Cadence Design Systems (NASDAQ:CDNS) reported third-quarter financial results on Monday. The transcript from the company’s third-quarter earnings call has been provided below.
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View the webcast at https://events.q4inc.com/attendee/503798832
Summary
Cadence Design Systems reported strong Q3 2026 results, with revenue growing 22% year over year and all key metrics exceeding guidance. The company ended the quarter with a record backlog of $8.2 billion.
The company raised its 2026 revenue growth outlook to 21% and EPS outlook by $0.30 to $8.40, driven by strong AI growth opportunities and expanding technology leadership.
Cadence introduced Maestro, a portfolio of domain-specialized AI models for chip and system design, which has shown compelling early results and strong customer traction.
Recurring revenue grew by 18% year over year, and hardware-assisted verification saw unprecedented demand, contributing to record performance for the Palladium and Protium platforms.
The company authorized an additional $2.5 billion for its share repurchase program, reflecting confidence in its business and commitment to returning capital to shareholders.
Full Transcript
OPERATOR
Simply press star then the number one on your telephone keypad. Thank you. I will now turn the call over to Richard Gu, Vice President of Investor Relations for Cadence Design Systems. Please go ahead.
Richard Gu (Vice President of Investor Relations)
Thank you, operator. I’d like to welcome everyone to our third 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. For the Q&A session today, I would ask that you observe a limit of one question only. If time permits, you can requeue with additional questions. Now I’ll turn the call over to Anirudh.
Anirudh Devgan (President and Chief Executive Officer)
Thank you, Richard. Good afternoon, everyone, and thank you for joining us today. I’m pleased to report that Cadence delivered another outstanding quarter with all key metrics coming in well above our guidance. Backlog reached a record of $8.2 billion, reflecting strong bookings driven by growing customer demand. Given our strong business momentum, expanding technology leadership, and compelling AI growth opportunities, we are raising our revenue outlook for the year to 21% growth and our EPS outlook by $0.30 to $8.40.
John will provide more details on both our Q3 results and the updated financial outlook. Agentic AI represents a transformational market opportunity and Cadence is exceptionally well positioned to capitalize on it. Our differentiated three-layer framework is anchored by our physically accurate simulation and optimization engines in the critical middle layer, tightly integrated with AI agents and orchestration at the top and accelerated compute and proprietary data at the base. Agentic AI continues to evolve at a remarkable pace. With our AI super agents gaining strong traction, we are now addressing the long-standing customer demand for domain-specialized models.
We believe the future of agentic AI-based design is increasingly domain-specialized models combined with intelligent model routing for performance and reinforcement learning loops with underlying tools for recursive self-improvement. Today I’m excited to introduce Maestro, a portfolio of domain-specialized AI models purpose-built for chip and system design. By controlling all three layers—our AI super agents, Maestro models, and proven EDA and SDA tools—we are uniquely positioned to deliver optimal performance, efficiency, and throughput results for our customers.
Initial results are very compelling, demonstrating up to 30% higher quality and up to 5x greater token efficiency versus general-purpose foundation models. Moreover, the Maestro models can be further post-trained on customers’ proprietary data to deliver even better outcomes. Our model router chooses among our models and other leading frontier and open-weight models as needed. For optimal results, customers can opt to deploy Maestro models in the cloud or in their on-prem environment.
Maestro is gaining strong early customer traction with several deployments underway. Momentum of our AI super agents continues to accelerate with compelling early results and more than 100 active customer engagements. We added a new agent to Chip Stack for RTL generation and early PPA optimization that created RTL, delivering an average of 24% area and 18% power reduction versus pure foundation model generated code. Importantly, this demonstrates the power of combining our AI super agents with AI models and our underlying EDA engines to achieve outcomes that foundation models alone cannot deliver.
Honda is using the RTL generation agent and AI-powered automation to improve productivity from specification to RTL development. In Q3, MediaTek deployed Chip Stack into its production digital verification flow, achieving 50% to 70% improvement in verification coverage and 1.4x to 2x reduction in engineering effort. With deployment now scaling across design teams, in analog and custom design, Vera Stack saw a 4x increase in customer engagement with deployments in various production designs at multiple customers. The combination of agentic workflow, embedded skills, and our Virtuoso platform is unlocking significant new productivity opportunities in a traditionally difficult to automate domain. We are seeing strong customer interest in InnoStack, our digital AI super agent, as customers push to improve PPA, accelerate design closure, and boost productivity.
In Q3, the Renesas MCU team successfully deployed InnoStack across a wide range of engineering tasks, achieving a 34x productivity improvement, with plans to expand its usage to layout automation. Also in Q3, Samsung Foundry successfully used InnoStack to dramatically improve engineering efficiency and accelerate PPA exploration, reshaping its approach to technology migration and DTCO optimization. SocionX is deploying InnoStack in its production design environment and expects its combination with our advanced physical design tools to deliver up to a 5x overall productivity gain while maintaining design quality.
