Operator: Good afternoon. My name is Jade, and I will be your conference operator today. At this time, I would like to welcome everyone to the Cadence third quarter 2026 earnings conference call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session.
Operator: If you would like to ask a question during this time, 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. Please go ahead.
Richard Gu: Thank you, operator. I would like to welcome everyone to our third quarter of 2026 earnings conference call. I am 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.
Richard Gu: 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.
Richard Gu: 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, we would ask that you observe a limit of one question only. If time permits, you can requeue with additional questions. Now I will turn the call over to Anirudh.
Anirudh Devgan: Thank you, Richard. Good afternoon, everyone, and thank you for joining us today. I am 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.
Anirudh Devgan: 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.
Anirudh Devgan: 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 longstanding customer demand for domain-specialized models.
Anirudh Devgan: 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.
Anirudh Devgan: Today, I am 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 agent, Maestro Models, and proven EDA and SDA tools, we are uniquely positioned to deliver optimal performance, efficiency, and throughput results for our customers.
Anirudh Devgan: 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.
Anirudh Devgan: 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 ChipStack 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.
Anirudh Devgan: Importantly, this demonstrates the power of combining our AI super agent 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.
Anirudh Devgan: In Q3, MediaTek deployed ChipStack into its production digital verification flow, achieving 50% to 70% improvement in verification coverage and 1.4 to 2x reduction in engineering effort, with deployment now scaling across design teams. In analog and custom design, ViraStack saw a 4x increase in customer engagement, with deployments in various production designs at multiple customers.
Anirudh Devgan: 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.
Anirudh Devgan: 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.
Anirudh Devgan: Socionext 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 3D IC design, the early response to our AuraStack AI super agent has been overwhelmingly positive.
Anirudh Devgan: Nokia is deploying AuraStack 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.
Anirudh Devgan: 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 SDA. 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.
Anirudh Devgan: We are pleased that recurring revenue sustained its strong momentum, growing 18% year over year. I am particularly proud of the significant wide-ranging agreement 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.
Anirudh Devgan: 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 trends across the portfolio. Our digital and sign-off business continued to expand, achieving 16 new full flow wins during the quarter.
Anirudh Devgan: As agentic flows increasingly require full vertical workflows, there is growing demand for our synthesis and sign-off solutions. Our sign-off products saw growing adoption, and our verification software product 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.
Anirudh Devgan: 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 a 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.
Anirudh Devgan: In analog and custom design, Spectre FX continues to gain momentum, highlighted by major competitive displacement at a marquee infrastructure silicon provider with multiple new 2-nanometer projects adopting our platform.
Anirudh Devgan: 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 investment, growing adoption of custom silicon, and increasing demand for high-performance IP.
Anirudh Devgan: Along with the transition to advanced nodes and chiplet-based architectures, these AI-driven trends are creating a strong pull for our differentiated star 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.
Anirudh Devgan: 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 electronic and physical systems intelligence portfolio.
Anirudh Devgan: 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.
Anirudh Devgan: Our Millennium platform had strong adoption, driven by deployment of our GPU-accelerated electronic and mechanical systems 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 factory. The MSC business is an integral part of our physical AI strategy, and its integration is progressing well.
Anirudh Devgan: MSC Adams, our industry-leading multi-body dynamic solution, helped secure a competitive displacement at a leading power company, along with adoption of other Cadence products. In summary, Cadence delivered exceptional Q3, and I am 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.
Anirudh Devgan: 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: 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 design. 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.
John Wall: Here are some of the financial highlights from the third quarter, starting with the P&L. Total revenue was $1.633 billion. 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 billion, while the principal value of debt outstanding was $2.650 billion. Operating cash flow was $424 million.
John Wall: DSOs were 66 days, and we used $400 million to repurchase Cadence 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 would 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.
John Wall: For our updated outlook for 2026, we now expect revenue in the range of $6.387 billion to $6.417 billion. 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.
John Wall: We expect to use more than 50% of our free cash flow to repurchase Cadence shares in 2026. For Q4, we expect revenue in the range of $1.695 billion to $1.725 billion. 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.
John Wall: Non-GAAP EPS in the range of $2.19 to $2.25. As usual, we have 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 am 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.
