Intel Corp The Six Five Summit: AI Unleashed 2026

Hey, everybody. Welcome to The Six Five Summit 2026, AI Unleashed. We're continuing the conversation with another AI Infrastructure Spotlight. I'm Ryan Shrout, joined by my colleague, Matt Kimball. Today, we're going to talk about a challenge nearly every enterprise is wrestling with today. How do you build AI infrastructure? How do you build it fast enough? How do you build it flexible enough, cost-effective, to move beyond pilots and into real production, which seems to be a topic we touch on quite a bit these days. Joining us for this conversation is a good buddy of mine, Anil Nanduri, Vice President of AI Products and Go-To-Market for Intel's Data Center. Welcome back to The Six Five, Anil.

Hey, Ryan. How have you been? It's been a while. It's been good.

I'm looking forward to this conversation. I know you guys have some exciting stuff happening over there.

No, thank you, and great to have me on your show, and really looking forward to this conversation.

Great question, Matt, and I think the fundamental thing is a lot of our customers, they start thinking about hardware first. I think we really have to change the conversation to ROI. Why is that? Because it's all going to be around how AI can solve a problem. And from that perspective, you've got to really deconstruct how you're going to apply AI to what frame of the business and where you're going to drive the value. Based on that, you're really going to get a framing of what kind of budgets you're dealing with, what kind of cost economics you're dealing with, and what kind of a productivity improvement you want to be aspiring for. It's basic first principles of how you want to run through an operational aspect of it.

Now, interestingly enough, because AI is coming at such a rapid pace, you want to have that innovation engine going, which means that you want to do a lot of pilots, and that's fine. You sandbox them, create your pilots. But as you think about getting into production, you want to be a lot more clear about how you want to deploy it. There's a lot of things, like you said, is happening. It's no longer about token maxing, which is it's not like the highest number of tokens will give you the best ROI. It's actually the most efficient use of tokens that get you the ROI. And then I think I do want the audience to have a framing that not all tokens are the same. And then elaborate over the next half an hour what that really means.

But not all the tokens are the same, which means that you're going to have ultra-premium tokens, which is super low latency, super fast, very good for interactive. If you're trying to do some credit card fraud protection, you want to have almost real-time AI. You want that fast. On the other hand, you're doing some auditory work and you need to present an audit report in 20 days. You don't need that super fast AI. You could handle it very differently in a batch mode. So those ROI conversations start to play into how you think about putting AI into production.

One of the things that we hear about, we are talking about all through The Six Five Summit, is agentic AI. We went through this whole early LLM to reasoning LLMs to now agentic AI, and it is the biggest conversation that we have, and it dives into your point about not all tokens being equal. I am curious from an infrastructure perspective, what fundamentally changes when these AI systems begin planning and coordinating and taking actions instead of simply generating a response?

Ryan, another great question. What do agents do? They automate the work that we were trying to do. As more agents come in, you really need to have the agent loop to control how your agents operate. If you step back and look at how these AI systems work, we spend a lot of time trying to figure out where to run the AI. That AI is generating tokens, which is how you consume it. Token generation, as I said earlier, not one size fit all. You can generate tokens with small models and run it slow, but can very efficiently, even on a CPU, they are capable of running some level of tokens. As you know, GPUs start to go up the stack. GPUs are great for the flexibility they offer, some density of tokens you can run.

But if you are really wanting to get a large throughput and you want to really scale it, even the GPUs hit a Pareto limit. That is why you have solutions like Dataflow architectures, like an RDU or a Groq-like solution with LPUs. They are starting to kick and take the low latency, high interactivity, large volume of tokens that you need to generate. Token generation itself is a lot of optimization you can do, and then we can talk about that and to how you bring that into the business economics. The second part of it is what do I do with these tokens? These tokens are actually, especially in the agentic world, you are creating outcomes. Say I am creating an application, or I am trying to improve and refactor an application. As you are thinking about that, agents are helping you create code.

You are going to test that code. You need to verify, test, execute that code. That is where CPUs start to play a lot of role because CPUs are their execution engine, verification engine, and your sandboxing engine. If you see recently why are CPUs in a shortage is because a lot of these back and forth ping pong is happening in the system. You went a step ahead, as when I think about as agents come in, now you need to figure out who is orchestrating it, who is the quarterback? The quarterback function is becoming more and more important. It is about what we use the term harness, which is basically saying: how am I going to run what workload? Where am I going to run it, and who is going to run it? Here you are looking at saying: what kind of agents do I need?

What kind of agent loops do I need to orchestrate? What kind of security do I need? Do I need to run the model that is sitting on-prem and where my data is, or do I want it to be running in the cloud and move my data over? These are all going to be very important CIO decisions, and the enterprise have to really look at am I taking my data to the AI or am I bringing AI to the data? That's one way to think about it. But then as you execute this, you start to see different problems in the data center system. Your stress points are changing.

Your stress point moved from being about generating tokens, which by the way, is a key function and you're spending a lot of cost on it, to the running of the full end-to-end workflow where the wall clock time matters. These tokens, based on the systems you're running, you're stressing based on the context lengths, you're stressing memory, you're trusting storage, you're hitting databases, and now you're waiting on functions where if you're A good example is you're an enterprise and you're trying to look at your supply chain procurement, to a customer demand, and your financial tools to make sure when you can, say, invest. Say you have a huge amount of demand and you want AI to help you manage this.