 I think that we're seeing a lot more mainstream business finding reasons to adopt AI for regular product lines. So I think that while there's been a lot of focus on the big companies who are the first movers on this and while they will maintain a lot of research momentum obviously, I think we'll see AI moving out more into companies where it would not have been expected. We're seeing now of course a lot in healthcare but also in transportation and probably a way that this is happening is that a lot of what we look at in terms of machine learning is moving out into hardware and especially on the low-power hardware. So for instance if you're a company that has trucks that go out on service calls we're seeing more and more embedded devices going along with those trucks. They collect data, they can provide a lot of information out in the field and having machine learning in those kind of low-power devices means that we can handle a lot higher data rates out on the edge, out in the real world, as opposed to say having to bring all that data back up into the cloud, process it and send it back out. So when you take into account how much mainstream business there is like that, you know, whether it's farmers or electricians or somebody repairing, I don't know, a building, I think the numbers are much much larger than what we're seeing currently with the early applications. I mean obviously finance is incredibly sophisticated in this area because they've been working as data companies for decades and so they were really aligned to be able to leverage a lot of machine learning early and it fits very well with a lot of the needs and finance. But as it moves out into like I say transportation, energy, manufacturing, construction, a lot of areas that aren't quite as intuitive about the use cases I think that'll be the big change. Interesting, so yeah I definitely lived through the AI winter, I had done work in grad school and then that happened, I had to go into other kinds of work but that was decades ago. I think that the big problem that's moving right now is that a lot of companies experience tech debt in terms of how they manage data or collect data and if they don't get rid of that they're not in position to be able to adopt a lot of the machine learning kinds of work that their competitors are adopting. So what I see as kind of the next AI winter is not so much AI goes away but actually a large part of enterprises at risk probably becoming acquisitions and we're already seeing this now with a lot of notable acquisitions happening with some companies getting into a trillion dollar scale for a market cap. I think we'll see a lot more of that and again it really comes down to the kind of preparedness that they have. I love it. I really love big data Spain. This is I believe my sixth year coming back since 2013. I love Madrid. It's just such an amazing city. I feel so comfortable as soon as I get there. Big data Spain to me gives me like a bird's-eye view of what's happening and not just in Europe but across the world. It's amazing people have flown in from all over. The kinds of use cases that we're seeing here it's very diverse and it has a nice balance between the technology side and the business side which is very hard to find. So I love coming to Spain. I love the people, the food, everything of course but big data Spain itself is a very special kind of event and it's wonderful how it's grown over the years. I look forward to seeing much more.