Hi, everyone. Welcome joining us at the nineteenth Annual Needham Technology, Media, and Consumer Conference. Well, on this virtual stage with me today are Nimish Modi, Senior Vice President and the General Manager, Strategy and New Ventures, as well as Richard Gu, Vice President of Investor Relations of Cadence Design Systems. Before we begin, just some really quick housekeeping items. Well, since this is a virtual fireside chat, please feel free to submit your questions in the Q&A box. We will have some time on the back of this session to allow attendees to allow our basically our guests Nimish and Richard to address your pressing questions. As usual, we'll keep your questions anonymous. Next, I'm going to read the safe harbor statement from Cadence. Today's discussion may contain forward-looking statements, including Cadence's 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. All forward-looking statements during this meeting are based on estimates and information available as of today, and Cadence disclaims any obligation to update them. All right, cool. So Nimish, we've known each other for a while, but since this is actually the first time you join me for a fireside chat with investors, can you give us an introduction about yourself and your role at Cadence? Sure, sure, sure, Charles. Thanks for inviting me. Hello, everyone. So I run the Strategy and New Ventures group at Cadence, primarily responsible for corporate strategy, corporate marketing, M&A, and, new ventures, which includes running our molecular sciences group, you know, which has the, the OpenEye acquisition, the team that we, acquired about 18 months ago or so. I've been at the company now for about 18 years. The first half of my tenure was, in R&D. I ran portions of all our 4 business groups, and, prior to Cadence, I was at Intel for 18 years, in the CPU group. Most recent, role there was, you know, in charge of, the server CPU R&D. Great. So Nimish, look, so you have this hardcore, right? I call it hardcore semiconductor background, well, based on your 18 years- Yes at Intel. Well, your, your blood is probably still blue, but then you led the systems business, and then now corporate strategy, M&A, more importantly, new ventures at Cadence. I, I think you might be one of the best people in the industry, EDA industry, to really talk about this convergence of silicon and the system designs. So we all know this is not really a new trend in EDA, and I mean, Cadence has had this intelligent, system design strategy for probably more than seven years. Can you talk about how Cadence came up with this strategy quite a few years ago, and whether this strategy has evolved, how that has evolved over the past seven years? Yeah, that's a great question, right? So I think it's a big topic, but what we've been seeing has been over the course of the last few years, there's been an accelerating hyperconvergence, you know, in the electronics industry. You know, the hyperconvergence meaning of mechanical, electrical domains, of hardware and software, of semiconductor and system. And you know, of course, all these different you know, aspects are required in the realization of a system. But what's happening is that with the increasing complexity of silicon and systems, more software and the like, you know, and the need for an optimized user experience and the like, the interdependency has grown much, much stronger. And so there's a lot more of co-development, co-verification, co-optimization, which has been needed. And then you can see this in terms of our customer base as well. You got semiconductor companies not just doing silicon, they are also moving up the stack, providing more software, you know, in this era of chiplets and the like, doing more of this, you know, heterogeneous integration. System companies building silicon, you know, coming down, if you will, the stack in building purpose-built silicon. So we are seeing all of these different trends, which are, you know, have been coming along and accelerating. And so from a Cadence perspective, you know, it's, we kind of saw this, as you rightly pointed out, I mean, we've had a strategy in place for the last 6-7 years, our Intelligent System Design Strategy. And that was envisioned with the view in mind, you know, kind of to kind of help, you know, kind of to provide this full set of capabilities which straddle silicon and the system, you know, world, and we'll talk much more about those. But to enable our customers to kind of realize these, these different, you know, opportunities, the, the foundational aspect of our strategy at the core, the heart, is our, our computational software, you know, expertise that we have, right? I mean, we've been in EDA now for over 30 years. We've developed a tremendous amount of capabilities and skills in computational software. And then, when you think about a strategy, think about this as, you know, the best way to kind of describe it is as a, as 3 concentric circles. The innermost circle is silicon, the middle circle is about systems, you know, the mechanical, electrical, and the like, and then you got the data circle, which is the outermost circle. And so when you overlay our computational software, you know, to these three circles, you put computational software on silicon, that's EDA. You know, computational software on the system circle, and that's system analysis and simulations, and what we call SDNA, system design analysis. And