In PCB and 3DIC design, the early response to our Aura Stack AI super agent has been overwhelmingly positive. Nokia is deploying Aura Stack on our Millennium supercomputer to advance electronic system design at scale. By unifying agentic AI, our physics-based solvers, and accelerated compute in a secure on-prem platform, Nokia is achieving a 5x acceleration in key design and analysis workflows. Moving on to our businesses, we are delighted that all product groups delivered outstanding results, with year-over-year revenue growth of 20% in Core EDA, 24% in IP, and 30% in SD&A. Accelerating design activity continues to drive strong demand for our Core EDA, SDA, and IP products. At the same time, we are seeing amazing customer momentum for our agentic AI portfolio, which is also fueling increased demand for our underlying products. We are pleased that recurring revenue sustained its strong momentum, growing 18% year over year.
I’m particularly proud of the significant, wide-ranging agreements we have signed with top customers. We furthered our long-standing partnership with a global marquee systems customer through a significant, wide-ranging expansion of our Core EDA, hardware, and SDA solutions. In Q3, Cadence and a top telco deepened their long-term strategic partnership with a broad expansion across our AI offerings, EDA software, hardware, and SDA solutions, establishing Cadence as their primary design partner.
Core EDA revenue growth was driven by broad-based strength across the portfolio. Our digital and signoff business continued to expand, achieving 16 new full-flow wins during the quarter. As agentic flows increasingly require full vertical workflows, there is growing demand for our synthesis and signoff solutions. Our signoff products saw growing adoption, and our verification software products had strong growth in big digital and memory design, with increasing usage at major hyperscaler and memory companies.
As AI and HPC designs continue to grow in scale and complexity, hardware-assisted verification has become an increasingly strategic capability for our customers. This is driving sustained, unprecedented demand for our Palladium and Protium emulation platforms, which delivered yet another record quarter. We added several new logos and saw meaningful expansion with marquee customers across a wide range of verticals. Our hardware cloud offering showed strong growth, with a leading semiconductor company adopting more hardware cloud services to accelerate new product development.
In analog and custom design, Spectre FX continues to gain momentum, highlighted by a major comparative displacement at a marquee infrastructure silicon provider, with multiple new 2nm projects adopting our platform. Our IP business delivered another outstanding quarter driven by deepening engagements with leading hyperscalers and semi customers. We continue to benefit from strong AI-driven tailwinds fueling accelerating AI infrastructure investments, growing adoption of custom silicon, and increasing demand for high-performance IP.
Along with the transition to advanced nodes and chiplet-based architectures, these AI-driven trends are creating a strong pull for a differentiated IP portfolio across interface, memory, and foundation IP. We are well positioned for sustained growth as we continue to deliver superior power, performance, and area across our expanding advanced IP portfolio. A leading space technology company selected our IP portfolio for their custom chip program, paving the way for a broader services and silicon engagement.
Tensilica delivered a record revenue quarter, and we announced a joint development with Analog Devices across next-generation audio applications. Our System Design and Analysis business delivered another strong quarter driven by growing adoption of our integrated solutions and proliferation of our expanded electronics and physical systems intelligence portfolio. Allegro momentum continued, including a competitive displacement at a leading EV customer that chose Cadence’s integrated PCB design and multiphysics analysis platform as its primary solution. Our Millennium platform had strong adoption driven by deployment of our GPU-accelerated electronic and mechanical system suite along with our Reality digital twin solution.
During the quarter, Switch, a leader in sustainable high-density AI infrastructure, selected our Millennium and Reality Digital Twin solutions to advance the design and simulation of its next-generation AI factories. The MSC business is an integral part of our physical AI strategy, and its integration is progressing well. MSC Adams, our industry-leading multibody dynamics solution, helped secure a competitive displacement at a leading power company, along with adoption of other Cadence products.
In summary, Cadence delivered an exceptional Q3 and I’m delighted with the accelerating momentum of our business. We are executing extremely well in a vibrant AI market, have our strongest competitive position ever, and are extending our leadership in AI. Our highly recurring and ratable business model, combined with growing customer R&D investments, is positioning us very well for continued strength in this dynamic AI environment. With that, I will turn it over to John to provide more details on our Q3 results and our updated 2026 outlook.
John Wall (Chief Financial Officer)
Thanks, Anirudh, and good afternoon everyone. Cadence delivered strong third quarter results driven by robust demand across our businesses and growing momentum from AI-driven designs. Growth is accelerating and broadening. Q3 revenue grew 22% year over year with strong double-digit growth across all product groups. We exceeded our guidance on every key financial metric and ended the quarter with record backlog of $8.2 billion. Here are some of the financial highlights from the third quarter, starting with the P&L. Total revenue was $1,600,633,000. GAAP operating margin was 26.2%; non-GAAP operating margin was 44.1%. GAAP EPS was $1.05; non-GAAP EPS was $2.11.
Next, turning to the balance sheet and cash flow. Our cash balance was $1,452,000,000, while the principal value of debt outstanding was $2,650,000,000. Operating cash flow was $424,000,000. DSOs were 66 days, and we used $400,000,000 to repurchase Cadence Design Systems shares during the quarter, reflecting our confidence in the business and our commitment to returning capital to shareholders. Underscoring that commitment, our board has authorized an additional $2.5 billion for our share repurchase program.
