How do you define and drive productivity of the infrastructure over kind of simply token activity, right? You

know, one thing that's that's pretty unique about us is that we're probably one of the last at scale applied ML shops.

If you if you take kind of the hyperscalers out of the out of the picture and we own and we own our full stack right from from, you know, the experiences our users interact with, obviously, you know, predominantly powered by AI to bare metal.

We have our own data centers, we rack our own GPUs, and that creates a real strategic advantage for us when it comes to balancing quality and efficiency in our AI.

We can look at the specific workloads that we want to run uh and then we can optimize entire stacks a really good example is what we talked about last time you know matt when we're talking about semantic people search semantic job search we you know we can do things like model compression uh sorry model pruning to be able to make sure that you know we can reduce model size which obviously means that we can get more throughput uh out of every gpu we can do embedding compression we can do context summarization And we can develop tools and infrastructure to do these things to improve efficiency and have that be something that's really easy for our applied ML engineers to do, right? Because at the end of the day, they want to be focused on shipping product, not thinking about the costs of the products that they're shipping in half.

But it goes a layer further, right? We are optimizing our own kernels for efficiency, our own GPU kernels for efficiency.

We are... you know, adapting networking and storage based on the workload.

And as CTO, how do you balance this demand for high-speed, cost-effective inference with that control that's necessary, that data sovereignty required to protect your users? And when it comes to the board, how exactly do you justify this spreadsheet math and the return on investment in AI of these infrastructure choices?