then on the data circle is AI, you know? And so that's basically, you know, our strategy, and we've been very steadfastly, you know, kind of executing to that. So, what we've realized is all those, you know, the computational software heritage that we have, you know, on the core EDA algorithms, the techniques, the solvers, and the like, are very portable and leverageable, if you will, to the computational part of, you know, the system and the data space. So we are absolutely confident when we embarked on the strategy, you know, seven years ago, you know, we're absolutely confident that there was not just a need for this continuum, if you will, to deliver to the challenges of the hyperconvergence which were happening, but that we could make a disruptive kind of influence, have a disruptive influence on that. And, and we're really happy that it's played out. I mean, to the second part of your, our question, you know, on the, how has it evolved? The strategy hasn't really changed. It's just more methodical progress, you know, on the delivery of, of that, of that strategy. You know, at the core is EDA. We've got to make sure you've got best-in-class tools there, but gotta make sure that, you know, we invest accordingly to make sure that they continue being best in class, and they evolve to meet the increasing needs of the market and of the customers. And, and then, you know, we, we invested, we did organic investments, initially in the system analysis space, and, you know, and then we have methodically built up a portfolio over there. You know, and you know, the latest of that was with the acquisition that we announced of BETA CAE, and we can talk more detail about the systems portfolio. And then, you know, we also looked at this in the context of what's the next big thing likely to be, and we feel life sciences is that vertical. And so we also made an investment in, you know, molecular modeling, molecular simulation with this acquisition of OpenEye. So that's basically, you know, how, how we view the strategy, how it's evolved. I mean, and we feel very, very good about the strategy in terms of, you know, that's the vision, and very pleased with the execution through both organic and inorganic means, and, you know, very thankful, obviously, to our customers for, you know, their faith and confidence in these solutions. Charles, you are on mute. Yeah. So Nimish, you know, I attended Cadence Live at Silicon Valley last month. And by the way, thanks for the invitation. I recall the fireside chat between Anirudh and Jensen Huang of NVIDIA. And Jensen is basically saying NVIDIA uses several Cadence products. He actually gave some really specific shout-outs to a few products, Virtuoso being one, that's Cadence custom design tools, and Palladium. He spent a lot of time talking about Palladium. That's Cadence emulation hardware, right? And I think he also touched upon something called the Reality 3D, that's- Yeah ... the data center simulation tool. So I'm sure Cadence, NVIDIA probably buys more than just these three products from Cadence, but I found it quite interesting that two out of the three products Jensen highlighted are not quite the traditional EDA software tools, but more like system design tools, both hardware and the software, right? So can you kinda give us an overview, Cadence system product portfolio and why they are relevant to the future of the industry, not just the semiconductor industry, but the probably the more broader electronics industry, or even broader than that, especially in the context of AI? Yeah. Yeah, Charles, I mean, yeah, it was great. You know, Cadence Live was great. It was great to have, Jensen over there, and, you know, listen to his views about the massive kind of global transformation, which is happening, you know, driven by AI, and how, you know, NVIDIA is helping accelerate that transformation and, you know, talk about the partnership with Cadence as well. You know, I mean, we are really privileged to have NVIDIA as a customer. Not just a customer, but also a valuable development partner, you know, a teaching partner. And, so when you talk about this macro level, you know, kind of systems, kind of perspective, you know, we talked about the hyperconvergence happening, hardware, software, mechanical, electrical, and the like. Then you tie that, you know, to look at Palladium. I mean, that's a classic manifestation of all of those different things together. You know, very complex supercomputer, you know, that, that we have built. And, and this is something which, you know, has got hundreds of reticle-limited, you know, kind of dies in there, multiple racks, liquid cooled, very complex, and it's solving some of the most complex challenges our customers have. I mean, you heard Jensen talk about saying that, you know, it was very- it was essential, you know, for, for designing, a Blackwell. You know? And so I think, this, this is, the hardware piece of it is just one aspect of it. Now, taking a step, kind of, you know, abstracting it and, you know, talking about your question on system. So when you look at our system portfolio, it's got two main components. There's the system design piece, there's the system simulation or analysis piece of it. And, you know, system design is, you know, where we got our packaging, our PCB solutions. We've been in it for over a couple of decades now. And now with this, all these trends happening on 3D IC, on, you