00:00Sassine, a lot of focus on the company has been in recent weeks and months about the relationship with NVIDIA,
00:06right?
00:06But the point of the next 48 hours is to basically set out what happens next for you.
00:11A big part of that is you do this big deal with Ansys and you have to tell the world,
00:16well, how are you going to use that?
00:18What's the technology roadmap from here? So outline it for us.
00:23First, thank you for the opportunity to explain the thesis behind the Ansys acquisition.
00:28The engineering complexity is compounding both at the silicon level, the chip level, as well as the intelligent systems, the
00:38AI-infused products.
00:40You hear companies talk about physical AI.
00:43How do you make that engineering task happens in a cost-effective way and tame that complexity in a timely
00:52manner?
00:53We're bringing together the silicon community, which is the chip design, with the physics of an end product together in
01:02order to deliver to that future.
01:04That's where Ansys leadership and multiphysics simulation, you need fidelity of the physics as you're creating a digital twin of
01:12that product and how to co-design it with the electronics part.
01:17We have it on good authority from NVIDIA and gentlemen.
01:21You're good at chip design, but I think a lot of people are searching for what other areas, business lines,
01:27products do you expand into beyond your history in the context of that acquisition?
01:34Yes, Synopsys traditionally has been the enabler of chip design, both on the architecture side, all the way into manufacturing.
01:45With the Ansys acquisition, it expanded our customer base dramatically.
01:50Actually, we went up more than 10, 11x in terms of our customer reach.
01:54If you think of an automotive OEMs, we are at more than 90% of the automotive OEMs using our
02:02software to both design the electronics and the physical aspect of the car, be it the structure, the fluid dynamics,
02:11etc.
02:12Now, the reason these two worlds are converging is driven by complexity.
02:16The complexity of these systems are going to be AI-infused, where you have agents running a robot or a
02:25car or a drone.
02:27How do you design this complex multi-domain of engineering and co-design it by creating a virtual representation of
02:36it?
02:36That's what Synopsys with the Ansys portfolio brings into the equation.
02:41AI makes it more complex.
02:43Many would say AI agents might be doing the design work themselves, might suddenly be looking at building digital twins.
02:51Talk to us about how you fended off some of the anxiety that software is going to be ripped up,
02:57the script ripped up by the latest, greatest agent from an anthropic, for example.
03:02Yes.
03:03So, you step back and think about it.
03:06Software is everywhere and is in everything.
03:09What we do, the special sauce is not the coding of the software.
03:14The special sauce is the physics, the algorithm, the solvers, the fidelity of the solvers.
03:21Imagine you are a semiconductor company that takes you about 18 months to develop a chip.
03:26And you spend hundreds of millions of dollars just in the development phase of a chip.
03:31You cannot be good enough before you go to manufacturing.
03:35You have to be 100% certain.
03:37Once you finish the design and you ship it to manufacturing, the product is going to come back and it's
03:43going to work.
03:43So, that fidelity of physics, because at the end of the day, the software we build has to translate into
03:49a manufacturable product that's going to work.
03:52So, this is where we're using AI everywhere in our products in order to bring an automation and a next
04:02level of simplicity and digitization to the workflow.
04:07But the key is the physics aspect of what our customers are doing and how we enable them to deliver
04:14to it.
04:15So, that's the moat.
04:17I'm interested as to just what your customers are feeling right now with supply chain angst, with logistics headaches, but
04:24also more broadly, they can't get their hands on the right things.
04:26They can't build them quick enough, particularly when we think about memory.
04:29How are you thinking about not just the difficulty in designing, but making sure that the yield is good enough
04:35to come up against what is a very difficult time?
04:38Exactly.
04:39What our customers are facing, beside the supply chain challenges and the global stress that we're dealing with, there's a
04:47shortage of engineering.
04:48They cannot get enough engineers to deal with that complexity.
04:52So, AI helps augment the existing engineers.
04:55The second aspect of it, reducing the cost.
04:59You can reduce the cost by improving the yield.
05:01Memory is a fantastic example.
05:04No matter how much the memory companies try to expand capacity by building factories, you need to be able to
05:11have better yield.
05:13That's where we come in to help them design the actual physics of manufacturing with the design phase to improve
05:20the yield.
05:21So, those are the components and reducing the design cycle.
05:26Traditionally, chip design was 18 to 24 months.
05:29Now, you have customers like NVIDIA and other talking about the design rhythm of 12 months.
05:34That is not possible without injecting AI everywhere in the flow.
05:39And when you go to manufacturing, you're able to improve your yield.
05:44So, Sassim, what conclusions did you reach in recent weeks after the sell-off in software names about the long
05:53-term definitive impact that AI will have on EDA?
05:57You're one of the biggest EDA names, right?
05:59But you're not the only one.
06:03Unfortunately, it was a naive approach to say that all software is the same.
06:09You're going to have some software where LLMs, foundation models, yes, is a perfect application to, let me call it,
06:17replace.
06:17But when you look at the engineering software, I'll go back to the physics aspect of it.
06:23We run, we code, and we deliver software to our customers.
06:27But that's not the moat.
06:29The key differentiation are the solvers to translate from an architecture design to an actual manufacturing product.
06:36So, I believe it was absolutely an overreaction by putting every company that delivers software in the same categorization.
06:45Looking at Bloomberg Intelligence, they're looking positive on your longer-term operating margins, on the ability for Ansys to really
06:52provide profit lift.
06:54But just talk to us about growth in the here and now of the design IP business.
06:59Are you going to get to more than sort of low single-digit growth?
07:03Are we going to see a re-acceleration despite China being such a tough market right now?
07:08Yeah, so we had a couple of headwinds.
07:11One of them is China.
07:12China was growing for us at a 20-plus percent CAGR over a period of about three to four years.
07:19That has slowed down for all the reasons that we know of in terms of restrictions and export control.
07:26Now, despite that headwind, the proliferation of silicon design and the complexity of designing that silicon, it's giving us an
07:35opportunity in many, many regions and across the customer base, be it AI, HPC design, automotive, mobile, consumer, etc.
07:44Now, what we have communicated is we reinforced our long-term commitment to a double-digit growth in EDA as
07:56well as IP in the mid-teens and the Ansys portfolio is as well in a double-digit growth.
08:01And the reason for reinforcing that confidence and commitment is the massive acceleration in engineering and the transformation we're going
08:11through.
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