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 now expect revenue in the range of $6,387,000,000 to $6,417,000,000; GAAP operating margin in the range of 28.2% to 29.2%; non-GAAP operating margin in the range of 44.2% to 45.2%; GAAP EPS in the range of $4.84 to $4.90; non-GAAP EPS in the range of $8.37 to $8.43; operating cash flow of approximately $2 billion; and we expect to use more than 50% of our free cash flow to repurchase Cadence Design Systems shares in 2026.
For Q4, we expect revenue in the range of $1,695,000,000 to $1,725,000,000; GAAP operating margin in the range of 30% to 31%; non-GAAP operating margin in the range of 44% to 45%; GAAP EPS in the range of $1.23 to $1.29; and non-GAAP EPS in the range of $2.19 to $2.25. 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.
Overall, I’m pleased with our Q3 results, especially our recurring revenue growth of 18% year over year. At the midpoint of our updated 2026 outlook, we now expect revenue growth of 21%, operating margin of 44.7%, and EPS of $8.40. As always, I’d like to close by thanking our customers, partners, and our employees for their continued support, and with that, operator, we will now take questions.
OPERATOR
Thank you. At this time I would like to remind everyone who wants to ask a question to please press star, then the number one on your telephone keypad. To withdraw your question, press star one. Again, we ask that you pick up your handset when asking a question to allow for optimum sound quality. And if you’re muted locally, please remember to unmute your device. As a courtesy to all participants, we ask that you limit yourself to one question. We will pause for a moment to compile the Q&A roster.
Your first question comes from the line of Jason Salino from KeyBanc Capital Markets. Please go ahead.
Jason Salino (Analyst at KeyBanc Capital Markets)
Hey, thank you for taking my question, Anirudh. You know, really nice to see the Maestro announcement. You know, I’m not an EDA engineer, I’m not an AI scientist, so maybe can you just explain what this is a little bit better? It sounds like it’s part model router, but also maybe domain-specific models. So is Cadence developing these domain-specific models? Maybe just elaborate and then how monetization of this new product will work. Thank you.
Anirudh Devgan (President and Chief Executive Officer)
Yeah. Hi Jason, thank you for the question. Yeah, there’s a lot in that release, so let me try to unpack it a little bit. First of all, we are very pleased with the momentum of our agent stack and all four super agents. You can see there are more than 100 engagements now as we work with customers. Over the last six to nine months, several trends became pretty clear. And to me, we are launching Maestro as what I would call the next generation of agents, and there are three big factors in the next generation of agents.
One is, like what has happened in the last one year, that of course we worked with all the frontier labs from the very beginning, whether they’re doing chip design themselves or we are using their LLMs. Over the last one year, there is this emergence of very, very good open-weight models. And I always wanted to do domain-specialized models, but now it’s finally possible with open-weight models. And it’s actually very cost-effective to post-train these models to deliver domain intelligence, which is remarkable. We’re getting up to 30% better quality by using our tools and data to post-train these models. And also you don’t need one size fits all; it’s a portfolio of models, from small decision LLMs to small and medium LLMs, all the way up to frontier-class open-weight models post-trained on our data and intelligence.
So that’s the first part of Maestro. It gives real customization for chip and system design and gives much, much better results. It’s actually quite remarkable how much these models can be improved by post-training. Now the second part is that in our agents, when they do these tasks—we have a variety of tasks—we don’t need only one size or type of model to do the task. So the second part is model routing. Just like you don’t need to use the airplane to go to your local grocery store, similarly with agents you have a variety of tasks that we need to do, from simple tasks to deep thinking and reasoning tasks. So the model router will, knowing what the task is from our super agents, use the right model, and that can easily give up to 5x improvement in token efficiency—so either much lower cost or much more work for the same cost.
And then the third thing, which I always talked about, is this reinforcement learning loop with the underlying tools, and we have a great example. For example, we talked about our new RTL agent that one of the customers publicly endorsed—Honda—gave 24% area and 18% power improvements because the integration with the underlying tools and how we call them, and continuous self-improvement, whether it’s skills or models, gives us a unique advantage. So if you combine these three things together—domain-specialized models, domain-specialized family of models plus model routing, plus deep integration with our tools and data—it gives a unique differentiation that nobody else can do. And it also allows us to control all three layers of the cake and innovate in all three layers of the cake. So I’m actually super excited, and I think this is what’s going to happen in all industries as these LLMs mature: domain-specialized intelligence, model routing, and integration with underlying engines.
OPERATOR
Your next question comes from the line of Jim Schneider from Goldman Sachs. Please go ahead.
Jim Schneider (Analyst at Goldman Sachs)
Good afternoon. Thanks for taking my question. I was wondering if you could maybe give us an update on the monetization of the agentic AI solutions you’re seeing. I believe last quarter you talked about recurring revenue growth of 20%, this quarter was 18%. Maybe talk about what the difference there in the recurring was and, underlying that, whether there was any acceleration in the agentic part of that recurring revenue. Thank you.
Anirudh Devgan (President and Chief Executive Officer)
Yeah, thanks for the question. I’ll start and then John can comment. I’m very proud of the recurring growth, and I mean this quarter there were some specific things—like if you compare it to Q3 of last year there were some specific things with China revenue moving around, which is not there this year. But in general, recurring revenue is doing phenomenally well. And if you step back and compare, last year recurring revenue was 11% and it’s 18% for this quarter, and it was very good last quarter.