know, chiplets, heterogeneous integration, and like, it's, you know, we are in a really, really good position to kind of deliver to the needs of the moment, if you will. And it's a very complex set of capabilities which are needed to you know, to realize this promise of chiplets and the like. You know, and we've got our platform called Integrity 3D-IC Platform, you know, which is the only platform out there, which is, you know, uniquely and natively integrates all of these capabilities under one umbrella. So you, of course, you need the implementation. Think about this as three layers over here. You absolutely need the implementation, analog, custom, digital design implementation. You know, then, of course, you need packaging. I mean, you can't have a packaging solution without packaging technology there. So it's, you know, the advanced packaging, which is needed. And then on top of all of that, you need system analysis, 'cause, you know, the challenges of analysis, be it electromagnetics, be it thermal, you know, are very acute in the manifestations of these chiplets. You know, you need in-design analysis capabilities there. So we provide all of these, you know, through our Integrity platform. So that's that on the system design piece of it. Now, you know, I've said this before as well, but, you know, unlike when you look at core EDA, there is, you know, the Moore's Law is your forcing function. You have the need to retool and reinvent yourself, keep up with the latest, you know, technology changes every 2.5-3 years, else you kind of, you know, fall behind, you become irrelevant. There's not been that forcing function in system simulation. You know, there are tools out there which have been, you know, kind of there for a while, legacy tools. They're good, good tools, but, you know, there was a great opportunity for, you know, kind of coming at it and disrupting that market. You know, as I mentioned, you know, with our core EDA heritage computational software leverage, we felt we could make a disruptive difference, and that was you know, what our decision to go into, into the system analysis kind of space. And we started off with, you know, organic development. Both Clarity Celsius electromagnetics and electrothermal solvers were organically developed, and they, they were disruptive. I mean, Clarity came in with, you know, 10x more performance, no loss in accuracy, and massive capacity. So you don't have to piecemeal, you know, your design and then simulate different pieces, stitch them together. You could kind of, you know, do be it the car, be it the airplane, you know, and the like. So you know, this was a very big step forward in terms of kind of, you know, starting to have a disruptive influence in that market. So we think that, you know, when you look at the space, and you know, you heard Anirudh talk about this, that when you look at the EDA space, a lot of this, 99% of the design is all simulated, done digitally and the like. Versus when you go in the system space, for various reasons, only about 20% is. So this whole shift-left paradigm, you know, where you want to get away from doing a lot more of the physical testing and move it up in the chain, and not just, you know, flush out issues earlier, but also, you know, come up with a better design, you know, because you're exploring more at an earlier stage. I think those are... That's the opportunity, and that's the promise, and the potential of the future. And, you know, so now we've kind of built up our generative AI portfolio as well. You know, these tools kind of sit on top of our simulation tools, so what you call our principal simulation tools, and, you know, a couple of them in the system space, Allegro, for example, are, you know, PCB automation. You know, there's never really been much automation in the PCB design space, and now with Allegro X AI, you know, we are really, you know, showing some tremendous results for our customers, 4x to 10x productivity benefits. One of the customers talked about that at Cadence Live. And then for systems, we have the Optimality Explorer. So this is not just simulation. I mean, simulation, of course, is needed, but it's also about optimization, right? You know, so you obviously want to do, you know, productivity and do, you know, things faster, but you also want to come up with a better design, and that's the really big opportunity, which we've seen a lot of in EDA, but now to extrapolate that and make that happen in the system space. So that's at a high level, how we see our systems tools and, you know, be it design simulation, and how simulation tools overlaid with the AI tools, and how they are being, you know, really helping our customers across multiple verticals. Great, thanks. I think I definitely have more questions to follow up on some of the things you mentioned. Like, you guys want to make a disruptive difference, right, in the system space, when the simulation kind of simulation coverage in the space is only, like, 20%-ish, based on what you just said. But before we go there, right, I think I have to throw in more of a near-term question on hardware. I mean, hardware, I don't know, it's probably still considered as a part of the more traditional semiconductor business, but that they have something that feels like a more software/hardware co-design, more of the system. There's a system angle to that. So we know Cadence has been executing well, overall, but relative to the