Ali Goldzi

What our customers are facing, beside the supply chain challenges and the global stress that we're dealing with, there's a shortage of engineering.

They cannot get enough engineers to deal with that complexity.

So AI helps augment the existing engineers.

the second aspect of it reducing the cost you can reduce the cost by improving the yield memory is a fantastic example no matter how much the memory companies try to expand capacity by building factories you need to be able to have better yield that's where we come in to help them design the actual physics of manufacturing with the design phase to improve

the yield.

So those are the components and reducing the design cycle.

Traditionally, chip design was 18 to 24 months.

Now you have customers like Nvidia and other talking about the design rhythm of 12 months.

That is not possible without injecting AI everywhere in the flow.

And when you go to manufacturing, you're able to improve your yield.

Yeah, unfortunately, it was a naive approach to say that all software is the same.

You're going to have some software where LLM's foundation models, yes, is a perfect application to, let me call it, replace.

But when you look at the engineering software, I'll go back to the physics aspect of it.

We run, we code, and we deliver software to our customers, but that's not the mode.

The key differentiation are the solvers to translate from an architecture design to an actual manufacturing product.

So I believe it was absolutely an overreaction by putting every company that delivers software in the same categorization.