So I expect recurring revenue to do very well in 2026. And this is a result of of course our agentic AI solutions, but also agentic AI solutions driving more usage of our base tools, which, as we always said, is going to happen. So that’s what we see: a lot of engagement is agentic and a lot of usage of our base tools. So that provides a lot of growth for recurring, and then you add that with our ongoing strength in IP and hardware and you get a very, very good set of results.
John Wall (Chief Financial Officer)
Yeah, I’d agree with that, Anirudh. I mean it basically reflects strong demand across our software portfolio and healthy customer expansions and continued strength in core EDA. Yeah, I think that’s the way to look at it in terms of strong IP growth and recurring revenue. We’re very pleased with it.
OPERATOR
Your next question comes from the line of Vivek Arya from Bank of America Securities. Please go ahead.
Vivek Arya (Analyst at BofA Securities)
Thanks for taking my question, Anirudh. We recently heard three things from your competitor: that they can grow sales kind of out of a mid-teens base next year and then compound it to 30; that they can achieve 50% operating margins; and then also, interestingly, there is a royalty-based model that can help add nearly a billion dollars to their top line over the next several years. So I realize this is not the call to give a multi-year growth outlook, but I was just hoping to get your perspective on how you envision sort of this growth and royalty component for Cadence and how well is Cadence positioned to maintain or expand your market share and your leading operating margins over time. Thank you.
Anirudh Devgan (President and Chief Executive Officer)
Yeah, thanks for the question, Vivek. So overall, I’m very pleased with our growth and not just growth, but profitability and margin. And you know we always talk about rule of 40—growth plus margin. So I think we will end the year, if you look at our guide, about 21% revenue growth and slightly less than 45% margin. So we will end up north of 65 in 2026. That’s the important thing to point out, and we still have one more quarter to go, so we’ll see.
It’s an important quarter for us—fourth quarter is always important—so we’ll see how that finishes, and we can update you like we usually do in February for the next year. But overall the environment, I think, is very positive for us. As you know, the AI build-out continues, and the amount of design activity in all aspects of AI—and even the traditional companies in analog, mixed-signal companies, digital companies, memory companies, system companies—the amount of design activity is the strongest that I have seen. And we’re gaining share in almost all segments of our business, whether it’s IP or hardware or EDA, agentic AI.
So our product portfolio, I think, is the strongest it has ever been. And then we have this huge opportunity with agentic AI, and I think we are redoing how agentic AI will be defined for all industries with our own domain-specialized models and the flexibility to continue to work with all the frontier models. So if you look at all these three things, I feel Cadence is in a very, very strong position. Now in terms of your specific question on royalty, we are always looking at different kinds of business models. So in IP, we always had royalty for a while in Tensilica, and now we have royalty in our design IP business as well. But to me that’s just one vector. We’re always looking at how to best serve, give value to our customers, and then get of course value in return.
OPERATOR
Your next question comes from the line of Siddhi Panagrahi from Mizuho. Please go ahead.
Siddhi Panagrahi (Analyst at Mizuho)
Thank you. Congrats on a good quarter. I just want to ask on hardware, I see even the inventory again grew this quarter. I’m wondering, hardware is a strong quarter,
John Wall (Chief Financial Officer)
Thank you. Yes, sure, Citi. Great. Look, I’ll take the hardware question first. The hardware continues to perform exceptionally well. I mean, we delivered another record quarter supported by strong demand from AI and HPC customers, and we’re also seeing growing interest in cloud-based hardware access. The hardware-assisted verification is becoming increasingly important because customers are designing systems of unprecedented complexity, and they need the capacity to verify those designs before committing to silicon.
We have a strong pipeline entering Q4. We continue to expect another record year for hardware. As always, hardware can be lumpy from quarter to quarter depending on product deliveries and customer installation schedules, but the underlying demand environment remains very strong, very healthy, and we’re very, very encouraged by the pipeline of customer engagement. In terms of inventory, the increase in hardware inventories is to support customer orders. We have a significant backlog that we’re trying to work our way through, but there are no capacity or supply chain constraints in terms of addressing that.
We’re just building inventory to hit those numbers. I think you asked about G&A. That’s in part of the quarterly movement. This quarter reflects the timing of annual charitable contributions, which occurred in Q3 this year compared to Q4 last year. So I wouldn’t extrapolate that timing difference into the underlying G&A run rate. There’s a slight impact in G&A in Q3 because of those charitable contributions.
Anirudh Devgan (President and Chief Executive Officer)
And just to add on the hardware piece, I mean Palladium and Protium are performing exceptionally well—not just for AI, and of course we are working with all the major AI companies, but also for any other kind of chip design right now. Hardware became an almost indispensable way to design these things. Now one thing is always like, will this trend continue? We’ll see, but there’s one megatrend. Hardware is normally sold or consumed based on design size. If the design size is bigger, then you need more and more hardware. I was recently thinking about what is going to happen to design size. I mean, the design sizes are already big, but what’s going to happen in the future? I was at imec recently. I gave a talk at imec, which is a European research consortium, and TSMC also gave a talk in the same session. TSMC showed their process roadmap and said in the next five years the chip size will go up by 48x—48x.
