DeAI News Journal: The Money Behind DeAI

DeAI News Journal: The Money Behind DeAI

Grayscale and BlackRock both published reports pointing to the same conclusion: blockchain and AI are converging, and compute is becoming a tradable asset. Episode 3 of the DeAI News Journal follows the money.

January Jones and Brad Keoun start with the three meanings of "token" in this space: crypto coins, AI text tokens, and tokenized assets. Then they hear from investors, builders, and market watchers about how decentralized AI is being funded, priced, and traded.

In this episode

• Gil Rosen and Steven Willinger, Blockchain Builders Fund. Recorded at Stanford. Why the token launch era burned founders, and why investors now want revenue and distribution before a token.

• Greg Osuri, founder and CEO of Overclock Labs (Akash Network). The spot market for GPUs is nearly gone. Compute futures are arriving. Your home gaming rig can run your own AI and sell its spare capacity. Greg also compares today's AI giants to the Gilded Age railroads.

• Ivo Entchev, legal and product lead at Tilt. What the Grayscale and BlackRock reports signal, why AI agents will need machine money, and how compute could follow Bitcoin's path from spot trading to ETFs.

Chapters
00:00 Welcome and this week's theme
02:11 The Grayscale and BlackRock reports
03:00 Three kinds of tokens
06:30 How token pumping broke crypto startups
08:00 Steven Willinger: what's working for founders now
09:35 Gil Rosen: fundraising after the token boom
12:17 Greg Osuri and Akash Network
13:05 Access matters more than cost
16:38 Compute futures and physical settlement
19:07 Defining decentralized AI: the railroad parallel
27:32 Your home as a token factory
32:00 Compute as a load balancer for energy
39:01 A knowledge problem, not a capital problem
41:20 News roundup with Ivo Entchev of Tilt
45:08 Agents need machine money
48:00 How compute becomes an asset class

Read the stories and catch new episodes at http://deainews.com.

A Distro Media Production

Produced, edited and designed by January Jones

Transcript (by Riverside)

January Jones (00:38): Welcome to the DeAI News Journal, the podcast of DeAI News dot com. I'm Jan Ray Jones and I'm here with my co-host and founding editor of DeAI News, Brad Keoun.

Brad (00:51): Hi January.

January Jones (00:52): Hi Brad. Well, this is our third show, and like we set up our themes for show one and show two, on this one we're talking more about the money, the investment space, and how there's a market being made around decentralized AI. and so we'll follow that thread with some of our guests today. We're gonna talk to Gil Rosen and Steven Willinger from the Blockchain Builders Fund, who we met at Stanford. And then we'll also talk to Greg Osuri a kosh network about why AI compute is so hard to buy right now. And then we'll talk to Ivo Enchev from Tilt, and he's gonna put in context the recent news we heard from Grayscale and BlackRock that seemed to be legitimizing the entire DeAI. ecosystem and the reason that we're doing this show. Right, Brad?

Brad (01:42): Yeah, I mean everybody's talking about this report. it's been another big news topic of the week. And yeah, I mean the whole industry is seeing this as kind of validation that this giant money manager, probably the world's biggest money manager, is saying that this is the future, which is this intersection of blockchain and AI. So it's pretty pretty pretty fascinating. I mean, it's fun for us to be on the story.

January Jones (02:11): Yeah, for sure. And so there were two reports that came out right together, Grayscale and BlackRock, and they were kind of both leading to to the same conclusions and and calling out some of the key people who are in this industry who are who are ones we're familiar with because they are coming in this out of the crypto and blockchain and the infrastructure side, right? And using now all these tools that is enabling AI to to to run. but that gets us back to what we're talking about today. So when we put these shows together, sometimes we're using interviews like we did today, where we had them in the past and then we're putting it together into a new narrative. And I think what we saw from the themes, the themes that are coming together in today's show is about tokens. It's the token journey. So we reference a lot of blockchain and crypto projects. And of course, we saw in crypto, you know, the token is the coin a project launches to raise money. But when we're talking about AI, there's another piece that's called a token, right? And a token is the piece of the text of the model that reads and writes. then there's the other tokens that we're talking about when you're tokenizing compute and in the markets. so did I mess that up, Brad? Or is that kind of like token, token, token?

