So it starts with that productivity gain at the individual level, but if they're not sharing how they're gaining productivity, no one else can learn from it. So you have a couple people who are getting huge advantages and no one else is. The second set of problems that end up happening is you build a very small product that's not very ambitious and you spend a lot of effort doing it and it's successful, but now you have to spend a lot of effort maintaining a system that often is older when newer AMI models can do much more off the bat and you're not evolving with it. So what the real challenge ends up being is how do we start thinking about the organization differently? So the org chart that we have today was invented in 1855 to manage the railroads. It was designed so that you could pass information in real time up and down a train line and have the right people answer. The time clock and assembly line was invented in the 1910s by Henry Ford, agile development in the early 2000s, and we keep doing these same methods of organizing ourselves because until now the only way you could apply intelligence and management to a project was with a human. So if I added people to a project, that's the only way I could add intelligence or management. But now we have a different way of adding a quantitative intelligence, right? I can actually use AI to add intelligence of all sorts, monitoring of all sorts. They have video and audio capabilities. We have to redesign organizations around this. One of the big issues I see with operational leaders is not getting that AI is aimed at them, that there are key people in this process, right? Either they tend to use an IT problem because it's made of code, but it's true that coders are actually quite bad at using AI because they don't understand the subject area and they want it to work like code, deterministically. So, you know, AI doesn't work deterministically. It works statistically. So, if you're actually good at thinking about, you know, statistical outcomes, you actually do a pretty good job of thinking about this. If you're good at managing people, you're often very good at working with AI. Then on top of all those pieces, right, the other thing that they get wrong about this is not thinking enough about the fact that they have to innovate now, right? So, you actually need to think about how to use these things because the systems aren't perfect. But guess what operations people are used to doing? Dealing with imperfect systems and figuring out how to deal with those flaws. And I think the third big problem, I'd say, is somebody else, believe somebody else has solved this problem. Like, if I just go to the right vendor and they'll have solved the problem for me, right? But instead, it's going to be working with people who deeply understand your process, whether that's internally or with consultants who really know what you're doing, and starting to think about how do I innovate, where is the opportunity here, rather than waiting for someone else to invent it for you. The examples I have are mostly from, you know, consumer-facing organizations, but they're ones that have, you know, the same kind of problem, 10 or 12 different data sources. And what they do with the AI is they actually have the AI agents literally go and do lookups on each of these, like, on its own, following curiosity, to figure out what information it needs. If it finds what it needs, it looks for, you know, it doesn't find what it needs, it looks for something else. Follow leads and actually combine that information together and give you the report you want that's actionable, right? Like, you don't actually want data. What you want are insights. And now you have an insight engine that can generate insights for you. You should be using that a lot. So it's not really much of a problem if people are innovating on their job, right? Because they're already doing that. Like, that's what people do all day is they're like, what if I do the data enter this way? What if I do, like, experimenting on your job is cheap and what people do all the time anyway, right? So that's not really a problem because it shouldn't be distracting them. It should be helping them. And if it's distracting them a bit and there's a little bit of that effort, that's okay too. The question is, how are you harnessing that? So somebody comes with a breakthrough idea. For each one of those, two other people are using AI and haven't quite figured it out and another three aren't using it at all. So the question for you is, what does the person with a breakthrough idea do with that? Who do they talk to about? How do they show that to you? Where's the lab that they're presenting this information to? So the problem I have is not so much that there's not talent there or that you have to balance this out. It's so much as how are you even harnessing the innovations happening. And then at the leadership level, you have to start making choices. Like, you can't just say whatever happens, happens. You have to say, okay, this looks like this has really good effects on procurement, right? We should absolutely be, everything we do, we should do a test on this. We should let this help us with negotiations for each procurement thing. We should let it actually, you know, we should talk to it about actual quality of our products coming through one way or another and have this dialogue back and forth with the AI. And let's invest effort into making that work. So you're going to be looking at the innovation happening throughout the organization. You'll be using AI yourself to understand what's good or bad. And from that, you're going to get an intuition about where you want to really invest. That's going to require a reinvention, right? It's an operational, strategic, and leadership problem, not a technology problem, ultimately. Let's just zoom out a little bit. If you ask the AI labs, right, they think they will achieve within the next five years, actually within the next three, AGI, a machine smarter than a human in every intellectual task, artificial general intelligence. They genuinely believe it, right? They believe it privately. They believe it publicly. You should believe that they believe it. You don't have to believe them, but they believe it, right? A lot of words believe, but you get the idea, right? We don't know whether that's right or not, but it is absolutely what they're predicting. So I think it's worth, first of all, thinking in scenarios. Assume, you know, that we have machines that are very smart in five years. But I think what the labs underestimate is how real work gets done. There are real organizations. There are interdependencies. There are traditions. There's unwritten context. And, you know, there's humans, right? And organizations. And they're messy. And process is messy. So I think that what you're going to see is even as AI gets better, there'll be a set of firms that try and embrace it and change everything that happens. And there's going to be a set of firms that sit back and say, this is too complicated. Someone will solve this for us. Then we'll figure it out. And I think what's going to happen over the next five years is the companies that figure out how to become AI first and AI forward and build the capabilities internally to do this are going to start to pull away from the others. You suddenly have, like, what, you know, operational organization doesn't need more management, doesn't need more brains, doesn't need more code, doesn't need more analysis. And the companies that figure out how to apply it are going to get the advantage. And you're going to start to see a gap widen and that may not come.