Now typically this would happen to be like 5 to 10x in the old days, but I think it’s going to be 48x because you have to add regular technology scaling plus 3D IC—so all these 3DIC packaging, wafer-scale integration. That’s going to further create more and more demand for hardware and, you know, die-to-die IP and things like that. So now our customers are not only verifying one chip or one chiplet; they are verifying those chiplets working with other chiplets, and the size of the chiplets is going up and the amount of chiplets is going up. So I think this trend—the criticality of hardware-based design and verification combined with the growth in size—is going to continue for some time.
OPERATOR
Your next question comes from the line of Joe Quatrochi from Wells Fargo. Please go ahead.
Joe Quatrochi (Analyst at Wells Fargo)
Yeah, thanks for taking the question. I was curious if you could just kind of talk about the change in the 2026 outlook, particularly from an operating profit perspective, because I think when you flow through the incremental margin on the upside in revenue, it’s quite significant relative to maybe your targeted over 50%. So maybe just any help there in terms of understanding what’s going on there?
John Wall (Chief Financial Officer)
Sure, yeah. Look, we continue to be very pleased with the underlying profitability of the business. Despite integrating Hexagon’s D&E business and making targeted investments in our strategic growth opportunities, we’re now expecting approximately 44.7% non-GAAP operating margin for 2026, which is slightly above last year. But if you look at the revenue growth this year over last year, it’s about 1.1 billion. Non-GAAP operating profit is growing about 500 million.
And when you consider the Hexagon D&E being part of that—and that’s not really contributing much in the way of operating profit at all this year—and I think we said at the start of the year we expect the Hexagon D&E to be about 160 million for the year; it’s doing slightly better than that, but it’s pretty much on target. If you back that out, we’d be where you typically expect us to be, which is kind of mid-teens for revenue growth, high teens for EPS growth, and in our usual kind of 54–55% incremental margin range. So that is kind of impacting the incremental margin for this year, but we’d expect that to help us next year because the integration is going really well and we’re taking steps now to improve things for next year in the same way as we did with Beta the last couple of years.
OPERATOR
Your next question comes from the line of Charles Shi from Needham and Company. Please go ahead.
Charles Shi (Analyst at Needham and Company)
Thanks for taking my question. Hey, Anirudh, glad to hear about the Maestro model portfolio just announced today. In light of your competitor who announced GPT Synopsys, can you tell us what your thoughts are regarding collaborating with the frontier labs for a specialized model? Given that Maestro, based on what you just described, it sounds like it’s a collection of open models post-trained by Cadence. And I think now your customers also have choices between your model portfolio and, let’s say, GPT Synopsys.
What do you tell your customers when they have both choices and why they should pick Cadence? Thank you.
Anirudh Devgan (President and Chief Executive Officer)
Yeah, thanks for the question, Charles. So, first of all, I just want to point out that we have worked with OpenAI from the very beginning of when they got started in terms of chip design, and we have all kinds of collaborations with OpenAI—and same thing with Anthropic and Gemini, the three leading frontier models. We always discuss all kinds of collaborations with them. Now, in terms of what we want to make sure as we do Maestro, the key thing is Maestro is not just our own models, but it still has maximum flexibility to use the right model for the right tasks. So let’s say for a certain percentage of the tasks, a certain frontier model is better—we will of course use that to give the best performance to our customers. But the key thing in our solution is that we want to control the three layers of the cake, and we want to control the interaction with our customers.
We want to be the primary interface to our customers, which is what our customers want. In return our agents will provide them a lot of value, and we’ll use the right LLM for the task. The critical thing is we become the primary interface and we guarantee the confidentiality of our data and their data. The other thing we will allow with Maestro is, if a customer wants to post-train Maestro further—and this is another request from our customers—we will allow that. If the customers want to have it on prem, we will allow that. If the customer wants to host on the cloud, we will allow that. So it gives maximum flexibility and maximum performance by combining all three layers, and only Cadence can provide this. And that will give, without any doubt, the best results for our customers.
OPERATOR
Your next question comes from the line of Harlan Sur from JP Morgan. Please go ahead.
Harlan Sur (Analyst at JP Morgan)
Yeah, good afternoon. Thanks for taking my question. Anirudh, last quarter you mentioned the pickup in the more traditional analog, power, microcontroller segments of the market, right? Targeting industrial, automotive, consumer IoT. That’s the positive cyclical dynamics we’re seeing now as excess customer inventories have bottomed in these end markets. Are you continuing to see the positive demand trends in this segment? And these same companies are now very focused on embedding next-generation physical AI-based capabilities into their chip designs. Like your MCU customers, for example, are adding NPU capabilities to their chip designs. Your analog customers are designing more sensor-related capabilities into their chips, more ADCs, on-chip DSP processing. Is this also translating into a better book of business for the Cadence team?