John Wall: As always, I would like to close by thanking our customers, partners, and our employees for their continued support. 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 1 on your telephone keypad. To withdraw your question, press star 1 again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device.
Operator: As a courtesy to all participants, we ask that you limit yourself to 1 question. We will pause for a moment to compile the Q&A roster. Your first question comes from the line of Jason Celino from KeyBanc Capital Markets. Please go ahead.
Jason Celino: Hey, thank you for taking my question. Anirudh, really nice to see the Maestro announcement. I am not an EDA engineer, I am not an AI scientist. Maybe can you just explain what this is a little bit better? It sounds like it is part model router, but also maybe domain-specific models. Is Cadence developing these domain-specific models? Maybe just elaborate and then how the monetization of this product will work. Thank you.
Anirudh Devgan: Yeah. Hi, Jason. Thank you for the question. There is 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 AgentStack and all the four super agents. You can see there are more than 100 engagements now. As we worked with customers over the last six to nine months, several trends became pretty clear. To me, we are launching Maestro as what I would call the next generation of agents. There are three big factors in the next generation of agents.
Anirudh Devgan: One is, what has happened in the last one year is that, of course, we worked with all the frontier labs from the very beginning, whether they are doing chip design themselves or we are using their LLMs.
Anirudh Devgan: But over the last one year, there is this emergence of very good open-weight models. I always wanted to do domain-specialized models, but now it is finally possible with open-weight models. It is actually very cost-effective to post-train these models to deliver domain intelligence, which is remarkable. We are getting up to 30% better quality by using our tools and data to post-train these models.
Anirudh Devgan: Also, it is a portfolio of models. All the way to 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. That is the first part of Maestro. It gives customization for chip and system design and gives much, much better results. It is actually quite remarkable how much these models can be improved by post-training.
Anirudh Devgan: Now, the second part of that is in our agents and when they do these tasks, have variety of tasks. We do not need only one size or type of model to do the task. The second part is model routing. Just like you do not need to use the airplane to go to your local grocery store.
Anirudh Devgan: Similarly with agents, you have a variety of tasks that we need to do, from simple tasks to deep thinking and reasoning tasks. The model router will efficiently, knowing what the task is from our super agent, use the right model, and that can easily give up to 5X improvement in token efficiency. Either much lower cost or much more work for the same cost. Then the third thing is, which I always talked about, this reinforcement learning loop with the underlying tools.
Anirudh Devgan: We have 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 because the integration with the underlying tools and how we call them and continuous self-improvement, whether it is skills or models, gives us a unique advantage. If you combine these three things together, domain specialized model, 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.
Anirudh Devgan: It also allows us to control all three layers of the cake and innovate in all three layers of the cake. I am actually super excited, and I think this is what is going to happen in all industries as these LLMs mature, this 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: 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 are 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 maybe underlying that, whether there was any acceleration in the agentic part of that recurring revenue. Thank you.
Anirudh Devgan: Yeah, thanks for the question. I will start and then John can comment. I am very proud of the recurring growth. 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.
Anirudh Devgan: If you step back and compare last year, recurring revenue was 11%, and it is 18% for this quarter, and it was very good last quarter. I expect recurring revenue to do very well in 2026. This is a result of 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. That is what we see. A lot of engagement with agentic and lot of usage of our base tools.
Anirudh Devgan: That provides a lot of growth for recurring. Then you add that with our ongoing strength in IP and hardware, and you get a very, very good set of results.
John Wall: Yeah, I'd agree with that, Anirudh. It basically reflects strong demand across our software portfolio and healthy customer expansions and continued strength in core EDA. I think that's the way to look at it in terms of strong high teen growth and recurring revenue. We are 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: Thanks for taking my question. Anirudh, we recently heard three things from your competitor, that they can grow sales kind of at a mid-teen space 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 $1 billion to their top line over the next several years.
Vivek Arya: 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: Yeah, thanks for the question, Vivek. So overall, I am very pleased with our growth and not just growth, but profitability and margin. And we always talk about rule of 40 metrics or revenue 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.
Anirudh Devgan: And we still have one more quarter to go, so we will see. It's an important quarter for us. Q4 is always important. So we will 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.
