Yes, let me do my best with that. And then if I don't, get to everything just kind of re-ask what I miss. But that’s how we look at it internally. If I kind of broke down the pyramid of engagements that we have and that we're talking the type of customers we have, the top-tier or the major enterprises looking to deploy AI in the middle tier might be again significantly large enterprises, but not necessarily with the spend at the level of a Tier 1 project. And so it might be considered like a middle tier project. And then, of course, there's kind of, I would say, early-stage technology deployment, but really more for test of applications and really kind of getting their feet wet. So if you think at three different tiers, we look at each of the opportunities and how we play in them. I think the differentiation and you asked about competitors. The primary competitors we see out there are Dell, HP, Supermicro. And each one of those is a large-scale hardware player, with margins substantially lower than us in the business today. Now, I'm not here to try to argue whose models per se is better. It's just what we're good at and what we're focused on. Of course, the three companies I mentioned are not the only companies, but they are the examples of what we compete against. And if someone is trying to roll their own internal AI deployments and it's a hardware-only game, with those folks, that’s the line of business, that's the margin structure they are set-up to win on. For us, what we're good at and it comes with 25-years of history of deploying these type of systems and like, we're not talking about one server and generic software application. Just yesterday, I was over -- we have one of the earliest deployments of liquid immersion technology for a big customer of ours. That's in our lab, and our differentiation is we're out in front of the technology, we're learning about it. So we can bring it to market and we can design it for an environment that we've seen before, because over 25-years, a lot of organization, know-how. So whether it'd be on the design side, the actual deployment side, when you think of datacenters and the complexity of connecting massive amount of compute power with the memory, with the storage, with the networking and making sure the right power infrastructure is in place, this is not easy. It's probably just understated and with 25-years of history of this type of experience and as Ken mentioned, and I mentioned in my script, we've had some of the largest AI deployments in the world to-date. And so when you combine all that, our value-add, yes, we know-how to manufacture these products and systems, sure, but our value-add is how to design them for customer environment, how to deploy it and how to manage it.