Anirudh Devgan (President and Chief Executive Officer)
Yes, Harlan, that’s a great point. I think the momentum is very strong in analog and mixed-signal—and you can look at their results as well—driven by physical AI and overall recovery of the market. And of course memory, which we work with all the big memory companies, is having amazing improvement from a year or two ago. What it shows up is, first of all, we are very, very strong in mixed-signal companies because we have the most complete portfolio with both analog and digital. These designs, like you mentioned, are more and more mixed-signal, so that plays to Cadence’s strength. And also VeraStack—you know, I’m really pleased by the momentum in VeraStack—because analog customers, whether they are mixed-signal companies or these big AI companies that also have huge analog teams, have a massive demand for analog productivity which we could not provide earlier.
We always wanted to, but with agentic AI now we can do a lot more in analog productivity, and you can see that in some of the customer quotes we have seen and the amount of engagements we have with VeraStack. So overall, I’m pretty pleased not just with the environment of analog mixed-signal, but also the new capabilities we are launching with VeraStack.
OPERATOR
Your next question comes from the line of Joe Vruink from Baird. One moment, please—go ahead. Joe Vruink, a reminder to unmute your device. Your line is open. Moving on for now. Your next question comes from the line of Lee Simpson from Morgan Stanley. Please go ahead.
Lee Simpson (Analyst at Morgan Stanley)
Great. Thanks for fitting me in here, Anirudh. It does seem as though you and your peer are pursuing very distinct routes to monetizing agentic AI in chip design. I mean, I thought you answered one of the previous questions quite well. It really does ring out that you’re looking to have your own control on the modeling. Your deployment flexibility comes through. So you can see that adoption as you see it versus a frontier-model-centric partnership is something you think will gain quite good adoption.
But is there something in the performance that we need to better appreciate, or even in the delivery costs, that really led you to go down this route in strategy? And maybe just a follow-up question: you did mention that a leading space company was adopting some of your IP. Just wonder if there’s anything specific to call out there—are they taking just typical IP blocks, or does it come with any broader IP services attached? Thanks.
Anirudh Devgan (President and Chief Executive Officer)
Yeah, let me answer the second part first and come back to the first one. I mean, in general, like we have mentioned that, you know, now 45% of our business is coming from the system companies, you know, the non-traditional, you know, versus the traditional semi. So these are new customers. And what is also encouraging to see is that it’s not just big AI, you know, hyperscalers doing design, but we are seeing a lot of other system companies like the one we highlighted, like a space company, even companies in finance doing their own chips.
As we make chip design easier and easier, this is always the promise that more and more companies will do that. And when these new companies do design, we naturally have IP opportunities, but also hardware and also naturally design services because they are getting started. And we have one of the best design services capabilities as we are the best at using our own products. So I think this is some space we have to watch. But I’m pretty encouraged by more system companies doing chip design.
Now, in coming back to the AI solution, we want to have control and we want to give flexibility to our customers. So this is the right way to look at it. And there is a lot of value in the model itself, and it is actually pretty cost-economical to post-train a Frontier-class model. Okay, this could not have happened like two years ago, but now it’s actually pretty manageable cost too. And, you know, you get pretty remarkable results, like I mentioned, by very incremental cost to post-train a Frontier model.
And of course that’s an important thing to have in the bag along with a commercially available Frontier model. It’s just choosing the best solution of the two. And then in our own agentic development, right, we have our own IP team, a lot of people using our, of course, using Agent Stack and all the solutions. So the benefits of cost apply to us as well. So even though there is some incremental expense to train them, there is also incremental saving in our own deployment of AI. So overall I think it should not affect any financial model of Cadence. It only gets stronger as we add like a new kind of revenue capability and all the margin progression and all should go as we have planned for a while.
But what Maestro does is this flexibility, but also gives customization ability, it gives control and then it gives cost saving. So this is best of all worlds.
John Wall (Chief Financial Officer)
Yeah, Lee, if I could add there, I think that’s a very, very important point that Anirudh makes in terms of the flexibility. We really want customers to have choice and control over the models they use while benefiting from Cadence’s domain expertise and deep integration with our design tools. Our strategy remains centered on delivering high-value engineering software rather than building a large general-purpose AI infrastructure business. We’re focused on improving the efficiency and the value of agentic workflows and maintaining attractive software economics as customer adoption scales.
With the way the flexibility works in our model, our model allows customers to use their own LLM infrastructure, bear their associated model compute cost if they want to, and Cadence monetizes the underlying design agents and our agentic orchestration capabilities. The economics of particular deployment options could vary, of course, but the principle is that greater customer usage should create greater value for Cadence. I think that’s where we differentiate ourselves.
OPERATOR
Your next question comes from the line of Joe Vruink from Baird. Please go ahead.
Joe Vruink (Analyst at Baird)
Hi, can you hear me?
Anirudh Devgan (President and Chief Executive Officer)
Yes, yes Joe, please go ahead.
Joe Vruink (Analyst at Baird)
Oh great. I wanted to go back to VeraStack and InnoStack. I think it’s fair that the analog and back-end areas haven’t seen the AI proliferation yet like front-end design has. Are you seeing this change? Is specialized models and post-training something that helps better address these domains? And if all of this is starting to happen, is there a commensurately higher revenue implication for Cadence relative to even what has been seen to date? Yeah.