Anirudh Devgan: There is an amount of design activity in all aspects of AI and even the traditional companies and analog mixed signal companies, digital companies, memory companies, system companies. The amount of design activity is the strongest that I have seen. We are gaining share in almost all segments of our business, whether it is IP or hardware or EDA, agentic AI.
Anirudh Devgan: Our product portfolio, I think, is the strongest it has ever been. Then we have this huge opportunity with agentic AI. 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. If you look at all these three things, I feel Cadence is in a very strong position.
Anirudh Devgan: Now, in terms of your specific question on royalty, we are always looking at different kind of business models. In IP, we always had royalty for a while in Tensilica, and now we have royalty in our design IP business as well. To me, that is just one vector. We are 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 Panigrahi from Mizuho. Please go ahead.
Siti Panigrahi: Thank you. Congrats on a good quarter. I just want to ask on hardware. I see even the inventory, again, grew this quarter. I am wondering, hardware is a strong quarter, how sustainable hardware growth for heading into next year, 2027? Any color would be helpful there. Quick clarification, John. There was a spike in G&A expense in Q3. Was that anything one time there? Thank you.
John Wall: Yeah, sure, Siddhi. Look, I will take the hardware question first. The hardware continues to perform exceptionally well. We delivered another record quarter supported by strong demand from AI and HPC customers, and we are 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.
John Wall: We have a strong pipeline entering Q4, and 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 are very encouraged by the pipeline of customer engagement.
John Wall: In terms of inventory, the increase in hardware inventory is to support customer orders that we have a significant backlog that we are trying to work our way through. But there are no capacity or supply chain constraints in terms of addressing that. We are just building inventory to hit those numbers.
John Wall: Then you asked about G&A. That is in part to the course movement, and this quarter reflects the timing of annual charitable contributions, which occurred in Q3 this year compared to Q4 last year. So I would not extrapolate that timing difference into the underlying G&A run rate. So there is a slight impact in G&A in Q3 because of those charitable contributions.
Anirudh Devgan: Yeah. And Siddhi, just to add on the hardware piece, Palladium and Protium performing exceptionally well. Not just for AI, and of course, we are working with all the major AI companies, but also, even any other kind of chip design right now, hardware became almost indispensable way to design these things.
Anirudh Devgan: Now, one thing is always like, will this trend continue? And we will see, but there is one mega trend. So hardware is, you probably know this already, but just to highlight it even further, hardware is normally sold or consumed based on design size. So if the design size is bigger, then you need more and more hardware. So actually, I was recently, just the example of what is going to happen to design size. The design sizes are already big, but what is going to happen to design sizes in the future?
Anirudh Devgan: Just to give you example, I was at imec recently. I gave a talk at imec, which is a European research consortium, and TSMC will also give a talk, same session. So TSMC showed their process roadmap and said in the next 5 years, the chip size will go up by 48x. Now, typically, this would happen to be 5 to 10x in the old days.
Anirudh Devgan: But I think it is going to be 48x because you have to add regular technology scaling plus 3D IC. So all these 3D IC packaging, wafer scale integration. So that is going to even further create more and more demand for hardware and die-to-die IP and things like that.
Anirudh Devgan: 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 are going up. I think this trend, one 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: Yeah, thanks for taking the question. I was curious if you could just talk about the change in the 2026 outlook, particularly from an operating profit perspective, because I think, when you flow through just the incremental margin on the upside and revenue, it is quite significant relative to maybe you are targeted over 50%. Maybe just any help there in terms of understanding what is going on there.
John Wall: 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 are now expecting approximately 44.7% non-GAAP operating margin for 2026, which is slightly above last year. If you look at the revenue growth this year over last year, it is about $1.1 billion.
John Wall: Non-GAAP operating profit is growing above $500 million and when you consider the Hexagon D&E being part of that, and that is not really contributing much in the way of operating profit at all this year. I think we said at the start of the year, we were expecting Hexagon D&E to be about $160 million for the year. It is doing slightly better than that. It is pretty much on target.
John Wall: But, if you back that out, we would be where you typically expect us to be, which is mid-teens for revenue growth, high teens for EPS growth, and in our usual kind of 54%, 55% incremental margin range. So, the Hexagon D&E is kind of impacting the incremental margin for this year. But we would expect that to help us next year because the integration is going really well. And we are taking steps now to improve things for next year in the same ways we did with BETA the last couple of years.