high expectation, right, we can debate whether they are too high or not. But the last quarter, well, that was not perceived as a spectacular results, or print. So one of the reasons for the guidance, I think that was probably seen as the issue, right? Coming in slightly below consensus, it seems like it's a hardware revenue being a little bit less than expected, and part of the reason is the transition, product transition from Palladium Z2, Protium X2 to Palladium Z3 and Protium X3. So can you provide us, some color on why this transition has had an impact on the financials, and why investors should or should not worry about it? Sure, sure. So, yeah, so let me, you know, start off with a, a macro-level kind of view, you know, of emulation and verification. You know, now, verification, you know, is a, is an NP-complete problem, right? You're never really fully done with that, and it's probably the most challenging, you know, problem that our customers face, you know, probably accounting for the largest portion of R&D headcount as well. And then, so when you're looking at where the market's going, the trends going, and its chips are becoming much more complex, you know, Blackwell, right, 200 billion, you know, transistors running many more, you know, sophisticated application and software, the verification challenges grow exponentially, you know? And so, you know, when you add N transistors, you're just raising the number of states, right? There's two, and you got, the verification complexity goes up by 2 raised to N. And so the traditional ways of doing verification, which is by simulation, just doesn't, absolutely doesn't cut it anymore. You're just not gonna get too far with that. And that's where hardware-assisted kind of verification, like emulation, FPGA-based prototyping, kind of, you know, come in. You know, so when we look at that and, you know, and you say, "Well, okay, you've got the simulator running at, you know, you know, in kilohertz, you got the actual chip running in gigahertz, and you got in between, you got the emulation, you know, platform there, you got a prototyping platform." Depending upon the use cases and the where you are in your design cycle, early stage RTL, late stage RTL, software bring up, you know, you use these two platforms, and several of our customers are using them in tandem. Now Z2 and X2 are already the best products in the market, and, you know, R&D team kind of actually, you know, hardware R&D team performed a near miracle. I mean, they came up with Z3, X3 in just about three years, you know, virtually half the time. Now just, you know, using Blackwell as an example, right? 200 billion transistor chip was designed, you know, with eight racks of Z2. Now, when you look at, you know, Z3, you know, 16 racks out there, and, you know, we actually can handle, well, you know, about 48 billion, kind of gates, which is about designs five times the, you know, the size of Blackwell, right? So it's really future-proofed in that regard. Hundreds of chips in a rack, hundreds of billions of transistors overall, you know, across the rack, reticle limited, liquid cooled, InfiniBand, optical connect, right? And also, by the way, it's aside from capacity and performance, you know, there are also some new capabilities that we provide, which is four-state emulation, right? So that's really great for low power, you know, kind of verification, and also analog mixed signal modeling and the like. And so, you know, this is a very complex supercomputer, but it's also solving some very addressing very complex challenges, you know, for our customers. So, you know, Now, of course, we've had beta customers. We talked about NVIDIA, you know, Arm, and AMD, who you know as our beta customers. You know, so when we looked at, you know, the interest which we are seeing from customers, you know, when we announced these products, there's a lot of interest from customers on this, and we fully expect, you know, the, a very strong, you know, kind of ramp-up of that happening in the second half. Production of these systems happens in Q3. You know, we're taking orders now, and, you know, so, you know, and we are building, we are planning to scale, right? We are building up for scale right now, to meet the demand. And so Q3 is gonna be, you know, much more in compared to Q2 in terms of that, and then Q4, obviously more than Q3, and it's gonna go into 2025 as well. And so, you know, given the strong interest in these platforms, we've just been kind of prudent on what Q2 kind of meant, looked like for Z2 X2, and being a little bit more conservative with our assessment over there. But, you know, we are absolutely confident about, you know, this is the right step to take long term. It's addressing these problems. And, you know, when you look at AI and the workloads of AI and the AI chips and the like, I mean, these are just all very, very big chips. You know, more, more you can put on, the, you know, the better they are at addressing the needs, right? And so you can fully expect these, this to continue. So this is the right thing for the market, the right thing for our customers. So, you know, we kind of, you know, came up with these systems, announced these systems, and we're gonna be ramping up pretty aggressively there. And, and then, of course, Charles, as you know, we always remind investors, right, that, "Hey, we manage our business for the long term," you know, and not to look at any