Anirudh Devgan (President and Chief Executive Officer)
Hi Joe. I mean first of all, you know we started with ChipStack but then we have launched the three other major super agents, as you know: VeraStack for analog, InnoStack for back end, and AuraStack, the latest one for package and PCB. And I have to say the demand is actually phenomenal. Demand is phenomenal especially because ChipStack is doing great, but also these other agents are even more integrated with our base tools. So InnoStack—if you look at our endorsements—you know typically when we work with customers to get a public endorsement, it only happens like 1 out of 10 customers because not everybody wants to talk about what they’re doing. But we have maybe 10 endorsements this quarter. So that tells you the extent of the deployment of all these agents. And if you look at InnoStack, Renesas is talking about like 34x productivity improvement, Samsung Foundry—see, there are other use cases that can happen, not just physical design.
One big use case is design-technology co-optimization with the foundries. So InnoStack is a critical agent for that. Socionext talked about 5x productivity improvement. Rapidus has talked about 2x improvement. So there’s a multiple-X improvement happening within InnoStack. VeraStack already said the demand is off the charts. And AuraStack—this is the latest area where innovation hasn’t happened—so Nokia showed 5x productivity improvement with AuraStack.
So ChipStack is probably a year now in deployment, and these are maybe six to nine months. But there is accelerated deployment of these newer ones, and they are even more tightly integrated with the base tools. And then Maestro adds further differentiation on top of these capabilities. So I feel very, very confident. And, you know, what’s going to happen is, you know, we have a lot of R&D investment in the traditional tools in the middle layer, but now we are putting significant differentiation and investment in the top layer.
You know, we are defining, I believe, what will be the agentic flow for the future in all industries. So as we make the agentic top layer more and more complicated, more and more integrated, more and more differentiated, it is very difficult to compete with Cadence in agentic AI, whether you’re a startup or if you are, you know, a customer trying to write some agents. But the capabilities with model routing, with domain-specific models, with tight integration with agents and the tools—this is something that our customers can’t do themselves.
So that also bodes very well for all these customers realizing that and working more and more closely not just in the middle layer but in the top layer with us. So I feel very good in terms of how the speed and differentiation of our agentic offerings.
OPERATOR
Your next question comes from the line of Jay Vlischauer from Griffin Securities. Please go ahead.
Jay Vlischauer (Analyst at Griffin Securities)
Thank you. Good evening, Anirudh. In your prepared remarks you made some important references to the components of your core EDA, particularly in digital. It’s demonstrable that over the last number of years you’ve gained considerable share in core EDA. But I’d like to delve into what the components of that share gain have been. In other words, when we think about the various large categories that comprise core EDA, especially digital, would it be fair to say that the single largest difference in terms of the dollar and share gain you’ve had over the last number of years has been with Innovus in implementation and perhaps to a lesser degree in areas like synthesis, RTL, simulation, analysis and so forth? Or perhaps you can speak about the breadth of where that incremental revenue and share has come from in core EDA to the extent that it’s not really largely concentrated in place-and-route?
Anirudh Devgan (President and Chief Executive Officer)
Yeah Jay, great question. So yes, I mean, if you look over five to seven years we have gained so much in digital implementation, you know, with Innovus becoming basically the platform of choice for advanced nodes design. And then what is particularly encouraging in the last six to 12 months is not just Innovus but all the tools around Innovus. And, you know, we always have talked about for 10 years now—of course there’s integration at lower nodes, so that was going to happen anyway as we go to 3-nanometer, 2-nanometer.
It’s difficult to separate signoff from implementation or synthesis from implementation. But then with this agentic AI, you know, there’s a further kind of need for vertical flows which are well integrated. So what I’m—and I mentioned this last quarter also—I’m particularly pleased to see, I mean, not only are our tools in synthesis and signoff much, much better now versus a few years ago, but we are getting this real full-flow deployment. And not just small or medium customers, but some very, very large customers are deploying our full digital flow. And this is a great trend for Cadence. At the same time we maintain our undisputed leadership in analog, and then because of the strength of Palladium, the strength of Jasper, the strength of Verisium, and agentic flows, ChipStack, it is also helping our verification business and logic simulation.
So overall that’s why I was saying I feel that in all three big parts of EDA we are in a very, very strong position and gaining momentum—not just individual tools being better and proven by large companies, but also this integration at advanced nodes, integration with the agentic solution. So we’ll see how it works, but overall I feel pretty good about it.
OPERATOR
Your next question comes from the line of Gary Mobley from StoneX. Please go ahead.
Gary Mobley (Analyst at StoneX)
Hi guys, good afternoon. Thanks for taking my question. Your bookings in the first, somewhere in the ballpark of, I don’t know, mid-20%, call it 26% year over year. If I recall correctly, this was supposed to be a low renewal period—2026 was supposed to be. So my question is, you know, what’s driving the strong backlog, the strong bookings? Is it, you know, utilizing traditional EDA tools as a result of, you know, AI agent workflows? Is it a reflection of the strength of the semiconductor industry?
Any color that you can add there would be helpful, thank you.