Operator: Your next question comes from the line of Charles Shi from Needham & Company. Please go ahead.
Charles Shi: Oh, 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 the GPT-Synopsys, can you tell us what your thoughts are regarding collaborating maybe with the Frontier Labs for a specialized model.
Charles Shi: Given that Maestro, based on what you just described, it sounds like it is a collection of open models post-trained by Cadence. And I think now your customers also have choices between your model portfolio and let us say GPT-Synopsys. So what do you tell your customers when they have both choices and why they should pick Cadence? Thank you.
Anirudh Devgan: 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. So 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. So we always discuss all kinds of collaborations with them.
Anirudh Devgan: Now, in terms of what we want to make sure as we, and 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 task. So let us say if 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.
Anirudh Devgan: 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. And in return, our agents will provide them a lot of value, and we will use the right LLM for the task. But the critical thing is as we became the primary interface, and we guarantee the confidentiality of our data and their data.
Anirudh Devgan: 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.
Anirudh Devgan: 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 JPMorgan. Please go ahead.
Harlan Sur: Yeah, good afternoon. Thanks for taking my question. Anirudh, last quarter, you mentioned a pickup in the more traditional analog power microcontroller segments of the market, right? Targeting industrial, automotive, consumer, IoT. That is the positive cyclical dynamics we are seeing now as excess customer inventories have bottomed in these end markets. Are you continuing to see the positive demand trends in this segment?
Harlan Sur: 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, D/A converters, on-chip DSP processing. Is this also translating into a better book of business for the Cadence team?
Anirudh Devgan: 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. Of course, memory, which we work with all the big memory companies, is having, of course, amazing improvement from a year or 2 ago. I think what it shows up is, first of all, we are very strong in mixed signal companies, because we have the most complete portfolio with both analog and digital.
Anirudh Devgan: These are designs, like you mentioned, are more and more mixed signal. That plays to Cadence's strength. Also ViraStack. I'm really pleased by the momentum in ViraStack because as analog customers, whether they are mixed signal companies or these big AI companies also have huge analog teams.
Anirudh Devgan: I don't know if you realize that some of these big AI companies, designing their own IPs and all have huge analog teams. There is 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.
Anirudh Devgan: You can see that in some of the customer quotes we have seen and the amount of engagements we have with ViraStack. Overall, I'm pretty pleased, not just with the environment of analog mixed signal, but also the new capabilities we are launching with ViraStack.
Operator: Your next question comes from the line of Joe Vruwink from Baird. One moment. Please go ahead. Joe Vruwink, 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: 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 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. You can see that adoption, as you see it, versus a frontier model-centric partnership is something you think will gain quite good adoption.
Lee Simpson: Is there something in the performance that we need to better appreciate or even in the delivery cost that really led you to go down this route, in strategy? 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.
Lee Simpson: Are they taking just typical IP blocks, or does it come with any broader IP services attached? Thanks.
Anirudh Devgan: Yeah. Let me answer the second part first and come back to the first one. In general, like we mentioned that now 45% of our business is coming from the system companies, the nontraditional versus the traditional semi. So these are new customers.
Anirudh Devgan: What is also encouraging to see is that it's not just the big AI hyperscalers that are doing design, but we are seeing 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 was always the promise that more and more companies will do that.
Anirudh Devgan: When these new companies do design, we naturally have IP opportunities, but also hardware and also naturally design services because they're getting started, and we have one of the best design services capabilities as we are the best at using our own product. So I think this is some space we have to watch, but I'm pretty encouraged by more system companies doing chip design.
Anirudh Devgan: Now, 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. There is a lot of value in the model itself, and it is actually pretty cost-economical to post-train a frontier class model.
Anirudh Devgan: This could not have happened two years ago, but now it's actually pretty manageable cost to, and you get pretty remarkable results, like I mentioned, by a very incremental cost to post-train a frontier model. 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. Then in our own agentic development, right?
Anirudh Devgan: We have our own IP team, a lot of people, of course, using AgentStack 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.