one, you know, quarter or two quarters, first half, or whatever. We had a record year on hardware last year, and we fully expect this year to be another record year. So that's how we look at that. Thanks. Fantastic. Well, especially when you said, this new Palladium Z3, Protium X3 is capable of design five times a chip, five times larger than Blackwell. Well, the future is definitely very going to be very interesting and exciting. So let's abstract or zoom out a little bit more. We know emulation, prototyping hardware, well, it's very successful for Cadence, but in January, you guys also launched another hardware product called Millennium M1. It's also a supercomputer, but it's not for semiconductors, it's for CFD simulation. I'll leave it to you to explain what is CFD to investors. Even Jensen, I think he said at the GTC that Cadence, as a software company, is now making hardware, right? I think he's not only referring to Palladium, but also Millennium M1. So can you tell us why this product, and why does Cadence believe the company can become successful in this hardware supercomputer CFD business relative to the incumbent solutions? And please remind us, what are the incumbent competitors you have? Yeah, so it's a good question, right? So again, let's start off from a strategic perspective. So when we talk about the realization of our intelligence system design strategy, you know, CEO Anirudh, right, he talks about this in the context of a three-layer cake, which is very apt over here. Because all these three layers, you know, are optimally, you know, the optimal benefits come when you are partaking of all the three layers. The middle layer is what we call, you know, is our simulation and optimization layer. You know, all our software tools are over there in that layer. We call it principles simulation. You know, it's first principles-based, right? Physics-based, chemistry-based, and the like, and it's providing the physical intelligence. Now, on top of that is our AI layer. You know, our generative AI tools, you know, LLM copilots and the like, you know, call it the data intelligence layer. And then all of this is running on, you know, specialized compute. Now, could be CPUs, DSPs, GPUs, our own Palladium, for example. And, you know, and so this is, you can call it the, you know, it's accelerated compute, and call it the compute intelligence layer, right? So you got the principle intelligence, you got the data intelligence, and you got the compute intelligence layer, right? So you know, Palladium is a classic example of that. Now, when we talk about this in the context of system analysis and computational fluid dynamics, you know, we mentioned, right, earlier, that in the chip design, 99% of the design is all done digitally, virtually, you know? And you get 99% coverage, if you will, before you tape out. Now, the system world, for multiple reasons, largely because of accuracy reasons and the time it takes to do all the simulation, it's just about, as we talk about 20% of the, for example, an airplane, only 20% of their flight envelope is really, you know, simulated. And when you're talking about that, and that's really much more in the context of steady state. But when you're talking about takeoff and landing, and the like, there with, you know, there's a lot more complexity, turbulent flows, and a lot more effects to be considered. And even there, you know, there are only certain portions of that are done, you know, with simulation. Of course, the whole thing is still validated, but done by the traditional, you know, you know, physical testing and the like. And the goal is to kind of move this up as much as possible. Now, you know, the traditional CFD tools out there in the market, you know, are just- they're just not capable of handling this level, and, and there's a reason why the state of simulation is the way it is. So, you know, we, when we, a couple of years ago, we, you know, acquired this small company called Cascade from Stanford. Very good heritage, excellent team, excellent technology, and they'd come up with some remarkable algorithms on CFD, which, you know, got much, much, much higher accuracy. Now, of course, you've got to do much more compute for that stuff and, and the like, and, and so CPUs were just not good enough, but they also was very, very easily scalable to GPU, and you're getting, you know, a really remarkable kind of speed-up, you know, by running that on a GPU. And so that's basically the promise of, of, you know, the potential of this. So when we announced the Millennium M1 supercomputer, it really is that. You know, so we're creating, you know, it's similar to the Palladium, as you mentioned, it's our own supercomputer, but targeted for a different, you know, kind of kind of, you know, set of verticals over here, and solving a different problem, if you will. And the response from customers has been awesome because, you know, one rack, one Millennium rack, is replacing, like, 32,000 CPUs. I mean, it's just... You know, so when you, when you look at that and say, "Wow, you got this, you know, tremendous, you know, performance enhancement and accuracy," this is really going to be helping this whole, you know, shift left paradigm to be realized much more. And, you know, we expect that to be, you know, more and more broadly proliferated. Since the announcement, and of course, we had beta partners, customers at the time as well, but, you know, we, since