Brad (03:36): I mean, I think you got right to the spiritual soul of this discussion, which is it's so completely confusing, right? I can't can't imagine coming into this, you know, without

January Jones (03:45): Ha ha ha

Brad (03:49): having I I mean, I've been in covering crypto for well, geez, six years now. And so everything's token, token, token. I mean, that was the whole business model of so many of these crypto projects is just sell a token. Pump the token. so, but anyway, yeah, there's crypto tokens and then these AI tokens, which are like Four fifths of a word is kind of the way people have described it to me. On average, it's like four fifths of a word. So you can it's roughly the number of words, and but then there's input tokens, the tokens that go into the AI, and then there's the output tokens, which is what you get back. And those are also different tokens. And now the tokenizing stocks, those are really basically like crypto tokens, right? You're basically putting a stock inside a crypto tokens you can send it around on the blockchain and trade it on set exchanges and decentralized exchanges, just like cryptocurrencies. So but yeah, it's it's It must be incredibly confusing and they're totally different things, right?

January Jones (05:06): It can get confusing about the language, you know, for the tokens for sure. And so we'll try to define that. but it's a good starting place. So we're gonna hear a little conversation that we had at Stanford. So as we said, we were we were there and we were at this blockchain applications conference, and so I thought that was a really good view into kind of the world that we're moving into with decentralized AI. Because it was people that were moving from the blockchain world, putting the blockchain in the back and the AI in the front, as we say. and so we talked to Steven and Gil about this, and we really wanted to hear what is happening now with projects that are coming up, coming through accelerators. How are you building a business now, even if it has blockchain in it? How are you describing it? And then also like where is the oversight and the money coming from? you remember the the conversations with Steven and Gil were about to go into those clips. What was your takeaway on on their perspective? Do you think that they've come around like they're part of the crypto remorse cycle? Of how businesses were launched for a while.

Brad (06:30): Well, a lot of this was actually driven by the venture capital community out there in Silicon Valley. I mean, they would push these projects to sell a token just so they could get their money back. whereas in the old days they would take a flyer on these companies and then they would have to wait till the company went public and sold its stock. But with tokens, there was so much hype they could they can invest capital in the companies. So they're buying the equity of the companies and then somehow and then they would have these token sales. They would get all this money. The VC firms would also get an allocation of the tokens and then they would be able to sell the tokens and they would get their overall investment back a lot faster. And and so and then it just ended up killing a lot of the companies, right? If they put all their energy into managing this token and pushing the token up and that becomes their primary business. then they're not really building anything that is a useful company that would be that customers would want to use for decades. So it just sort of perverted the whole system. And I think there were some honest moments from, you know, Gil and and and Steven basically saying, yeah, the it people took it too far. And that is probably not gonna be the way that companies or that these crypto projects are making money more. So what is that new model? It's pretty interesting. I don't think it's clear exactly.

January Jones (08:00): Well, let's get into a couple clips and just hear from them because really what we were talking about with Steven, we got kind of that perspective, but what we wanted to hear from him especially because he actually teaches blockchain entrepreneurship at Stanford. So we asked him what is working right now in building these businesses up and the investment space.

Steven Willinger (08:22): for the most part, you can generally want to invest in a company that's gonna generate revenue soon and quickly and in large amounts. And so yeah, I think it's a great thing for the industry, right? Wh we now have founders focusing on the right thing. the number of founders I saw distracted by the wrong thing over the past couple of years is is way too high. well I mean the wrong thing is unfortunately founders, you know, react to signal just like any other, you know, being that reacts to stimulus. And founders were able to get lots of attention and raise lots of money, without checking the core boxes that a founder needs to to check, right? Which is understanding their market, building a product that delivers value and then, you know, selling and marketing that product such that they find their users and and build a profitable business. instead, they were, you know getting signal from venture investors who like, okay, like I want you to launch a token as fast as possible so that someone else can buy it. Exchanges who were, you know, similarly, it's like, okay, we we need as many tokens on our platform as possible so we can make money selling those tokens to retail investors. And we want you to spend all your time and energy marketing your token to those those same people, right? And and so that's all f fine, but if they don't ultimately use those resources to deliver something valuable, it was a lot of money wasted.

Brad (09:35): I asked Steven's co founder at Blockchain Builders Fund, Gil Rosen, if raising money has gotten harder for founders in blockchain based projects.

Gil Rosen (09:43): there are a few things that have shifted. One is token prices are down. Yeah. But more broadly speaking, like exit potential is also down. Right? The five to ten billion dollar FDV token launches, like those are probably not gonna happen at all, let alone anytime soon. Okay. Right? Which means that you know appetite for risk is much smaller if if your exit's gonna be five hundred to a billion dollars. So so that that's part one. Part

Brad (10:05): Right, right.

