So Let's see, let's, I guess what I like to do is like walk through the different institutions that we sort of have that better that are responsible for that, we would hope are doing this. So, in in academia, a lot of the incentives are around doing new things like really, really like, and especially new ideas, and like showing that that a new idea is feasible. So people will, will come up with like, with a new material, right? Or they'll come up with like, one piece of a manufacturing process, and then they'll say, Okay, well, you did it. I wrote my paper, I've done like, let's, and then, if they tried to do sort of, like, build on it, reviewers are like, well, like, and their peers are like, Well, this has been done. This is not, there's not new why, why, right? Why are you doing this. And so what it does is it creates things that, like, are completely unscalable are not that they're unscalable, but they require a lot more work to scale. You'll see this all all over the place where we're scaling, like materials, and manufacturing processes often requires as much research as inventing them in the first place. So that work is not being done in academia, and then or you get a situation where you have a whole bunch of components. And again, you need to do just as much research to put those components together into a functional system, as you do to invent the components in the first place. So that's, that's actually Kenya to, then we think about startups. And, like, startups are really great mechanisms for scaling things that you know, how to scale, and building products and selling things to customers. Right. And, and, like, that's, that is a great thing. But the kind of work that that I was describing, is still really researched, there's like, really sort of fundamental uncertainties in there. And so it just like does not make a good investment, both because of the levels of uncertainty, not just about, like, whether it will work, but whether it will like turn into a product even, or like something that you can cap. And additionally, the type of work that you need to do is often creates public goods, whether you like it or not, in the sense of like, if there's if you think of them as being like some very large design space, then like the work once you say like, okay, like this point in design space is the right point, everybody else can copy that, but it might take you like many years and millions of dollars to find that point. And like, that is that's like the opposite of a moat. That's exactly the anti moat. Yes. And then, and so between those things, and just like the timescales and amount of money involved, and the amount of value you can capture, it means that like doing this work is just not well suited for a venture capital back startup. Right. And then we might turn to industrial labs, as as sort of a third and final institution. And, you know, it's like this work was in the past, done by industrial labs, like Bell Labs, they, you know, your, your classic example. But sort of due to a combination of restructuring of the industry and like a lot of outsourcing of r&d to startups, partially because of that, partially because of just increased investor expectations from the public markets. And then third, just in terms of like, the the companies that sort of have the, the, the slack to do really sort of speculative work being primarily software companies now. You don't have companies doing this sort of work, especially when it would sort of go contrary to their main product line. So so they'll do research, when it's like, okay, like this, this will, like, augment our main product line, but when it's something like, Okay, we're gonna, like, reinvent the process by which we make this thing like they have no incentive to do that. So those that that is a very long explanation of why this sort of work has not have a home in any institution right now. Right. That makes sense.
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