those early days as well, you know, we've actually had customers who come back and, you know, gotten more of this and proliferating it, you know, across, more of their designs. So still very early stages, Charles, but, you know, this is again, you know, this is a long-term kind of endeavor, and we see the potential, you know, to come up with something which is disruptive to solve a real and growing problem for our customers. And so that's basically how we, how we, how we view it. Yeah, I kind of feel like you guys, your software is helping your customers to design GPUs, but at the same time, you guys are also trying to use the best technology, like GPUs, into like Millennium type of the supercomputer solutions for customers where may still be using some really legacy stuff, like a actual physical testing or CPUs, for example, right? Like you said. Yeah. Um- So, so just on that one, Charles, I mean, just to add on to that, I mean, that's a very good observation, right? So when we think about it from a context of AI, right, and by the way, you can, this AI is another, you know, like I said, the data intelligence layer, right? So it's not just the, the, you know, the, the accelerated compute piece of it and the simulation. There's the AI layer on top, and that's the overall, you know, value prop, which just enhances it tremendously. So when we look at it through a different lens of, you know, how is AI really helping our business, right? In that context, what you just said is very true. So not only are customers using our tools for designing their own AI chips themselves, that's one aspect of it, we ourselves are using AI internally for our tools. We now witness all the generative AI tools that we have to make them better and more full-featured and more capable for our customers to build better, you know, solutions. And then you got the, you know, the opportunity with this AI to go into these newer markets or newer areas, which were previously not possible, you know, to kind of do. And so be it with, with the, you know, the Reality, you know, digital twin solution that we have. Millennium is an example over here, you know, going into bio and the like. And so I think this, there's multiple, you know, layers of goodness, you know, which come together. And so we look at all of this in a very holistic manner, and making sure that each of these layers, that we are very well positioned on each one of these, and the real value comes in when you look at it as a slice of the three layers. Like Millennium is a slice which is providing all, you know, pieces of that and going through. Yeah, very exciting stuff. Thanks. Thanks for the additional color. So, Nimish, I just—we heard at the CadenceLIVE event, right? That data center, automotive, and bio simulation, that feels like they are the top three priorities for Cadence in systems. And so at Cadence, you manage the new ventures, right? Notably, I saw OpenEye. Yeah ... is one of the, the venture you're managing, the biosimulation business group. So I think we understand this is probably more of the long-term thing, but you know, for investors, it's easier to understand data center, automotive, why it's important for Cadence. But why biosimulation? Well, I think you touched upon this a little bit, but isn't it a vertical that may be a little bit further afield from Cadence core verticals? Can you mind... Do you mind spend some minutes to, Sure ... shed some light? Sure, sure. Happy to, Charles. I mean, so yeah, I mean, in one way, when you think about this, you can say, it's the vertical of life sciences is very different than automotive or consumer. But, you know, from our perspective, the key unifying element across these is the computational piece, right? We talked about the computational software. We think it can be, you know, very, it's very leverageable, that core competence we have in computational software, and it can be a massive disruption in innovation, and it can transform this, drug discovery process. Just, you know, picking up on the 20% that I mentioned about the system simulation, when you apply it to bio, you know, so that's about 1%, or 1 or 2% of that is only done, you know, with the digitally, with simulations and the like. You know, it's all, most of it is the traditional wet lab and all those approaches, right? And when you look at the amount of R&D spend, pharma R&D spends, like, $250 billion, right? And the simulation market is probably $1-$1.5 billion. I mean, so it's a very small smidgen of that, you know, piece of it. So I think, when you look at the bio market, I mean, they actually, you know, it's, it's, it's almost the inverse of Moore's Law. You know, we have seen that, you know, that the number of FDA-approved molecules per global R&D, pharma R&D spend, it's reduced by half every nine years. You know, they actually got a name for it. The re- inverse the Moore, they call it Eroom's law. I mean, and so I think, you know, the, the opportunity is really there for simulation, for AI, to kind of come in and reshape that whole pharma funnel, if you will. And so that's been, you know, that's our thinking, you know, behind it. And we feel like, again, you know, we're, we're very, very good in, in, in, you know, transistor simulation. And, you know, here, it's also, you know, looking at molecular modeling, molecular simulation, any interactions between the different molecules and like. Very good at, you know, that, so