Compute Labs creates AI-Fi ecosystem for decentralized computing

0:01I'm ashon Addison from the cryptocoin show and today on blockchain interviews we have Nick co-founder of compute Labs Nick welcome to the show and thank you for taking the time asan thanks for having me glad to be here there's so much to talk about in terms of AI the computational power needed decentralized compute networks and I think people are underestimating

0:23just how much computational power is going to be used in the next couple of years I'm excited to dive into all of that today with you I would love to start out with a little bit on what you and your team been building at compute labs and then we can dive into all those details yeah that sounds like a fun topic I'm happy to be a part of it awesome all right let's kick it off cool

0:45uh should we start with what is compute and what's happening with the industry and the growth of it I think so that'd be great so you definitely hit the nail on it uh the needs for compute is growing exponentially what we know is that there's a aggregated growth rate for compute needs uh based on several different sources out there about 30% a year and what that means is that in

1:14general by 2030 and only 5 years um the demand for compute Hardware is going to be just exponentially growing at that same rate so about like 420 billion uh by then um and what we know is to your point when we think that okay there's probably like a limited amount of use cases there's not going to be like that much demand like we think we probably

1:41like hit the curve there's that much compute needed no it only grows more and more because Innovation and Engineering requires just more and more Hardware I mean we have xai and their coin gigafactories or um infrastructure data centers and then we have AWS and core weave scaling their GPU data centers there's just more and more Hardware being used and we're

2:10seeing this every day so for example like we're getting demand from um gigantic companies asking us to help them get like hundreds of millions dollars worth of Hardware so yeah the point is is that this industry is growing uh we don't talk about as much as we did maybe six months ago or a year ago but it's still growing definitely I it seems like every new

2:33version of llm that comes out is requiring 10 times more power and and computation and I I think a lot of people don't really know you know where does that compute power come from does it just come from an Amazon warehouse is it actually coming from decentralized networks um maybe you can elaborate a little bit more on Gathering that compute power and then getting it to the

2:55right place yeah that's a really good point where are people getting their compute Hardware from so you're right that there's of course AWS and then there are the deep in projects so um you can think of it of course as like one is centralized one is decentralized um and and un centralized aspect of it we have the big players we have AWS we have gcp or Google cloud services um we also have

3:23core weave um and then we have smaller players like um I don't want to call them small but you know in comparison WS I'm sure we have Lambda I guess um there are a ton of cloud marketplaces out there and the way they work is most of the time they have their own Hardware that they own and they provide it to customers that need cupu needs and then there are going to be others who

3:51aggregate kind of like runp pod who um they just getting it from different sources similar to a dein project except it's Web Two based um and then we have the very web 3 deepen projects which are simply aggregating from as many variable sources as they can sometimes even from uh consumer grade like retail people just like you and I um the difference is

4:16going to be when you have a use case that requires compute which of these providers has more of the specific requirements you have in terms of Hardware so for example um if I was going to be building out like a really heavy training model um that needs super efficient computational needs I can go to gcp or maybe AWS but even sometimes they don't necessarily have all the

4:47hardware that I need I can go to a um deepend project that maybe is pulling from all of these variable sources but again because they're pulling from different sources altogether it might not even mean uh it'll be enough for me to sustain the speeds and efficiency I need so a lot of times actually what happens is these customers who are building these heavy

5:12AI products end up getting custom built server structure so they'll go to an operator who'll say he like hey I have a very specific set of requirements can you get it for me and then the operator go and does their thing it's just custombuilt servers uh based on very unique cases it's very interesting and do you think that there is a growing role in in

5:38the dpin networks and with decentralized physical infrastructure networks they're not always to do with just Gathering uh resources for for AI you know there there's a lot of different use cases but with with Gathering compute power these deepin networks it seems like 2025 could be a year for major growth in physical infrastructure networks as compute the

6:02compute power problem seems more prominent more people are aware that AI specifically requires a lot more compute and hey maybe there's potential to tap into phones or home computers or there's a revenue generating opportunity for people do you think that's something that will like increase dramatically in 2025 it's a good question so I think of these different types of compute vendors

6:32or compute providers um serving a different customer profile almost when I think of deepin projects I think they're really excellent at serving small businesses small to mediumsized businesses when I think about those other centralized players that we've been talking about I think about them serving U medium to large size businesses um and the difference is

