Core Scientific-Ian Ferreira

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I'm an from Iran on chief product officer at core scientific. standards with scientific. Approaching two years now before that. I. Danced around a couple of other machine learning startups and spent a decade at Microsoft working in the search team. So have been around the algorithms and big data distributed system space, my entire career mostly. But do now like what kind of brought the core scientific do there? So core scientific was interesting for a couple of reasons. One is. I definitely wanted to focus on a I said, that was one of my criterion. The second was it's a very different crowd. So lot of companies. If you go and work in a role, YOU'RE GONNA start from business problems, downwards, and kind of make your way to, let's say, ten flow kind of that. You know to flow by dortch layer up the stack. In what was unique about? Of course, scientific starting from the bottom bottom. So we were studying from concrete. Our Katie P chipsets in understanding into connect said really an opportunity to get the under the hood experience. If you will the hardware experience if you will of Ai and then work your way up at once you've done that you have a full picture of everything from. Okay. This is GonNa, use this library that's going to take advantage of this silicon feature. That's GonNa be accelerated by this hardware infrastructure and Blah Blah Blah, it just gave me a really. Unique opportunity to work from the bottom up. That's really interesting. So you're like because I, of course, scientific is I, guess you boast yourselves as a structure company. Providing, a lot of resources for people to do a myriad of things that I need compute power part of that being mining, various cryptocurrencies, as well as like machine learning and so on and so forth. Today. I. It's interesting that you you take it from. These are the resources that we have. These are the architectures that may be fit to these different types of. Algorithms that are applied across the board Does that. Even that perspective like how do you approach a problem, but what does that? Learned from that. So as you mentioned, core scientific provides a hosting infrastructure is gonNA suffer services for the two primary categories. One is blockchain in the other day I. In the blockchain side were lowered down the stacks in a somewhere between Denison as service and infrastructure is a service where we host. Mining gear for customers in aside, we've much higher up the stack. So where pretty much a a past bathroom as a service. In we'll talk about that some more later, but does it kind of the two differences in we have a, you know some synergies between the two. If you start at the facilities level, not commenting with crypto and an ai gear is high, he taipower Sarah facilities are typically much higher rated than you would find a traditional data center. And the other aspects is. Around controlling heat. In making sure you can deal with these machines at in normal standard racks that you might be used to So that's the infrastructure here and then we did a couple of things around. Using Algorithms around a workload placement, we do that both in ai on blockchain. to, as you can imagine, if you look at on the blockchain side are very common workload that you might want to figure out what's the optimal coin to mind? Right. So how do you do that? You have to figure out a bunch of algorithms, ingredients? To make a decision what to mind, and we can, we can talk a little bit more about how that works in the same thing. On the AI side, you might want to run a large training job. and you might WanNa, know is this better to run on. Azure visit bidders run on our infrastructure core aws because again you have the same equations, there's a cost in the compute. Capability in, you have to figure out what's the optimal for the customers need. So that's kind of the suffer overlap we have between the two verticals.

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