’s why I have been always been excited about physical AI, not just for the AI and SDA part, but also for EDA and IP.

Operator: Our next question comes from the line of Gianmarco Conti with Deutsche Bank.

Gianmarco Conti: Perhaps on hardware, another strong quarter, of course. But as we think about the next refresh cycle for Palladium and Protium, historically, you’ve roughly been on a 2-year cadence. And should we expect Z4, X4 within the next 12 to 18 months? Or is the bar to upgrade higher now given how recently customers absorbed the first generation? And perhaps related, are you seeing any of your own agentic AI tooling materially compress the internal hardware development time lines to the same extent that customers are reporting that same 10x productivity on RTL?

Anirudh Devgan: Yes, absolutely. Great question. So first of all, like I said, we have most of our headcount is engineering, right, whether it’s R&D or customer support. So we always want to use our own products in both our hardware groups, which is a significant design team. We do both software, hardware and all the system design in Palladium and Protium. And also, just to remind you in our IP team. It’s a great — we are working very well together, our IP team and EDA team because IP, we have so much demand. And instead of, again, increasing headcount, we are always sensitive about how much headcount. We’ll increase, and we are increasing headcount in all areas, including IP, but we can make them a lot more productive with agentic AI.

Now on the hardware part, Yes. I’m very pleased. I mean it’s a remarkable start to the year. Our competitive position is amazing. We are the only company that does its own chip, as you know. We have at least a 10-year lead in that in Palladium. And then Protium also is doing now in which we use the FPGA solution. Now just to be clear, we always design next-generation systems. And because we control the whole stack, including the system design and silicon design, one thing to remember is we will do it much faster than what the FPGA cadence will be. FPGA companies will also do next-generation FPGA designs. But because we are our own chip, we do our own design, it will be much faster than FPGA. So what that means is the lead of palladium over FPGA systems will only continue to increase as we introduce new products, okay?

But I’m not going to get into like when we’re going to introduce new products because the current products are doing amazingly well. Of course, we are designing Z4 and Z5. But what you have to remember is the current Z3 system has the capability to design 1 trillion transistor systems, okay? And right now, the biggest systems in the world are 100 billion to 200 billion transistor. So we have a lot of leeway. The industry is supposed to reach 1 trillion transistor by 2030. One thing I’ll assure you is we’ll have a Z4 system before 2030. So there is no issue of whether Z3 can handle the capacity and requirements. So we’re just happy to work with our customers. At the same time, we want to assure our investors and customers, we have a very, very good road map on hardware systems.

Operator: Our next question comes from the line of Jay Vleeschhouwer with Griffin Securities.

Jay Vleeschhouwer: Anirudh, now that you’ve completed Hexagon MSC acquisition, it would appear that you are the fourth largest non-EDA simulation company, let’s call it, industrial simulation with multiphysics. Your share is perhaps 1/10 of that total market, again, aside from EDA simulation. So the question is, now that you’ve assembled all these pieces, invested over $5 billion over the last 5 or 6 years, can you speak in some detail about what your principal technical and/or go-to-market objectives or executables are going to be for the next year or so? Synopsys talked about what they’re doing with Ansys, perhaps you could do the same for your pieces. It also seems you’re becoming a little bit more vertically integrated in go-to-market with the acquisition of a long-time channel partner. So maybe talk about some of those critical elements here to grow your revenues and share in that business.

Anirudh Devgan: Yes, Jay, that’s a lot there, right? There’s a lot there. So let me try to unpack some of it. I’m sure we can talk more if I don’t get to all the pieces there. Well, first of all, we are satisfied with the scope of our SDA business now after this acquisition. So I mean, this is rough numbers. So I think it will be roughly $1 billion of run rate. And what is more exciting to me is that it is focused in the 2 important areas of SDA. I’m a fan of SDA for a while now, I don’t know, maybe 8 years now. But not all SDA is created equal, okay? To me, we want to do the part of SDA that is either growing well or is closely related to EDA. So the part of SDA that is closely related to EDA is, of course, 3D-IC, okay? So we have an inevitable position in 3D-IC with Allegro being the leading packaging platform, and then we completed that with Clarity and Sigrity and Celsius, so all the thermal electromagnetics.

