 When we look at AWS, when we look at, of course, we can talk about whole OpenAI, Microsoft, Google. What difference do you see in AWS J and AI approach versus others? Yeah, it's interesting. You know, as I mentioned, all the hyperscalers started to look alike. They have a very similar framework blueprint approach strategy, whatever you call. They look very identical. But what stands out with AWS is, let me pull up my notes. So I think Amazon's USB is purpose-built hardware. They are actually delivering innovation at the hardware level, from AWS Nitro to Graviton to Tranium and Inferentia. That is a very, very important differentiator. And what it actually means is, when foundation models become mainstream, when customers tend to pre-train models, fine-tune models, the price-performance ratio that Amazon would be able to deliver with the homegrown hardware is going to be significant. It is going to become way cheaper for customers to fine-tune models and to run J and AI workloads. So Amazon's secret sauce will move from software to hardware. That is the biggest differentiating factor. So when customers look at a comparative analysis on how much does it cost to fine-tune a model, to pre-train a model, or to run a model on AWS versus Microsoft versus Google, Amazon is going to look far more cheaper. I think that is going to be one of the differentiating factor, and that is where the hardware innovation comes up. The second one, which is very unlike Amazon, is the choice of foundation models. So Amazon always puts the first-party product or a service as the primary service. Though they adopt a lot of open source, they brand it as a managed service, and it becomes a first-party service. But for the first time in the history of AWS, I have seen partners getting a lot of attention, and Amazon giving enough importance to model providers, whether it is anthropic, in which, of course, Amazon has invested, and AI 21, stable diffusion, cohere, they basically got all these models, and they also got Lama from Meta, and they are partnering with Meta. So the differentiating factor there is the best of the breed models coming from first-party, which is Amazon investing in Titan. Second is the most prominent commercial models in the form of anthropic plot to Jurassic from AI 21, or cohere's command. That is the proprietary choice of foundation models that are available. Third is the collection of open models, whether it is Falcon, or Lama, or Mistral eventually. So all of those will become available, and this wide range of foundation models and the choice will actually become a key differentiating factor. Third one, which is, again, very unique to AWS, and they're going to really capitalize on this, is the data gravity. So if you look at S3, Redshift, RDS, Aurora, and a variety of data services that Amazon has, a significant part of internet data lives on AWS Cloud. Now they are going to enable GenAI with all those investments and enabling customers to basically bring the data living in all these sources into their applications and their GenAI applications. And honestly speaking, Amazon hasn't done as much as it needs to do with that in the sense connecting the dots across various data services and building pipelines to feed the data into GenAI foundation models. But eventually, that is going to become the biggest differentiating factor because just like they say a significant part of internet runs on AWS, a significant part of data lives on AWS and enabling customers to consume that data in a very effective mechanism to build foundation models or train foundation models and build GenAI applications or key differentiating factor to summarize hardware innovation, choice of models and data gravity. I think those are the key differentiating factors for AWS.