Anirudh Devgan: It only gets stronger as we add a new kind of revenue capability and all the margin progression should go as we have planned for a while. But what Maestro does is this flexibility. It also gives customization ability, it gives control, then it gives cost saving. This is best of all worlds.
John Wall: Yeah, Lee, if I could add there, I think that is a 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.
John Wall: We are focused on improving the efficiency and the value of agentic workloads and maintaining attractive software economics as customer adoption scales. But 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. Cadence provides the underlying design agents and our agentic orchestration capabilities.
John Wall: The economics of particular deployment options could vary, of course, but the principle is that greater customer usage should create greater value for Cadence, and I think that is where we differentiate ourselves.
Operator: Your next question comes from the line of Joe Vruwink from Baird. Please go ahead.
Joe Vruwink: Hi. Can you hear me?
John Wall: Yes.
Anirudh Devgan: Yes, Joe. Please go ahead.
Joe Vruwink: Oh, great. I wanted to go back to ViraStack and InnoStack. I think it is fair that the analog and back-end areas have not 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? 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?
Anirudh Devgan: Yeah. Hi, Joe. First of all, we started with ChipStack, but then we have launched the three other major super agents, as you know. ViraStack for analog, InnoStack for back-end, and AuraStack, the latest one for package and PCB. I have to say, the demand is actually phenomenal. Demand is phenomenal. Especially because these, ChipStack is doing great, but also these other agents are even more integrated with our base tools.
Anirudh Devgan: So InnoStack, if you look at our endorsement, typically when we work with customers to get a public endorsement, it only happens 1 out of 10 customers, because not everybody wants to talk about what they are doing. But we have maybe 10 endorsements this quarter, so that tells you the extent of the deployment of all these agents. If you look at InnoStack, Renesas talking about 34x productivity improvement. Samsung Foundry.
Anirudh Devgan: See, there are other use cases that can happen, not just physical design. One big use case is design technology co-optimization with the foundry. The InnoStack is a critical agent for that. Socionext talked about 5X productivity improvement. Rapidus talked about 2X improvement. So there is a multiple X improvement happening with InnoStack. ViraStack, I already said the demand is off the charts.
Anirudh Devgan: AuraStack, this is the latest area where innovation has not happened. Nokia showed 5X productivity improvement with AuraStack. ChipStack is probably a year now in deployment, and these are maybe 6 to 9 months. But there is acceleration deployment of the newer, but they are even more tightly integrated with the base tools. Maestro adds further differentiation on top of these capabilities. I feel very, very confident.
Anirudh Devgan: What is going to happen is, we have a lot of R&D investment in our traditional tools in the middle layer. Now we are putting significant differentiation and investment in the top layer. We are defining, I believe, what will be the agentic flow for the future in all industries.
Anirudh Devgan: As we make the agentic the top layer and more and more complicated, more and more integrated, more and more differentiated, it is very difficult to compete with Cadence in the agentic AI, whether you are a startup or if you are trying to write a customer, trying to write some agent. But the capabilities with model routing, with domain-specific models, with tight integration with agent and the tools, this is something that our customers cannot do themselves.
Anirudh Devgan: 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. 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 Vleeschhouwer from Griffin Securities. Please go ahead.
Jay Vleeschhouwer: 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 is demonstrable that over the last number of years, you have gained considerable share in core EDA. I would like to delve into what the components of that share gain have been.
Jay Vleeschhouwer: 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 have 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?
Jay Vleeschhouwer: 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 is not really largely concentrated in place and route.
Anirudh Devgan: Jay, great question. Yes, if you look at over five, seven years, we have gained so much in digital implementation, with Innovus becoming basically the platform of choice for advanced nodes design. What is particularly encouraging in the last 6 to 12 months is, it is not just Innovus, but all the tools around Innovus. I have talked about for 10 years now, of course, there is integration at lower nodes.
Anirudh Devgan: That was going to happen anyway as we go to 3 nanometer, 2 nanometer. It is difficult to separate sign-off from implementation or synthesis from implementation. With this agentic AI, there is a further need for vertical flows which are well integrated.
Anirudh Devgan: I mentioned this last quarter also, I am particularly pleased to see, not only our tools and synthesis and sign-off are much, much better now versus few years ago, that we are getting this real full flow deployment. 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.