So and Integrity. So I’m pretty happy with the 3D-IC portion, which is like the closest to chip design, the part of SDA that is closest to chip design and the part that is growing the most because of AI. Now the other part now with Hexagon is all this physical AI and for design of cars and robots. So that with this acquisition is complete, and we can do a much better integration of that part of SDA. And there are multiple things happening there, okay? There are at least 2, 3 key things. So first thing is we will integrate the whole solution. I know you asked me this before, when will you integrate? So I think now that we have all the pieces of critical mass, this is the right time to integrate because we have CFD now, we have structural, we have multibody dynamics, we have pre and post, okay?

So we have a lot of effort to make a full flow solution, integrate them. And I kind of hinted at that at CadenceLIVE. The other thing, the way to integrate these solutions, which is true for EDA, but will be true in this area is agentic flow. So you will see from us an agentic flow to do system design. And that part of the market has not seen that much — it’s even worse automation than chip design that had a lot of automation. But there will be agentic flow, which will integrate all these things in a better way. The second thing we will do is that there is a lot of room for improvement of these solvers especially in our history of improving the base solvers, adding GPU acceleration, adding phys AI or AI surrogate models. So for example, there’s a potential for at least the order of magnitude improvement of performance of these new solvers.

So that’s the second thing we’ll do in terms of R&D. And third thing, what I’m also pleased with Hexagon is we did get like a good go-to-market team. That’s one area we have not been as strong because we were — most of the others was mostly organic. And we did move some of our people into go-to-market. But with Hexagon D&E business, we get a much stronger go-to-market team. And then as we mentioned, we also acquired some resellers to strengthen go-to-market, okay? At this point, I’m very confident of our R&D solution, and it will get improved by agentic solutions. It will get improved by speeding up the solvers, but we also need to invest in go-to-market and Hexagon gives us a good start. So you will see that, too. So these are the 3 kind of focus areas of improvement of SDA.

Operator: Our next question comes from the line of Kelsey Chia with Citigroup.

Wei Chia: Anirudh, you mentioned that the AgentStack helps address talent gaps for chip designers. It sounds like the AgentStack adoption is just accelerating from here. Based on your conversation, is that the case? Or are you seeing cases where customers prefer to build or use their own agentic stack versus adopting Cadence’s? And so is Cadence able to sort of charge for AgentStack or the increased base licenses as an incremental add-on within an existing fee or contract? Or is that monetization tied to renewals?

Anirudh Devgan: Yes. Thank you. There’s a lot of good questions there, okay? So make sure I — and I’ll start and John can add to that. Now first of all, I think just to be clear, the customers will always write their own agents as well, if I understand the first part of your question. Even in our pre-agentic flow, we would have given a lot of flexibilities to our customers. We had a Tcl/Tk or a Python interface to our tools, and they would always have their own flows. I mean this is natural for big customers. I mean these are who’s who of tech companies. So they always want to have some differentiation from one flow to the other. So — and that will happen in the agent word itself. So I think most of our customers are writing some of their own agents.

But the key thing is that the critical agents, okay, like these big super agents we talked about, like RTL design and verification, analog design and physical design, these are like super categories. And also the value of the agentic flow is not just in the agent itself, it’s always the coupling of the agent with the base tools because we operate the agent at a much lower level of interaction, this API calls, which is not possible for customers to do. So what has happened as an example, as we showed InnoStack or ViraStack and ChipStack to our customers, they realize, oh, there’s no point writing these kind of agents, okay? So they would rather use the super agents we have because not only we are good in agentic flow, we are good in the coupling to the base tools.

Now they will still write some agents to customize things which are specific to them, and we naturally welcome that. And then the AgentStack allows the environment to — for the customer to write its own agent, but also the customer to write its own skills. We want the customers to write their own skills in InnoStack, which may be specific for a part of design. So this has always been our strategy to be more open to customer kind of customizing their own environment, okay? And I think the second question is on renewals versus new — I mean it’s a combination of that always, John, maybe you want to comment on that.