6:56simply because of where they're pulling their resources from oftentimes on the deepins um depends on what type of deepin project we're talking about so we have Aether hyperbolic and IET these are really awesome deep in projects and like all good friends um and they're good for solving the specific use cases that they're built for athers are really good

7:22at solving video game needs hyperbolic is really good at serving um Ai inferencing and training needs um IET is a good combination of both um then we talk about you what kind of customer profile can they help with from small to large um when it comes to those uh aggregated resources from like you know my phone this laptop or or your devices those have their needs um me

7:55personally um I don't know what problems they solve but what I would gather is that they would solve economies a scale and they would probably make uh certain resources uh become way cheaper I mean you can get like 409s uh on an hourly 40 like consumer grade RTX 490s pretty cheap pretty affordably on an hourly basis if that's too expensive for certain businesses or certain you know

8:22people practicing and trying their uh AI use cases yes these other devices can help um but I guess my question is like how much cheaper do you need to get it to maybe it's not built for first world economies or or like you know the United States or Canada maybe those would be more effective for um Latin American demographic or or um the subsaharan African market so some different markets

8:50but I don't know if they'd be really effective for um like the markets that we're in right now yeah that's a great point and I feel like the ethos in part has always been to to help the unbanked or to help the the ones that couldn't normally access these kinds of services whether it's just getting a bank account or if it's accessing AWS and they charge

9:11a lot uh you know for anyone who's run a business they know um so I I definitely could see uh hopefully the the rest of the world and the developing countries can tap into and be the one to grow these deepin networks um so that they can and hopefully there's uh a revenue generation for the people in those countries that are contributing that would be great yeah I

9:33mean any more business for growing economies is always great and you know I'm specifically have always been interested in Defi and decentralized finance and now ai seems to be merging ever more so this year with blockchain and defi specifically and I've seen this new term that's coined aifi or and also defi which an AI at the end um do are is compute Labs

10:04involved with aii working with that at all and and what can you tell me about that yeah we started uh using the term ai5 back in March um I I won't lie a little bit of a little bit of it is just marketing um but effectively what we're trying to do is build the infrastructure um allowing investors to get into the growth of AI and AI Investments uh and

10:31that's why we coined it AI fi uh because our product is built around making sure that people can get into the investment growth of AI interesting and is that just for investing in AI related projects or does the AI actually help you choose manage any of those kind of functionalities of investing it's a good question so why aii wider usage of that in in the name um really it it it it's

11:00part marketing and then the other part is simply like making sure that people know that by investing in compute resources they are enabling the growth of um the AI industry so it's it's kind of like a commodity approach like invest into the core asset that builds powers and enables AI very interesting and with Gathering the compute power for AI how is compute

11:28labs functioning efficiently in in doing this can you talk a little bit more about the business relationship coming in and going out yeah certainly so this is a for me this is the fun part uh basically what we do is we raise Capital we raise Capital through institutional U traditional Finance but also through retail investment uh from markets that we can take capital from what we do with

11:55that is we then buy compute Hardware so mostly Enterprise grade Hardware like the b200 and h20s however we also do double in the consumer grade products like 490s and 59s now what we do when we have that Hardware we actually lease it out to um operators you can call them data centers or operators or data center operators you can even call them like cloud

12:22service providers um the point is we lease our Hardware to them so that they can continue to scale and grow their businesses most of the time they already have a customer lined up or demand lined up to use their Hardware which is why they need more Hardware to serve their customers so that's where we help them that's really interesting and I I keep hearing that you know there there's

12:48so much demand that's growing for AI that as you said they C they already have a customer that's demanding it they need more is there a shortage of of Supply or is there inefficiencies in supply chain in in getting uh compute Hardware to actually start running and and bringing that power for computing AI it's a really good question um there's the short answer is that there

13:15are inefficiencies in uh Le time so often times the provider that can get it most efficiently or fastest is going to be the winner to actually get the contract with the customer and the point is that because you know first you make an order to buy hardware for that custom built cluster it's going to take like 6 weeks to get it then you need like four weeks to actually run it test