Anirudh Devgan: At the same time, we maintain our undisputed leadership in analog. Because of the strength of Palladium, the strength of Jasper, strength of Verisium and agentic flows, ChipStack, it is also helping our verification business and logic simulation. Overall, that is why I was saying, I feel that all the three big parts of EDA, we are in a very, very strong position and gaining momentum.
Anirudh Devgan: Not just individual tools being better and proven by large companies, but also this integration at advanced nodes, integration with the agentic solution.
Jay Vleeschhouwer: Yeah.
Anirudh Devgan: So we will see how it progresses.
Jay Vleeschhouwer: Thank you.
Anirudh Devgan: 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: 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. My question is, what is driving the strong backlog, the strong bookings? Is it utilizing traditional EDA tools as a result of 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: Sure. Thanks, Gary. We are 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. What we are seeing is that Cadence is becoming increasingly important to customers as they look to accelerate their engineering roadmaps and address more complex design challenges.
John Wall: The backlog itself provides good visibility into that. It is important to remember 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. There is always a blend of time and value in those. Strong bookings in a lighter renewal year are particularly encouraging because they reflect expanding customer engagements and not simply the renewal calendar.
John Wall: We are very, very pleased with that booking strength.
Gary Mobley: Thank you.
Anirudh Devgan: Gary, needless to say, we have done three quarters.
Gary Mobley: Yes.
Anirudh Devgan: We will see where we end up.
Gary Mobley: Way above.
Anirudh Devgan: Yes. Thank you. Yeah.
Operator: Your next question comes from the line of Lachlan Brown from Rothschild. Please go ahead.
Lachlan Brown: 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? I appreciate that land expand naturally takes time, but are there signs that new logos could become more of a meaningful growth driver for that TAM expansion going forward? Thanks.
Anirudh Devgan: Yes, absolutely. I have talked about this for years, that as we make chip design easier to do, there will be more and more people doing chip design. If you step back, we made chip design maybe over the last 20 years, 100 times easier to do because maybe 20 years ago, all these 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.
Anirudh Devgan: So that is 100 times better, and that is what led to more and more designs. With AI, we can add another 5 to 10x, so it will be 1,000 times easier than probably 20, 25 years ago. This will invariably lead to more and more design activity.
Anirudh Devgan: 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 will always get questions, will they continue to do? But now with Google, Meta, Microsoft, Amazon, and Tesla, everybody is doing more and more designs.
Anirudh Devgan: At the same time, the traditional semiconductor companies are also doing well, of course, the big semi companies. This is only going to accelerate, in my opinion. We talked about space companies, finance companies, physical AI car companies. The amount of design activity we are seeing in automotive is actually quite remarkable, and robotics and things like that. These things, robotics also require a lot of different chips.
Anirudh Devgan: The amount of chips that are there in one hand, there are like 25 chips in one hand of a humanoid robot to control all the individual motors. I have believed this for years, and I think you will see more and more of this. 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: Hi, Anirudh and John. 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. It sounds like majority of that growth is primarily driven by hardware and agentic AI acquisition 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 if much of the revenue acceleration from agentic AI is still ahead of us?
Anirudh Devgan: Yeah. Thanks for the question. Yes, of course, we are very proud of the hardware growth. As you know, we will break out recurring and non-recurring growth. Like I was mentioning earlier, and John mentioned also, our recurring growth has improved from 11% last year to 18%. That is not hardware. That is ex-hardware.
Anirudh Devgan: Hardware will typically show up in non-recurring growth. That is 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: Yeah, Kelsey. I think, based on listening to Anirudh answer Lachlan's question there a few minutes ago, was that essentially, we believe agentic AI is a demand accelerator for EDA. 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.
John Wall: What is exciting is that agents can 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 are 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: Thank you all for joining us this afternoon. It is an exciting time for Cadence as we finish our Q3 with AI-driven product leadership and strong business momentum. The Wall Street Journal ranked Cadence's board number 2 among all S&P 500 companies in its 2026 best boards list. 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: Thank you for participating in today's Cadence third quarter 2026 earnings conference call. This concludes today's call. You may now disconnect.