John Wall: Yes. Yes. Thanks, Anirudh, and thanks, Kelsey. Our subscription model remains the anchor arrangement with our customers. The add-on monetization then comes incrementally through agentic workflow products that are kind of usage-based or consumption-based for capacity and through our token and card models. What’s different about agentic AI is that it doesn’t replace the core EDA engines. It calls them more often and it calls them intelligently. So the monetization opportunity is twofold really. So you’ve got like the new agentic workflow products and then you’ve got the increased usage of the underlying base tools through more exploration, more verification, more optimization and more compute. Now that said, we’re obviously being disciplined in our 2026 outlook. We’re not assuming a sudden step function in AI monetization in the guide, but we do believe agentic AI expands the long-term growth opportunity for Cadence.

Operator: Our next question comes from the line of Andrew DeGasperi with BNP Paribas.

Andrew DeGasperi: I just had a 2-part question. One is marquee — I think you called out in the prepared remarks that a marquee AI infrastructure company expanded the use of signoff solutions. I just want to clarify, was this a cloud provider? And then second, at CadenceLIVE, you discussed about physical AI in terms of the time line of adoption being around 2 years. But yet you called out that automotive and robotics companies have adopted hardware. I was just wondering, does this mean that, that physical AI time line has been brought forward? Or is this just a natural evolution of how these new markets will adopt EDA? And if so, when do we see that kind of software benefiting from that?

Anirudh Devgan: Yes. I think with physical AI and also agentic AI in general, I mean, yes, I’ve said for a long time, 2 contract cycles, and that is generally true. Though I think because of this new category of TAM expansion, which is more labor productivity related along with the base tools, I think there is a potential that the monetization of agentic AI could happen sooner than 2 contract cycles, okay? I don’t want to predict too much. And like John said, we are not putting it in our guide. But I think definitely, the more opportunities there because of all the shortages, because all the build-outs, because of physical AI. So we are — and like the previous question, we always can add in the renewal, but we always have capability to do add-ons, which we have already seen, okay?

So that’s what I would like to say. On the signoff, we are very happy. Innovus has been the leading solution for implementation, especially at TSMC and now increasingly with Samsung, Intel and Rapidus. But signoff is very — is coming on strong at TSMC and other customers. And we are working with all the leading AI players. And I think the one we mentioned specifically is a major kind of AI infrastructure/ASIC company, and we are glad to see that adoption.

Operator: Our next question comes from the line of Gary Mobley with Loop Capital.

Gary Mobley: John, I think, if I’m not mistaken, 2026 is going to be a low renewal period. By that, I mean, existing long-time customers scheduled to renew this year, kind of like 2022 was. And so was the strong bookings in the first quarter a reflection of some add-on sales as salespeople are trying to meet their quota? And do we expect that type of behavior to last through the balance of the year?

John Wall: Thanks for the question, Gary. Yes, I mean, 2026 is kind of lighter than 2025 for actual renewals on an annual value basis. But we often see that that’s — that those are some of the strongest growth years for us because of all the add-on activity. Yes, we were really, really pleased with the Q1 booking strength, and it was right across the board across all lines of business. So yes, so Gary, I mean, it bodes well for the year. But look, it’s just one quarter. As you know, we like to wait for a couple of quarters before taking up the guide in the second half. And although the last few years, Q1 has been strong, and this one has been very, very strong. So we had to take up the guide at the end of Q1.

Operator: Our next question comes from the line of Clarke Jeffries with Piper Sandler.

Clarke Jeffries: I just wanted to ask around the largest IP arrangement today with a global foundry. Was it really the extension of that agreement to additional nodes, the scope of more content or the addition of agent ready AI flows that made the biggest difference to get that to the largest arrangement you’ve ever seen?

Anirudh Devgan: Yes, that’s a particularly IP contract. So that one particular is focused on IP. And the 2 things that drove it is that it is a new node, new advanced node, more specifically 2-nanometer and more content in IP because we have a much broader portfolio.

Operator: Our next question comes from the line of Joshua Tilton with Wolfe Research.

