I think we covered some of it in terms of actually you work out that going after hard problems and deep problems is no more difficult in some ways than going after a shallower, simpler, more superficial problem. They're just differently difficult. There's different things that are hard about each of those things. But I think given life's very short and you only have so much time and energy, you might as well put your life force into something that will really make a difference if you hadn't done it. If you hadn't been there to push it. So I would just think of it through that lens. And then the other thing is if you're if you are and we talked about deep tech and I love interdisciplinary work. And I think that's going to be even more prevalent in the next few years in combinations of fields and finding the connections between those fields. And it's going to be even easier to do that with AI. And then the only other thing I would say is if, you know, if you have your depending on what your AGI timeline is, you know, mine's like 2030 or something like this. Then if you start off on a deep tech journey today, usually that you're talking about a 10 year journey for true deep tech, in my opinion. So then now you have to just consider AGI appearing in the middle of that journey. So what does that mean? It doesn't it's not bad necessarily, but you have to take that into account. Right. To will it be able to leverage it? What will the AGI system do with it? And it goes a little bit back to what you said earlier about alpha fold and general AI systems. So one thing I can think see happening is Gemini, Claude or one of these general systems making use of alpha fold like specialized systems as tools. I don't think we're going to have it just in one giant brain because it will have too much regression in. If I put all the proteins into, you know, Gemini, that wouldn't make sense. We don't need Gemini to do protein folding. Going back to your information efficiency, it would definitely affect its language skills or something like that. Right. In a bad way. So much better, I think, is to have really good general purpose tool usage models that will then maybe they could even train those specific tools, but they would be in a separate system. So I think that's kind of interesting to think through the implications of that and then what you might build today. Also physical things, too, like what kinds of factories would you build, what sorts of, you know, finance systems and so on. So I just think you need to really take that seriously. And on the one hand is like and imagine what that world would look like and then build something that would be useful if that comes in halfway through. So I think that's kind of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of a little