Warren Bayek, Wind River (00:09):
Thanks for having me again. I'll be brief because I know lunch is coming. So I just wanted to talk through a little bit what it takes to get from where we are to where we want to be. And I mentioned it this morning, the move to AI-native being an architectural change. But that doesn't mean we have to, as I mentioned, blow up what we have now and start from zero, right? We're not talking about a full reset. We're talking about using what we have today and enhancing it in a way that makes this AI-native infrastructure architecture possible, because we have all the pieces in place right now, right? We've had enough resets. We have virtualisation, we have cloud-native infrastructure, we have orchestration, we have telemetry, we have data, clean or not. It's all available. So much of what I like, what you went through, Dave, with the progression from column one to column three, to almost to five.

(01:06):
I agree, humans are always going to be in the loop if telcos are involved. But what I'm saying is the underlying infrastructure is basically there, right? So we're talking about enabling that infrastructure to run these different AI workloads and different AI intent engines that you talked about, without necessarily understanding exactly where they're running. And that sounds a little strange, but where I'm going is back to the intent base, where the supervisor, if you will, and the little pieces under that, they don't need to know that this site has a GPU and this site has a specific accelerator, and that site only has CPUs, and this workload needs the cloud. And so all of those things are built in already. So I guess in the end, what I'm saying is cloud native, which we have now, that's not the destination, especially for 6G or 5G plus, which will be able to run more AI applications. It's just a methodology to get to the next stage, which is the AI infrastructure.

(02:04):
So this is showing with this one infrastructure to be able to run the workloads we're talking about, right? All of these AI-ready workloads. For years, we've used this infrastructure to run network workloads. They've had to do that. The cloud's very efficient at that. We're very good at understanding how to orchestrate network workloads and how to do workflows within that environment. What we're working toward is using this common cloud-native infrastructure to enable the same sort of architecture in the AI space. So not to run AI workloads, but to put AI workloads everywhere in the network, to make the network an intelligent network of which AI is inherently part of the network. It's not applications that run on the network.

(02:55):
Now, what that means is the objective here isn't to put GPUs everywhere, right? I don't want to ruin my Nvidia partnership agreements, but the idea here is to make sure that we can run intelligent compute where it makes sense strategically, operationally, as well as economically. And a lot of that work, frankly, will be run on CPUs. A lot of AI inference can and does today run on CPUs, and that's a lot of the work we've done and why we're here with Intel. Wind River and Intel work very closely together in this domain where we integrate their leading-edge CPUs, which have a lot of AI capabilities. Now, they can't run the hundreds of billions of parameter LLMs, of course, because they're edge workload systems. But you're creating an infrastructure that enables the things I've talked about today. You're creating infrastructure where the network itself can run intelligent applications wherever they belong, whether it's the virtual RAN, the 5G core, edge apps can all run the applications and orchestrate the correct applications at the correct layer in the network.

(04:05):
So basically, you have one infrastructure model running different workloads. And the final step in this is where we're going now, this bridge from where we are today and where we need to go, from automation to network autonomy. And this is something actually AI came up with, and it's very common with how Wind River looks at the world. We have the telemetry today, the contextual reasoning, I think a lot of people have talked about that. This is something that needs to sit above the entire infrastructure, and then you decide what to do, and you act on that. So you're actually performing something within the network, whether it's an application that manages a device, whether it's the network itself, healing itself, finding the MTTR to improve your understanding of what's going wrong, to hopefully be able to head off problems before they happen.

(05:00):
In the mundane world of maintenance windows, it's one of the things telcos have a lot of problems with is our maintenance window issues. So to be able to figure out how to make sure maintenance windows go within the time frame without a hitch, these are all things that fit into this self-healing and fully autonomous network. We have some of those in place today, but as we build out more AI infrastructure and AI-native infrastructure, those models will go even-- those parameters and those ways of doing business will be even more efficient. So the telcos will have what they're after, right? Truly AI-native networks. So the idea is to move from where we are today to a point where AI becomes part of the network, so the network itself is inherently AI enabled, and it uses AI to run itself as much as it's capable to run. And we actually have some demos of this working with some of the Intel parts and processes that Prashant will probably talk about. So thank you.