Joshua Tilton: Maybe just a 2-parter, a little unrelated, so I apologize. But anything to call out on what drove such a strong quarter for China? And then maybe just a second part to that. Can you help us just bridge what is driving such a great organic raise for the full year relative to the organic beat in the quarter? I know you mentioned the record backlog, but is there anything one level deeper you can give us, especially in the context of — it sounds like you’re trying to tell us that even though you raised by a pretty solid amount that there still seems to be some conservatism in the guide for the second half. So any help there would be greatly appreciated.

John Wall: Sure, Josh. Thanks for the question. I’ll take this one. So Josh, yes, China, it was 13% of Q1 revenue, and that was just kind of broadly consistent with what we were expecting. Yes, we still expect China to be about 13% for the year. I think it can be lumpy from quarter-to-quarter. So I think the year-over-year comps probably look generous because Q1 in 2025 wasn’t that good in China. So the — it being 13% revenue in Q1, probably the growth rate looks strong. But it’s just — it’s a really important region for us that — yes, and we’re very, very pleased with the 13%. In relation to the guide, yes, I mean, we’re — look the, Q1 was a very strong start to the year. We exceeded all our metrics. And I guess when we back out the Hexagon, the $160 million of Hexagon and the $0.28, we’re basically raising the year by $65 million at the midpoint for revenue and about $0.08 for EPS.

Also on the cash flow front, that operating cash, the way we paid for Hexagon, the reported guide includes approximately $180 million of pre-close Hexagon tax liabilities that are economically part of the acquisition consideration, but are classified in operating cash flow. I think just the geography and the accounting forces us to put it through operating cash. If you adjust our operating cash guide, for that underlying — for that pre-close Hexagon tax liability that we’re paying, the operating cash flow outlook is approximately $2.1 billion, which would be about $100 million above our original guide. So there’s a lot of strength we saw across the businesses. So the $65 million is what we took revenue up by, but we’re seeing $100 million extra in cash that there’s potentially strength in the second half, but we thought it was too early to raise the second half right now.

Operator: And our final question comes from the line of Blair Abernethy with Rosenblatt Securities.

Blair Abernethy: Just want to ask about the Millennium platform. How is the adoption going there, Anirudh? And just in general, the health in some of your non-semi verticals like automotive, aerospace, industrial equipment and so forth. Just any commentary around that would be great.

Anirudh Devgan: Yes, absolutely. So yes, Millennium is doing great. I don’t know if you saw Jensen was there at CadenceLIVE and did a nice autograph on Millennium box. So we are pleased with the partnership with NVIDIA there. And I mean, there are 2 ways to — 2 kind of high-level applications. We are working on this kind of CFD or SDA application for a while, and that’s going well, especially in auto and also in drones, okay? So there’s a lot of — you know what Cascade acquisition we made is very good at very high accuracy CFD, which also applies to aerospace and defense. So there is autos, but also A&D is Millennium uptick, and we have several customers. Some we can talk about, some we can’t, okay? So that’s in the traditional Millennium.

And the other part, this year, like I mentioned in CadenceLIVE, we have all kinds of EDA application now on Millennium, which is super exciting. And the most exciting part of EDA application in Millennium is 3D-IC signoff because right now, the biggest issue is the complexity of these 3D-IC systems, not just to design them, which we can do in Integrity and Innovus, but to sign them off. So there’s this huge system that need to do thermal simulation, electromagnetic simulation, power delivery simulation. And they are more naturally like a matrix without getting too technical, they are closer to a matrix multiply numerical solver, which is great for GPU acceleration. So right now, I see Millennium as applying to more traditional areas like autos and then new areas like aerospace and drones and then applying to 3D-IC signoffs.

So we are super excited about the Millennium opportunity along with our traditional hardware systems.

Operator: And I will now turn the call back to Anirudh Devgan for closing remarks.

Anirudh Devgan: Thank you all for joining us this afternoon. It’s an exciting time for Cadence as we begin 2026 with product leadership and strong business momentum. And on behalf of our employees and our Board of Directors, we thank our customers, partners and investors for their continued trust and confidence in Cadence.

Operator: And ladies and gentlemen, thank you for participating in today’s Cadence First Quarter 2026 Earnings Conference Call. This concludes today’s call, and you may now disconnect. Goodbye.