Are enterprise networks ready for AI?

Let's start with the basics.

When companies say they're AI ready, they often point to compute, GPUs, servers, storage, model access.

That makes sense.

Without compute, AI doesn't happen.

But Brandon Ross says that that picture is a little bit incomplete.

What enterprises tend to measure is things like GPU and compute power, maybe some storage, um, but that really doesn't tell the whole story.

It's really more about the network, and it's not just about having a quality amount of bandwidth, but it's about, uh, moving data in a predictable and a secure and an efficient way.

Ross's point is that AI performance depends on moving data in a way that is predictable, secure, and efficient.

If the network can't reliably connect users, applications, clouds, AI providers, and data sources, those shiny GPUs that you bought may not translate into business value.

And Bill Long framed it a little bit differently.

He says that enterprise AI is following the same arc as cloud adoption, but the stakes are higher.

I think of it as kind of cloud on steroids, where, um, you know, i- in the old world where you had all your IT stack in the basement of your headquarters, that was, it was pretty simple, simple world, and you could put a perimeter around that, keep things safe.

But then really with cloud, it became a distributed hybrid multi-cloud kind of architecture where you had some infrastructure up in cloud, you still had some in your basement, you had some in third-party data centers.

And then providing the networking where you could connect all of those things together and start to manage it, that was, you know, that, that was pretty complicated.

That was, that was enterprises adopting cloud, and AI just put that on steroids.

In other words, the problems enterprises already had with hybrid cloud, things like distributed environments, security boundaries, data movement, and application performance, are all amplified by AI.

Let's start with the basics.

When companies say they're AI ready, they often point to compute, GPUs, servers, storage, model access.

That makes sense.

Without compute, AI doesn't happen.

But Brandon Ross says that that picture is a little bit incomplete.

What enterprises tend to measure is things like GPU and compute power, maybe some storage, um, but that really doesn't tell the whole story.

It's really more about the network, and it's not just about having a quality amount of bandwidth, but it's about, uh, moving data in a predictable and a secure and an efficient way.

Ross's point is that AI performance depends on moving data in a way that is predictable, secure, and efficient.

If the network can't reliably connect users, applications, clouds, AI providers, and data sources, those shiny GPUs that you bought may not translate into business value.

And Bill Long framed it a little bit differently.

He says that enterprise AI is following the same arc as cloud adoption, but the stakes are higher.

I think of it as kind of cloud on steroids, where, um, you know, i- in the old world where you had all your IT stack in the basement of your headquarters, that was, it was pretty simple, simple world, and you could put a perimeter around that, keep things safe.

But then really with cloud, it became a distributed hybrid multi-cloud kind of architecture where you had some infrastructure up in cloud, you still had some in your basement, you had some in third-party data centers.

And then providing the networking where you could connect all of those things together and start to manage it, that was, you know, that, that was pretty complicated.

That was, that was enterprises adopting cloud, and AI just put that on steroids.

In other words, the problems enterprises already had with hybrid cloud, things like distributed environments, security boundaries, data movement, and application performance, are all amplified by AI.