You're starting to get headlines about, I guess, a corporate reckoning with AI as more people experiment and spend money on it. The Uber headline that you mentioned, apparently Uber burned through its entire 2026 AI budget in four months basically. And like what's more important is the COO was actually asking whether or not that was worth it, like whether they saw productivity gains or whatever as a result of that.

I predict is that companies are like clearly you know they're going to keep using it more and more would be my guess but there will probably be a lot of investment made in sort of like optimal model routing because some models are like a hundth per query of what a frontier model is probably a lot of people don't know like what is the sort of like efficient frontier model usage and So actually routing the query to the sort of most uh efficient model. I have a feeling we're going to see a lot of investment in that area specifically.

You can kind of conceptualize this matrix of different sizes of workloads relative to different sizes of GPUs. And all of a sudden that tells you my god like H100s could last six, seven, eight years. A100s are going to last longer. And it totally changes the entire conversation around depreciable life of infrastructure.

We have hyperscaler clients who continue to grow with us. We have AI lab clients. Um as I said, nine of the top 10 AI labs on the planet choose Core Reef. And then we have this enterprise base.

On our end, I think we have 10 over1 billion dollar clients at this point and our financial services client backlog is in the tens of billions of dollars at this point.

The scenario you presented anthropic would be our client. So what I'm highlighting I want to correct the number I said earlier our financial service clients and this is direct to those financial services they're approaching 10 billion in backlog okay so this would be you know a good example of this and that's something we made recently is with Jane Street okay right that's not Jane Street coming through open AI or anthropic to get to us that is Jane Street coming directly to us and using our platform.

At the end of the day we don't know what exact workloads okay these entities are running. Um especially for entities like Jane Street. I would imagine that's highly secretive. Um but the point I would say is more that this is not them coming through an AI lab to us. They are interfacing with and managing the infrastructure directly on our platform.

Now they're coming and saying well actually we want it for five years and we don't want any interruption of use. We'll commit to the exact same economics throughout the full duration of the contract. You can't upgrade or change the infrastructure within it. You cannot cancel the contract. We want it for 5 years. And they want it at more scale.

The current architecture that we're deploying today is Blackwell. Blackwell comes we deployed predominantly in a MBL72 configuration which was an entire architecture change from deployment right if you recall hopper came before Blackwell. Hopper you could deploy these 42U racks which was typically like eight GPUs in a server case.

Blackwell for our deployments um is overwhelmingly liquid cooled in its deployment configuration. And instead of eight GPUs in a 42U configuration, it's in this larger 72 GPU rack.

Vera Rubin uh will be the next architecture that comes out and we've started receiving testing racks uh for Ver Rubin. But that doesn't necessarily mean, going back to the point earlier, that everyone only wants the latest generation of GPU, right? We have massive demand for Ampear, Hopper, Blackwell, etc.

In our last quarterly report our CEO Mike qualified that inference workloads represent well in excess of 50% of infrastructure utilization on our platform. The exact same infrastructure they use for training as well. Those customers are choosing Nvidia to work with on imprints.

Landed usage specifically, I wouldn't say is as much of a concern. Having a powered shell is the bottleneck. PowerShell is effectively an empty data center that is energized, right? It has all the power and associated components. I can come into it and deliver electrons into a rack. Has the cooling system built within it.

That is the bottleneck because of all of the supply chains that that come into that, right? Like not only you have electricity, do you have the land etc. But you have the backup battery supplies, you have the transformers, um you have personnel, right? Like think about the electricians for these sites. I think it's a 5year plus apprenticeship to be able to go through that program.

The way that we finance our business, you kind of break it into two broad buckets, right? You have parent co- financing and asset co financing. And asset co financing is where all of the GPUs get financed, right? It's where all of our client contracts sit and we could take these financings and put them into STDs or we'll just call it a box.

These SPVS uh and these these SPVS they have the infrastructure they have the data center costs and they have the uh the the debt agreements within them. And so you're able to pair this like five-year take or pay contract to an amortization schedule on the debt and you have the revenue come into the box, pay down the amortization schedule, pay down the operating costs of the data center, and it still contributes a it has a 25% contribution margin of profit up to the parent code.

One of the latest ones we did was uh as we call DDTL4 this was a investment grade rated first of its class. No one had done this before for GPU financing, nonreourse HPC infrastructure financing and got done at sofur plus 225 like that that is a phenomenal cost of capital for us and importantly we were able to bring in uh the insurance trunch of capital.

This idea that an H100 deployed in one cloud doesn't have the same performance of an H100 deployed in another cloud and the the metrics that people use are things like good put or model flop utilization MFUs and there are these measurements of like how much more performant is one G the exact same GPU by the way versus another GPU deployed in another facility. And so in order for something to be commoditized, it it has to be fungeible, right?

But the rest of it honestly is just how you operate the the GPUs and that is the coreweave software stack that is how do you keep these GPUs online, right? Like what happens if a GPU fails? Can you predict if a GPU is about to fail and swap in other infrastructure so that the client doesn't have downtime on that component? And there's there's an immense suite of software solutions that and and infrastructure management solutions that we have built to have the best goodput to have the best MFUS in the industry.

Goodput measures the fraction of peak hardware performance that the training job can extract. And MFU's model flops utilization hardware metric for evaluating real world efficiency of LLM's training.

The reality is this stuff isn't getting easier to operate, right? We've moved from these kind of relatively simple 42U aircooled racks of hopper to these immensely complex blackwell deployments moving into Vera Rubin following that like it's not getting easier to to build operate provision deliver these GPUs it's getting more difficult and I I I think until it starts becoming easier you don't really have a path to commoditization.