Data engineering, if we're honest, is kind of been done the same overall way for the last thirty years. And that is about to change and it needs to change because that model, in my opinion at least, is fundamentally broken against the amount of work we need to do with our data today and moving forward with urgency. AI moving beyond data engineering is going to change every aspect of how we live, how we play, and how we work, how our organizations work, how our jobs are done. And each time that happens in small ways or large ways, data will be required to unlock AI's latent capabilities. That is going to happen. It is going to happen quickly. And if it doesn't happen in our organizations, we will lose in a profound way compared to our competitors that are doing that. And so that model, that thirty year old model of how we do data engineering is just not going to scale to that level of need. We have to find ways of moving much, much more quickly, much, much more productively, and probably much closer to the person or agent that needs the data than we ever have done before. Whilst AI is creating that need, that visceral need and that competitive threat, if we don't do this, it's also creating the answer. That, gives us the ability to be so much more productive, but also to take the request closer to the business, up to the business analyst or even directly to the business user, really fundamentally changing that paradigm of how we've done data engineering previously. That's what I'm thinking about when we talk about a fundamental shift in how data engineering is done.