it's very easily leverageable. And then AI, of course. And then, you know, at GTC, we announced this, you know, partnership with NVIDIA on this, right, with BioNeMo and BioNeMo services, integrating with Orion, which is our molecular modeling and simulation platform. And so using that, you know, the LLM framework there, and, you know, integrating it. So this basically helps with protein structure prediction, identification of new molecules, and the like. Very, very early stages, but this is what the promise is. And, you know, in my opinion, you know, few years down the road, this can probably dwarf any of the opportunities that we're seeing when playing in today. So from our perspective, you know, we always, you know, strategy's a long term, right? You always think about strategy in decades. You don't think about strategy of the year or so. So we think about this in three horizons. You know, our first horizon is our core, you know, core EDA, right? That's the first horizon, continue investing and being best-in-class on engines there. Second horizon is our system analysis and design and analysis, which we are already, you know, well there in that and scaling. This is the third horizon. Bio is the third horizon. Think about it 5-7 years down the road. So it's a small investment we made with OpenAI, learning a lot and contributing to that with our computational software heritage. Super exciting, but early innings, but a lot of promise for sure. Thanks, Nimish. Well, I have two more questions, but, given the time, I think I'll only ask one. Okay. But, I think this is going to be very relevant to you, and Nimish, I have to ask you about M&A. Okay. Um- Okay ... we know there are lots of M&A going on in the EDA space right now, yeah, and including some of the largest deals in the industry that is happening right now. There are lots of news, some are true, some are kinda questionable out there about EDA, M&A as well. So how does Cadence think about our M&A strategy overall? And specifically, I mean, what role does M&A play, as you guys seem to prefer organic growth? That's just a sense I got, 'cause John Wall said, "Organic is delicious. Yeah. That's one thing he said, but how do we think about that? Yeah ... organic versus inorganic? Sure, sure. Yeah, for sure, Charles. I mean, you can't believe all you read, right? I mean, there's some pretty creative stuff out there. You know, our strategy hasn't changed, you know, since we set it up in 2018, right? And we continue executing to it. I mean, the one thing I do wanna point out is that M&A by itself is not a strategy, right? M&A is not a strategy in itself. It's the outcome of a strategy. It's one of the means to realize our strategy, ISD strategy. M&A is one of the means to realize, you know, that strategy. And so, you know, we are very, very thoughtful about M&A, and disciplined about how we go about doing it. But, you know, taking a step back again, our... We gotta, you know, our primary focus is our core business, right? Core EDA. And, and by the way, I mean, from 2018 to now, the core EDA business, if anything, has become even more meaningful, you know, given all the AI super cycle and, you know, all it means. I mean, for our customers, you know, core EDA has become even more meaningful, and for Cadence, it's opened up even more opportunities. So we continue doing what we have to do there, and it's all organic, right? It's virtually all organic in the core EDA space. I mean, we, you know, you've seen all the engines and new innovations that we've done over the years and continue doing. And so that, you know, is the core EDA. And of course, you know, we got tremendous leverage over there in terms of sales channel scaling. You know, they can scale whatever technology we come up with in core EDA. You know, and you know, the customer base is very familiar, you know. And so, you know, that's really where we focus a lot on, and it, as you said, it's organic. Now, where the bulk of our acquisitions have been, and, you know, there are two components of that. One has been, it's largely been in new spaces, you know, where we can get new technology, new domain knowhow, new customer reach, if you will. You know, and-... And then you know get that, and then we have done a few of them in system analysis, you know, and the other aspect to consider is scale. You know, we all of these are what we call tuck-in acquisitions. You know, they are very contained, they are very good in these regards that we just talk about when we bring them in, and the goal is to get them in, integrate all of these together, create a platform, full platform, multi-physics platform, for example, build that together, and then infuse it with all the goodness we have on computational software so that you in the end, you know, that whole is way, way better than the sum of the parts, if you will, right? With that what you come up with. And so that's been our, our strategy, and that's what we continue, you know, kind of focusing on. And, you know, we've also done some M&A in IP, you know, as well, for, you know, new protocols. Again, 3D IC, and, AI have opened up some opportunities there, so we've done some acquisitions over there in IP. But again, all of these are, are really, you know, you know, been tuck-in acquisitions. And, you know, acquisitions, as we said, you know, we are very disciplined. If they fit and further our strategy, they've great, great technology, great