13:40it make it operational that's a lot of time already for a AI company that needs their thing done tomorrow I mean a lot of Enterprise businesses um they think they need something tomorrow and um now often times there are going to be projects that know that they're planning for which is great but there's also the opposite another area of inefficiency I

14:04would say though is actually in the energy a lot of data centers simply do not have the energy or power to actually continue scaling or growing um sometimes it's legislative so for example governments might actually forbid or prevent a data center from um you know using more resources than they can and other times data centers just simply are there aren't enough of them in

14:30demographics to actually put Hardware in a specific area H very interesting and with an example some of the clients that you're working with are are those data centers all in America and and what are the potentials of tapping into Alternative forms of energy or maybe mining Bitcoin on the side or renewable energies so that they can be able to run bigger

14:56operations yeah it's a really good question so comp labs in general we work globally we work with as many data center operators as we can whoever needs compete resources whether it's the capital to buy it or the actual Hardware will serve them as long as you know they're not from a saction country um that that that's one and and then on to answer I think your later question which

15:19is like using the um uh run time that that isn't being utilized or the underutilized U processing power yeah so there's a lot of like um algorithms and products out there that can help you get the most or maximize your compute uh availability and so basically if your customer is locking in 80 or 90% And reserving most of your Computing uh

15:46availability then you can have like 10 to 20% that will either be used for mining but also that's kind of why there's these deepen projects out there you can also offset that allocation to those providers like Aether IET hyperbolic so some providers do that as well very interesting and we've had discussions with ather and IET on the show before um but I so AI compute seems

16:12to move so quickly that it's hard to keep up if we already trying to keep up on top of blockchain as well um and with compute Labs do you guys tie into the blockchain equos system or have a token and and that merging of AI and blockchain yeah it's a good good question so uh the short answer is yes effectively what we're doing is we are tokenizing our Hardware so when we back

16:39up and I talk about how we're raising Capital we're raising Capital through um those institutional investors and those retail investors but the way we're doing it is that we are issuing a structured product we're issuing an investable asset to these investors um and what happens is we're giving the rights to the Hardwares Revenue the profits and all the liquidation proceeds to those

17:06investors and that's what a token means so when you're an investor and you invest into one of Compu lab structure products you get a onetoone representation of your Hardware so if you're investing in h200 okay you'll get a GPU nft of a h200 product what that means is that now that you own this one um token of h00 you'll get all its Revenue um you'll get all

17:33its proceeds when we sell it or liquidate it you'll get all of those proceeds as well um and that's what blockchain is doing here tokenizing the actual Hardware that's really interesting sorry yeah yeah no with the yeah it's like real world asset tokenization of if if you're looking at buying and selling assets that are in a warehouse you know you're not really

17:56you're not moving them if somebody else wants to own it it's still going to be computer the same thing in that warehouse then nfts and real world asset tokenization is a great use case for this it's very interesting and what what are some of the next steps for for growth for compute labs and keeping up with the growth of AI in the industry for 2025 some of those road map plans

18:22yeah great question so uh our priority right now is helping operators get more res sources whether it's the capital to buy Hardware or just give them the hardware they need to serve their customers so um we've already done a pilot with the b200 with our partner in Japan um that's going to go live in late February early March uh we're really excited about that asset because the

18:49b200 is very limited in Access uh Compu Labs is one of eight of the largest suppliers in the world for it because that Hardware won't be available to the Mass Market until like Q2 Q3 2025 but as we wait for that pilot to go live we're constantly looking for more operators and data centers to help them um we have one lined up that we're going to be soon fundraising for we're we're

19:15going to be needing a ton of capital to serve that one customer but we also are looking for small clusters you know 1 mil 3 mil 5 mil um these types of Hardware clusters or operators that need that much happy to help those as well mhm that's great and what's the best way to learn more about compute labs and follow along with the work that your team is doing yeah we have a guy on our

19:42Twitter just killing it so follow us on Twitter we have a lot of fun there we also have our own Discord Channel as well sounds great Nick thank you so much for for the insights into Computing AI where everything is going I will leave a link in the show notes to the compute lab site the Twitter and the Discord as you mentioned and wishing you and your

20:03team all the best to moving forward and I would love to follow up in the near future thanks so much Ash great to be here appreciate it