John Wall (Chief Financial Officer)
Sure. Thanks, Gary. I mean we’re very pleased with the bookings performance and the growth in backlog, particularly given that 2026, as you mentioned, is a comparatively lighter renewal year for us. The strength reflects healthy underlying design activity, expanded customer engagements, and strong add-on opportunities across our portfolio. And what we’re seeing is that Cadence is becoming increasingly important to customers as they look to accelerate their engineering roadmaps and address more complex design challenges.
The backlog itself provides good visibility into that, but it’s important to remember that, you know, that backlog and bookings number reflects the timing of revenue recognition that depends on the structure and delivery of individual arrangements and stuff like that. So there’s always a blend of time and value in those. But strong bookings in a lighter renewal year are particularly encouraging because they reflect expanding customer engagement and not simply the renewal calendar.
We’re very, very pleased with that booking strength. Thank you. So Gary, needless to say, I mean we’ve done three quarters. There’s still one more quarter to go which is traditionally our strongest quarter. So we’ll see where we end.
Gary Mobley (Analyst at StoneX)
We have confidence in you.
John Wall (Chief Financial Officer)
Yes, thank you.
OPERATOR
Your next question comes from the line of Lachlan Brown from Roth MKM. Please go ahead.
Lachlan Brown (Analyst at Roth MKM)
Hi Anirudh. John, thanks for squeezing me in. Are you seeing much evidence that new AI design tools like ChipStack are lowering the barriers to entry for custom silicon for non-traditional designers? And I appreciate that land-and-expand naturally takes time, but are there signs that new logos could become more of a meaningful growth driver for that expansion going forward? Thanks.
Anirudh Devgan (President and Chief Executive Officer)
Yes, absolutely. I mean, like I’ve talked about this for four years, that as we make chip design easier to do, there will be more and more people doing chip design. And, if you step back, we made chip design maybe over the last 20 years a hundred times easier to do because, maybe 20 years ago, IBM, Intel would do CPU design with 500 people in five years. Now you can do a CPU design in six months with 50 people. So that’s like a hundred times better, and that’s what led to more and more designs.
And with AI we can add another 5 to 10x, so it’ll be like a thousand times easier than probably 20–25 years ago. And this will invariably lead to more and more design activity. And you can see that even with the hyperscalers. If you go back one or two years ago, it was not clear that the hyperscalers will do, or we always got questions: will they continue to do? But now with Google and Meta and Microsoft and Amazon and Tesla, everybody is doing more and more designs.
Now at the same time the traditional semiconductor companies are also doing well—of course the big semi companies. And this is only going to accelerate, in my opinion. We talked about space companies, finance companies, specifically car companies. The amount of design activity we are seeing in automotive is actually quite remarkable. And robotics and things like that. Robotics also requires a lot of different chips, right? The amount of chips that are there in one hand—you know, there are like 25 chips in one hand of a humanoid robot to control all the individual motors. So I believe this for years, and I think you will see more and more of this.
And our job is to make these things easier to use, give more flexibility to our customers, like we did with our agent solution in Maestro, so they can do more themselves.
OPERATOR
Your final question comes from the line of Kelsey Chia from Citi. Please go ahead.
Kelsey Chia (Analyst at Citi)
Hi. Hi Anirudh, congrats on the great results. My question is on EDA. EDA growth has been very strong for the last three quarters, 18–19% year-on-year growth, well above the historical growth rate. And it sounds like the majority of that growth is primarily driven by hardware and agentic AI attribution is still quite small. Is that a fair characterization, or could you give us a sense of customer adoption levels for agentic workflows today, and is much of the sort of revenue acceleration from agentic AI still ahead of us?
Anirudh Devgan (President and Chief Executive Officer)
Yeah, thanks for the question. Yes, of course we are very proud of the hardware growth, but as you know we will break out recurring and non-recurring growth. So, like I was mentioning earlier, and John mentioned also, our recurring growth has improved from 11% last year to 18%. So that is not hardware—that’s ex-hardware, because hardware will typically show up in non-recurring growth. So that’s a major improvement of our recurring growth from last year to this year.
And that is a combination of agentic AI plus the growth of underlying tools and design starts on the software side.
John Wall (Chief Financial Officer)
Yeah, Kelsey, I think based on listening to Anirudh answer Lachlan’s question there a few minutes ago, essentially we believe agentic AI is a demand accelerator for EDA. I mean, customers have always built their own workflows around Cadence tools, and we expect them to continue to develop their own agents as well. Our strategy accommodates that. What’s exciting is that agents could perform much more design exploration than an individual engineer calling our underlying engines repeatedly, and that creates the potential for significantly more consumption of our core EDA tools.
I think you’re starting to see that reflected in our EDA growth this year.
OPERATOR
At this time, there are no further questions. I will now turn it back to Anirudh Devgan for closing remarks.
Anirudh Devgan (President and Chief Executive Officer)
Thank you all for joining us this afternoon. It’s an exciting time for Cadence as we finished our Q3 with AI-driven product leadership and strong business momentum. The Wall Street Journal ranked Cadence Design Systems’ board number two among all S&P 500 companies in its 2026 Best Boards list. And on behalf of our employees and our Board of Directors, we thank our customers, partners, and investors for their continued trust and confidence.
OPERATOR
Thank you for participating in today’s Cadence third quarter 2026 earnings conference call. This concludes today’s call. You may now disconnect.
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