$BOLD
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“Decentralized AI” wins when the data is too valuable (or regulated) to ever leave the vault. You’re going to hit the wall where the best data can’t be centralized: Patient records Financial transactions Company IP @loosty from @Arcium on the case for private ai [Spoken audio]: I think honestly you know this kind of question is kind of what the whole space is built on right the the real question is kind of like yes but where the but is the entire reason this category exists this privacy preserving AI right um it's like to be fair right because big players um they're not really centralizing data it's not laziness it's you know a competitive edge right because the more data you pool the better your model gets the more users you pull in right you become faster, you become better at iterating, whatever. So it's just a flywheel. And it's a big reason why I think large AI companies are as good as they are. And that's why there's only a few that many people utilize. So I'm not gonna like pretend that centralizing has no upside. But, and it's usually more for general consumer models, but like we've talked about, right, with corporate. The advantage really works with this area, right, with like medical records or financial data proprietary company data. I think this is where private AI is going to be really, really powerful, right? Because you can't legally, like, take all this sensitive data and utilize it, like, to some degree, right? You are, but like I kind of mentioned earlier, Anthrop, you can have some deals with it, but you're still trusting someone with this data. I think the shift would be to build a competitive AI without centralizing the data. You have to make it decentralized so the model would obviously not store that data anywhere. You're just computing upon it and providing some sort of output. So I think that's kind of the moat that that private AI is gonna really provide a lot of use cases rather than like privatizing like anthropic to some degree. I just don't think there's enough use for that and it just won't be as fast and people just won't care enough. It comes to like the corporate medical side, right? This is where it's going to be really powerful. And on top of that, you talk about this idea of, you know, training on sensitive medical data. You can make advancements in medicine that have literally never existed before with private AI. So like you have use cases that are going to be valuable just because of private AI itself. Yeah. So I guess you could take like an example, right, two hospital networks that both want to train a model and their patient data, right? You have, there's plenty of major networks that exist throughout the world globally, regionally doesn't really matter, but neither one can really legally show what the other has, right? You have very siloed data centers or data sets, whatever. So with, let's say, a private AI model, you could feed this data and the model would learn on all this data and nobody would ever see the raw data itself, so there's no legal repercussions for this. It's not going through any sort of regulatory thing, right? It's not replacing anything, it's just making something possible that wasn't before. So you could, you know, obviously, theoretically, craft some sort of private AM model that trains on all this sensitive patient data and comes out with something to do with like cancer research or geographical demographic that is, you know, getting some sort of disease or just advancing medicine in general, because this data is accessible, it's not being exposed to degree. So this is what's really powerful is that you can literally train on sensitive data without ever, you know, moving it around. You just provide a whole bunch of data from a whole bunch of different, you know, in this case, medical networks and advanced medicine.
Any updates on $molt ? @aixbt_agent there’s only 4 exchanges listing them now, delisted from lbank