talent, you know, and we have the opportunity to bring a really good return on that investment, then we consider those. And they have been, you know, kind of tuck-in. The latest one is BETA CAE, you know, which we're waiting to close on, but opens the door up to a brand-new market for us in structural analysis. And, so anyway, that's, that's the way we, you know, we view things through the, through the, strategy lens of where M&A fits in, and, and then what type of M&A we've been doing and what our focus is, you know, is, is, the way I outlined it. Thanks. And maybe, well, we're almost at the end of the session, but do you mind if you just take one really short question? Sure, please. I think this question probably is not going to be short, but how would you compare and contrast the tailwinds from three mega trends that's going on? One is the design complexity, and two is AI, and three, I think this is probably talking more about the insourcing of chip designs. Probably this is more about the system companies. These are the three trends, major trends that's going on among your customers. How do you think about these three tailwinds? Well, I mean, design complexity, I mean, these are all intertwined, right? I mean, this is where, you know, you're looking at this in the context of complexity, and as we said, complexity just continues growing, you know, tremendously. And it's, it's the complexity coming in, not just in terms of scale, bigger chips and the like. Of course, that's one level of complexity, which has got tremendous, you know, underpinnings of that which need to be worked through, but more software, more targeted applications. You know, and so complexity is coming in the form of multiple, you know, kind of, multiple ways. And then, of course, as you're going down the physics route, right, you're going down, you know, 3 nanometers, and 2, and 1.4, and 1. Then on that, you overlay the complexity of, you know, heterogeneous integration, you know, with 3D-IC and chiplets. So complexity by itself is driving the need for tremendous, you know, need for more automation, more help, and that's where AI kind of coming in, right? AI is kind of helping in a way, to kind of manage, if you will, the complexity. We are still. I mean, you talk about, Charles, I mean, you talk about 200 billion, you know, transistors going to 1 trillion by the end of the decade, 5x increase. You're not gonna have 5x increase in headcount to kind of, you know, enable that to happen. Of course not. Now, headcount will increase, customers will have more engineers, but that, you know, AI is gonna help you, you know, tremendously help you, you know, with respect to the ramp, which is required over there. And not just the number of engineers, but making those engineers, you know, much more democratizing, you know, if you will, the talent in the engineering base and making engineers much more productive. So I think it dovetails right in there. To manage that increasing complexity, AI is helping, but AI is more than just about productivity and more than just, you know, time to market. It's also, as I mentioned about, we have a very good strong view of AI being help for optimization, coming up with a better answer, you know? And so it's, yeah, it's a multi, you know, dimensional. It's like a Swiss Army knife, right? You've got multiple tools in here, which are very, very, each in and of itself is very, very pertinent, be it productivity, be it fewer resources, be it faster time to market, be it better quality, be it a better chip or a better system. But when you put it all together, man, I mean, that's phenomenal. And that's why we- one size doesn't fit all, that's why we've got a portfolio of GenAI. And, you know, and then to the systems question, it's not just applying it, you know, all of this to your, to your semiconductor base. There's clearly the opportunity, you know, we talk about a $1 trillion semi market, it's about a $3 trillion system market and growing, and as this convergence is happening between systems and semis and the like, you know, it's just the opportunities are limitless. So I think all of these, these three trends, I should say, are all very strong tailwinds in their own right, but together, you know, they create a massive kind of a set of challenges for customers, but also massive opportunities for us. And this is why ISD, our strategy, is perfectly aligned with that, etc. All right, I think, we are at the end of the session. Thank you, Nimish and Richard, for joining us today. Appreciate all of you guys. Absolutely. Thanks for the opportunity. Yeah. No problem. Have a good day. Thank you. Thank you. See you. Bye. You are clear. All right. Great. Thank you. Thanks so much, Nimish. Thank you, Charles. That was great. Yeah. I appreciate the... like I said, really appreciate the opportunity, and- Yeah ... let me know if you hear any feedback. Yeah, yeah. Oh, one quick clarification, if you may, stay, like, a couple minutes. For sure. Yeah. I think one thing you mentioned is about one rack of a Millennium replacing how many CPUs? Thirty-two thousand. Good. Okay, I just wanna make sure, if I heard that correctly. Yeah. The number is massive. It's one rack. Yeah, one rack. Yeah, you know, yeah. Okay. Okay. Yeah, it's mind-boggling. It really is mind-boggling. I mean, yeah. Okay. Yeah. So all right, I'll head off to the next one here. All right. Thank you. Good luck. Talk to you later. See you. Bye.