Joanne Feeney

And so there's a lot of room for ASIC approaches to play roles in various types of models.

So we should expect that.

There is a lot of sporadic, I should say, information about how much AI agents and AI is being used in different settings.

So, right, there was a story today about how Microsoft has reduced its sales targets for getting AI agents to be adopted by their customers.

And so that has people concerned.

You know, Salesforce similarly is trying to roll out right their AI agent capabilities.

And some people are wondering whether that's going to happen.

But there are so many other applications in addition to those kind of smart helper AI agents.

And then I think the earlier in the week announcement with NVIDIA and Synopsys.

is a good example of that, right?

The industrial uses, whether it's these digital twins, basically being able to model out a whole factory or a whole building or any other physical presence in simulation, you need this kind of computational power, you need AI.

And so that kind of collaboration, I think, really points to lots of other applications for AI.

And then you go to biotech and pharma and

You know, computation, we've just been held back for so many years by its limitations from the CPU side.

And now that we have these GPU-based accelerators and innovations in fabric, like what Celestial is bringing to Marvell and what Broadcom already does, it is speeding up all of these computations, making far more applications possible across all industries.

Yeah, so X-Lite is developing a new laser technology.

And that is a fundamental ingredient to doing lithography, right?

ASML being the leader in extreme ultraviolet lithography, which depends on these very high-end lasers.

So X-Lite's taking a different approach to laser development.

And what's interesting is, and also reminds me of my past, is that this $150 million investment by the federal government, by the CHIPS and Science Act, the work is going to be done at a place called Albany Nanotech,