Gil Rosen (10:06): part two is you're only gonna have an exit if you have revenues in your real business. Now that we're able to actually use the technology and the it's viable, there's an expectation that someone is using it and you're able to like generate revenues. Okay. Right? So investors aren't willing to just underwrite technology anymore because it's not enough. Yeah. You need to underwrite there's a potential go to market, there's viability here. Okay. Right? So you know, we're trying to de-risk ourselves as well. Yeah. and and then kind of the last part is exactly what you mentioned where It is easy to vibe code a whole bunch of this stuff. It is easier to reach people. Yeah right. It is easier to you don't need as big a team to build an MVP, which you would need you know to be able to have those initial discussions. So we care a lot more about like, you know, yes, do you have the technology, but also do you have the the go-to-market distribution capability and can you at least validate that from conversations, from initial agreements? And if you can't, then there's a lot of risk there, and the people that are are able to succeed are the ones that have sector experience. And able to bring that and provide like a a distribution side to mitigate the risk of of distribution for any of these these technologies. So it is harder to I wouldn't say that it's harder to raise, I'd say the expectations are different. it's like yeah, you need to have more and yeah, you need to focus more on distribution because you need to be able to build a company, not just build a story and and a token that you can hype.

Brad (11:27): What about I mean do people need less money? Yeah if they can they they do. Yeah. Yeah.

Gil Rosen (11:32): A hundred percent. Like all of our teams, many of them have let go of some of their engineers, many of them just didn't end up hiring engineers. All the devs are becoming engineering managers. Yeah. And they're you know, well that's a question that we have for our teams is like, are you an AI first venture? Are you trying to optimize your functions in building your company around kind of le leveraging agentic workflows to optimize the engineering side, to optimize your go to market side and your distribution side, to optimize like all all of your you know sales interactions, your support interactions, the meetings that you're having. So it it's I think it we're doing it as a fun. We've been kind of leveraging AI to optimize a whole bunch of our work and it's been incredible and I think it's just as critical for founders.

Brad (12:17): Which brings us to our feature interview. Greg Osuri has been working on this problem since 2017. He's founder and CEO of Overclock Labs, the company behind Akash Network. Akash is a marketplace where people with spare computing power rent it to people who need it. And it's known as one of the OG DeAI projects, if that's possible. We're talking about a decade. That is a long time to be working in decentralized AI, but that's the point. He was way ahead of the curve.

January Jones (12:50): So first I'm gonna get into the question, it must be the money. We're gonna ask about the money. why is decentralized AI looking like a good cost alternative to what people are currently doing?

Greg Osuri (13:05): Yeah. I I think we it's no longer about cost. I think it's about access and sovereignty and privacy at this point. to cost. So with enough money, sure you can get chips and enough you can buy chips, but the the exuberance of cost is like not something you would compare that, hey, a cost is like twenty percent, thirty percent cheaper. it's no longer about cost. In fact, it's right now it's just getting access to H one hundreds or or B three hundreds, rather, there is a challenge with how the contracts are designed. So you no longer have like an on-demand market anymore. The spot market is almost dead for compute. it's now moved exclusively to contracts and these inflexible opaque contracts where they average anywhere from thirty-six months to sixty month contracts with twenty-five percent to fifty percent down payment. that is inaccessible for most companies, right? So and And and and it's not getting lower, it's only getting bigger. So there are a bunch of indexes now that track you know leasing prices for B300s and H200s and H100s or whatnot. The number just keeps going up. So when the number keeps going up, it's very hard to build a business on top of it, right? For example, we ourselves, you know, Akash network, but we operate a company called Overclock Labs, which is the commercial side of the Akash network, Akash being the decentralized and open network. Overclock being the commercial you know network that, or commercial company rather, that's built on top of Overclock Labs or on top of a cash network that provides the indemnity and you know SLAs and all good stuff that you need for enterprise contracts. we also have an off-take product, which is a cash ML, which is our inference product. our inference product is how we make money on top of you know on top of Akash. We we provide managed inference for Frontier Models.

Brad (15:06): An inference product again, just to sort of, you know, level set i the inference product is when you make a query of an AI and it goes and it's processing it, as opposed to the the training, right? Yeah.

Greg Osuri (15:15): Correct. So when you to Chat GPT Right. So when you go to Chat GPT and ask it a question, so that that mechanism is called inference. So there are two two types of compute or two types of workloads that are run on compute, on GPUs. One is a training workload, which is, you know, compute heavy, but also it's one time and done. And inference is actually when you use AI. So now most of the demand is inference. and for us to build a meaningful business, we need price predictability. We can have an H100, you know, that was costing about $2.50 per hour. about a couple of months ago now costing about three dollar fifty cents. When the cost of goods go up so rapidly, it's impossible to build businesses on top of it, right? So it's no longer about you know just getting like I mean su yes, cost is the overwhelming

Brad (16:09): Well doesn't that I mean the the ever rising costs, isn't that make that value proposition even more appealing?

