Jason Celino: Thanks, John. Thank you.
Operator: Our next question comes from Gary Mobley from Wells Fargo Securities. Please go ahead. Your line is open.
Gary Mobley: Hey, guys. Thanks for taking my question. Want to ask about JedAI and all the related machine learning and AI-enabled tools. We’ve been getting a lot of questions from investors in terms of how to think about how that becomes accretive to your growth rate and how it becomes accretive to your average deal size. Maybe if you can just share with us where you’re at in this price discovery phase and how you plan to, I guess, mass market price it? And if I’m not mistaken, this will all be included in digital IC, which according to the finish to the year was dilutive to your overall revenue growth. So how should we read into that? Is AI/machine learning simply just not impacting that line item yet?
Anirudh Devgan: Hi, Gary, great question. So first of all, I’m very optimistic about AI, and we always have talked about applying AI for optimization. I mean, in EDA or in chip design or system design, it’s more about automating the design process and producing better results. So even if you look at €“ the way I look at it, even if you get it right now, some of these chips have 100 billion transistors, right, on 1 inch by 1 inch. And if you look at by 2030, they will have 1 trillion transistors, okay? So just in terms of size, it will be 10x more. And then the chips are more complicated and then you add software on top of it. So the design complexity that our customers need to do will go up by at least 20, 30x in the next 5 to 7 years.
So the only way to meet that is by more automation. That’s the history of our industry. And the best way to do more automation right now is using AI, okay? And of course, we have done other ways to do automation in our industry. We started by doing more higher level design, moving from transistor level to gate level. Over the last 5, 10 years, we have done a lot of massive virilism, running things on more CPUs, using cloud. But going forward, one of the biggest ways to improve productivity is using AI. And you see that across our product portfolio. And the real benefit is that a lot of the mundane tasks can be done by the repetitive tasks and mundane tasks can be done by AI, so the designer can move to more higher-value tasks, right? And so the way we approach it through is a very comprehensive €“ we build this JedAI data analytics because data is critical, too, as you know, to a data analytics and AI platform.
And then we have multiple applications on top of it, okay? So Cerebrus is a key one for implementation. We also launched, a few months ago, Verisium for verification, which is another very difficult and all-consuming problem for our customers. And then we are not only focused on the chip side but also on the system side. So we have optimality, which is having great success on the system side. And typically, the system customers are not used to optimization or this level of automation that the chip industry has seen. But we are getting dramatic improvements with the optimality as well. So taken together, I believe that we have the most comprehensive AI portfolio. And we have always focused AI on optimization, which, of course, now called generative AI.
