Look in a two to four year time frame and thank you for, you know, 10 year time frames are hard to predict. But in a two to four year time frame, maybe maybe I'll highlight a key area which US and a few other companies are working on which I think will really make progress. We call this agentic workflows. So anywhere you can describe a task in natural language and the AI can act on your behalf and accomplish that. And so today, to give you an example, there are two examples within Google. We are, we are working, we have a project Mariner. It's a browser extension. So the browser can actually go browse and do things for you, complete and come back. If you look at where the state-of-the-art was maybe a year ago, it was kind of roughly 50% effective. So you give it a complex sequence of tasks it needs to get everything done accurately. Today it's progressed around 85%, right. And so, but that shows the progress. Similarly for coding, you know, as as a software engineer at Google today when you're programming, the AI is there on the side. It gives you suggestions and you accept it. But we are now working on where you can give it instructions. It'll automatically go and write the program and, and, and get it done. And so, and now imagine the impact of this agentic use cases working horizontally across many, many tasks. I think that's going to be very, very powerful. So the real promise of having an AI assistant in the context of anything you're doing, be it by learning something, radiologist looking at images, a doctor engaging with the patient, all of that is going to be assisted and it's going to deeply be embedded in your workflows. That is as real as it gets in a two to four year time frame. And and you can imagine the impact from a productivity standpoint. And I think that's one of the most tangible opportunities. I think it's very, very real that will happen. If you like the video, do like, comment, share and subscribe.