Fog Hashing- Mining Today, Computing Tomorrow

Hello everybody. Uh Nick Burley, VP North America for Fog Hashing. Uh I just want to first off uh if everybody can give a round of applause for this great

team up here. You know what? It's not 0% uh work uh pulling something like this off in two in one year. Uh that's a pretty impressive feat. So uh again,

Nick Burley uh VP of North America for fog hashing. What we want to talk about

fog hashing. What we want to talk about today is our offering. We're looking at uh some very interesting dynamics in the market right now. We have a situation

where let's be frank AI is coming in and eating capacity in the Bitcoin market for energy sites. Many of these circumstances the value of these sites

are directly proportionate to in situ resources that we have the capability to deploy right away. That time delta on the capability to drop compute as quick

as possible is a very uh very large concern in the industry right now. and

we want to show you some of the offerings that we have that are going to take advantage of this.

So in this I'm not going to go through all these bullet points. You guys are all smart. It's a circumstance where

all smart. It's a circumstance where we're looking at the the precipice of these two industries converging. And a

lot of the the circumstances people like oh you don't take equipment such as uh a GPU cluster and throw it into infrastructure. Well, you know what? I

infrastructure. Well, you know what? I

we've we've run the models, we build uh data centers, and there's a lot more compatibility than one speaks. Yes,

there's a lot of nuances for different parameters, but the question is, do you have an opportunity for three, four, five times revenue on your site? What is

the cost basis to action that uh opportunity? And having something in

opportunity? And having something in situ at time that can take advantage of those circumstances is paramount on time is money. Nobody wants to stop. Other

is money. Nobody wants to stop. Other

regions in the world are scale pill and this is the situation where on the current trajectory to keep the performance of models to keep that on a

linear trajectory the compute is going to have to expand exponentially. So we

all know that that doubling of the penny paradigm that a lot of people speak about is on day 30 it's x million. Well,

this is the circumstance right now that we're seeing in this exponential growth of compute.

So, one of the big things here is the the differences between the two. The

similarities are between the two are relatively close in the the aspects of the infrastructure layer.

the differences in the technologies are is a circumstance where the Bitcoin layer we all we all know there's a a

circumstance where the Bitcoin layer is much easier to deploy we get units on site we throw power into them we get them up and running and it's you have a

customer ready to go as soon as you plug in these circumstances aren't there with AI if you have a a scenario where you're negotiating creating an AI contract with

a group, it it can take months if if you're deploying our our industry runs on a capital parameter of get the site,

get it online and understand what your run costs are going to be over time. If

you're if you are building and allocating millions and millions of dollars towards infrastructure and it's a circumstance where that client side is

not knit tight, you can get a circumstance where you have a massive bleed. The last thing you want to do is

bleed. The last thing you want to do is deploy equipment on a site and have the customer's parameters not be clearly defined or negotiate for 18 months on a

contract. These things don't work. As we

contract. These things don't work. As we

go forward in the industry, one of the biggest things is we're going to have a lot of parallels, not so much to the technology stack as much as the market

stack. In Bitcoin, we had an very

stack. In Bitcoin, we had an very interesting circumstance happen where we had legacy um equipment go to different lowerc cost jurisdictions. That

trajectory is going to happen very similarly with the AI space where you have certain uh hardware applications that are depreciated assets that now

have an operational base to serve a function in the market but they are not going to be frontier training there.

It's not going to be a circumstance where those units are going to be sitting in that data center. We have

gone from a circumstance where we've you've had massive companies build 10-year engineering projects to meta building tents. Like if people don't

building tents. Like if people don't understand the implications on the supply chain and control side of going

from 10ear in engineering projects to tense being built to house billions of dollars of AS6 that shows you or sorry

excuse me uh G uh AI GPU compute the if people don't understand that we're running into the absolute pinch points of the supply chain right now. There's

not enough trace people. There's not

enough uh talent. If if you've been out to Avalene and had a a gander, that's a mega project. You're looking at 4,000

mega project. You're looking at 4,000 people on this site. Everybody's

crawling on over everybody. And each one of those groups have a ganter. And we

all know it all takes is one miss to ripple through a GAN. So on that capability having the capability to deploy equipment and get it up on speed when you have an opportunity and have

infrastructure face where the majority can be reutilized for higher revenue source is if people don't have that the opportunity cost loss on that is massive.

So I'm not going to bore you with all the the parallels on the economic side.

We all know the the circumstance for the um the difference between the mismatch on the both both um we know as we go

forward in this industry the the energy side has uh issue with matching uh the demand side right now you guys that are out there talking about daisy chaining

Mac trucks together for energy. This is

where we are. There's a circumstance where other jurisdictions are putting on as much load onto their grid in a month as North America does in a year. Other

regions are scale pill. How do we take advantage of what we have and maximize revenue potential? Now that is the

revenue potential? Now that is the biggest question.

So the what approach will be used for mining now and computing in the future?

One of the big things is we all know the parameters of electrons. Electrons are

going to uh are going to travel copper on aluminum regardless of the circumstance. the uh on the aspect for

circumstance. the uh on the aspect for cooling side, you have to make sure your integration layer is going to work through that regime of parameters that

are going to deal with 115° temperatures and they're going to deal with the other side of the equation as well.

So what we have is a capability to facilitate demand of AI and hydro um bitcoin mining. So with the as this

bitcoin mining. So with the as this situation where you have legacy machines coming offline and okay maybe it's not the cutting edge equipment those that uh

those GPUs are not going to be worth zero so building uh a revenue stream on the basis of easily deployable compute they will not on an inference load will

not have the same parameters as a training load. So, how do you maximize

training load. So, how do you maximize your capability to get a higher revenue stream into your site quickly? And

that's what we we focus on here.

So, with our capability here, we have chiller units with dry coolers. Uh, so

what that allows you to do if you have a a client right now for Bitcoin, you bring the the unit on site, you get it online, all of a sudden you get an opportunity. You're you're now already

opportunity. You're you're now already cash flowing on your Bitcoin. side.

You've got your site up online. The big

thing is, okay, now you're negotiating with a group on capacity. You now have something that's already up and running.

You then can add modules to the uh the equipment layer like a chiller to make sure you hit the parameters needed for your um for your AI side. The

temperatures are different. It's not

easy, but ultimately it's having a tech stack that has more of a jack knife capability where you have the capability to bring in units that are a little bit

more than just, hey, we have a transformer on site. Hopefully that

works. Having something like this gives you that flexibility where you can try to pull down some of that load that is uh is going to be going to some of these

other higher cost jurisdictions.

So the uh the big thing is is come talk with us. We have clients looking for uh

with us. We have clients looking for uh GPU uh space. We have clients that are looking for mining uh uh hosting

opportunities to hosted site. We are you can't miss us. We're the big uh we're the big uh container over there. Come

chat with us. We have multiple opportunities to help facilitate getting miners into your location, putting uh you in contact with uh GPU clients and

building out the infrastructure cuz guys, it's it's going to get crazy.

Exponentials are exponentials. And when

you see this line on a graph, there will be a pinch for power. And the last thing that society is going to deal with is saying, is it worth running my cure egg in the morning or is that the human

capacity of 8 hours of intelligence?

because that's what it's coming down to.

So the question is how are you ready to make sure that your infrastructure is going to cap that uh capture that revenue stream in the smallest amount of

time? Please uh come have a chat with me

time? Please uh come have a chat with me over there and let's talk more. Thanks.