So, okay, let's talk about what we're doing. You know, like Alex just said, going into what makes these AI models great, right? There's basically a few key ingredients. There's getting the research and the architecture good. There's having good infrastructure which is both the quantity of compute but like as important if not more is is also just like how efficiently can you use it? How reliable is it? What like what is the the quality of that? And then the third piece which is in in some ways it's hard to say that any of these are more important than the other because they're all necessary is effectively the data and what knowledge it learns.

So we're in a phase where basically the AI models learn from having really from watching really smart people do things. And if you're trying to get it to be able to be able to do certain capabilities, having it be able to observe really smart people doing those things is is very important. So there are a few examples of where we're trying to do this across the company because one um one basic insight and hypothesis that we have is that a lot of data generation across the field is done by these like contract companies. And Alex knows a bunch about this because he ran one before coming here, but in general, the average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks if you're working through the contract through these contractors.

So if we're trying to teach the models coding for example, then having people internally build tools that or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do who don't have thousands and thousands of extremely strong engineers at their company. So that's one example.

Another thing that our system needs to be very good at is using computers. So the way that you get a system to be good at using computers is by having it watch really smart people use computers. So that's basically the essence of what we are trying to do here. We are rolling it out in a way that is like you know it's like we're basically we're not like actually no human or anything is looking at or watching what your what people are doing on their computers. Just the content is sort of stripped out in like as as as much as is possible. It's like none of the data is being used for like looking at what people are doing or surveillance or performance tracking or anything like that. It's purely just like we are using this to feed a very large amount of content into the AI model so that way it can learn how smart people use computers to accomplish tasks. I think that this is going to be a very big advantage if we can do it. So anyway, that's what we're trying to do.

I think that there are going to be probably other things around the company where we basically try to enlist the fact that we have just like a very high quality set of people to teach the AI systems to do different things that we need to get them to be able to do over time. So this probably isn't the last thing like this. And I think it's this is like an interesting strategy. I think that we want to see how well it does. At this point it's somewhat of a hypothesis. Will actually be able to complete the loop to see how well these kinds of things actually improve it. If they don't, we won't do more things like it. If they do, then we'll probably do more things like it. So that's the basic thing.

In terms of how we communicate about this, I mean, this stuff is tricky. I think when I was looking through the the details of this, there's like all these things that we could have done better. So, that yes, I mean yes, acknowledged and we will try to improve this. The kind of core tension on this is that we want to communicate as clearly as possible about what we're doing while not having all of the details of things that we think are going to be strategically differentiating leak immediately to the competitors. And so I think part of the challenge is like we are a pretty big company. If we post stuff publicly, it leaks.

Some things matter more if they leak than others, right? Like if we're building something in our, you know, ad system for example or our infrastructure that's like bespoke to us and it's not something that other people are going to copy and it leaks, it's like not that big of a deal, right? Maybe it's kind of annoying. I think we know that AI is like one of the most competitive fields, right, in like probably in history. So anything that can give us that can make our the quality of our thing better is generally not something that I think it is in our strategic interest as a company to lay out the details in a lot of detail knowing the physics of the situation is that that stuff is going to leak. So I think you will have to we're just going to have to navigate that and it's going to be a little bit different on a thing by thing basis in terms of how we communicate.

But I actually think it is like not strategically in your interest for us to communicate everything like in all the detail that we normally would on this. But I think we do need to try to make sure that we get this right. And communicate enough so that people understand what's going on. So this I think will be a continued thing that we're trying to navigate. It's part of the complexity of trying how do we navigate running the company through what is just this incredibly dynamic period. I think that there's like lots of things that I think people would like more certainty on than we have. Lots of things that people would like more details on that it's not necessarily like it's not that any it's bad for any one person to know, but it is bad if it leaks. And I don't know how we how we how we exactly navigate that. So that's the basic situation on that.