Greg Osuri (16:19): More compelling for decentralized computer. I think more compelling for marketplaces in general. so there are a lot of innovations that are happening with open compute. Now you can do like forwards, you can do I mean we're there's a lot of experimentation around like, hey, you know, now compute should we commoditize?

Brad (16:34): These are like trading contracts and hedging and future like futures trading.

Greg Osuri (16:38): Correct. Correct. So yep. So there are a lot of announcements. See CME basically announced that they're doing compute futures. there are bun the NYSE, I mean the the ICE, which is the the holding company behind NYSE, is also you know, promoting or rather indicating that they're going to do compute futures. Most of them are cash settled, which kinda is not very helpful. but Akash has been sp is a is a spot market for compute. We've been around for like six months, six years. And we are physically settled. So when you buy a computer on a cash, you're physically taking the delivery of the compute, which is actually useful for the

Brad (17:14): That's pretty interesting 'cause I I mean y you know, I d it's kind of a separate topic, but I've talked with a couple of people about how these compute sharing networks might integrate with some of the financial trading around AI and and so you it sounds like you could be a you could deliver into some of these contracts if they had a physically settled, yeah.

Greg Osuri (17:38): Yeah. Yeah. So Akash could become a clearinghouse because we've been

Brad (17:43): huh.

Greg Osuri (17:44): physically continu continuously physical settlement is how you deliver compute. Very similar to power markets, not like oil markets, right? Oil markets is you take barrels of oil delivery, right? When you buy futures when they expire, you gotta you take delivery. If you don't, you know, y or else you gotta cash settle it. Akash has been doing physical settlement, continuous physical settlement for about six years. That's what we do. You know, we we you can in a decentralized setting, it's amazing. Any party, any counterparty without identity, without sharing identity or any of the mundane stuff that you would ask for, you can freely exchange compute and a contract. So Akash is now the clearinghouse. I mean, or or else or when the f when this futures eventually has settled, Akash could become the clearinghouse. And I think that's where puck is moving and BlackRock l literally came out with a paper yesterday.

January Jones (18:31): Yeah, we we covered that. Yeah. Mm-hmm.

Greg Osuri (18:33): About exactly the same thing what we've been doing for about six years. So I think the

January Jones (18:36): Yeah, yeah, that's good validation.

Greg Osuri (18:38): thesis is coming. I mean, time is right as compute is getting more scarce here, more expensive. People want predictability, people want maturity in these markets. and financialization of compute is is inevitable. I mean, it's not gonna be like it's not a future out thing. It it is happening right now.

January Jones (18:55): From your seat at the table and and longevity, how do you define decentralized AI in your work and when you talk to other people about it?

Greg Osuri (19:07): Good question. So I've been in the space full time for about ten years. We launched Akash about six years ago. and I've seen quite a lot in terms of how markets moved in in this time. And decentralized AI, I think like You know, a lot of definitions as to what a decentralized AI. From from my point, it really comes down to who has control over AI and who benefits out of it. I have nothing again overwhelmingly benefits out of it, right? Is there if you consider AI to be the most impactful Full innovations in the la in the last hundred years. For comparison, AI is predicted to contribute about 3.96% of GDP by 2035 with current trends. For comparison, railroads, which were considered the biggest expansion of American economy, contributed to about 2.5% at peak. We're talking about the Gilded Era where railroads were the AI back then. Even at peak, they contributed about 2.5%. So we're talking about AI more than anything we experienced in terms of GDP contribution in the history of America. So, you know, of course, I don't expect people to remember how Gilded Era was, it was, but it was crazy. Gilded era was is what led to the roaring twenties and a whole lot. Things and that's very similar, you know, things that are happening right now in AI. And there is enormous regulatory you know arbitraging by bigger companies. We saw you know Anthropic and open AI, even Elon Musk's xAI come up with recent threats about like potentially taking out humanity by 2030. all these you know scare tactics are not Uncommon. We saw this in the Gilded era. If you study the study history, you'll know ex what these railroad companies were doing. And same tech tactics are applying now. So if you have AI controlled by a few elites, few oligarchs, we're going to end up like railroads. That's the reality. And railroads did not take off in America, not because they were we were not technologically advanced or we didn't have the intention. It's just they got so oligopolized and so powerful politicized that they started they they're the very definition of crony capitalism, right? That's this the they began this idea of crony capitalism where you had regulatory advantage and only big companies could have the advantage and smaller guys could not and and a whole lot of things that led to regulation in America. We didn't have regulation before railroads, right? The idea of regulation came because of railroads, right? Because you know the

January Jones (22:06): Because of this control over basically the whole economy of the country at at the time, right? Because even though it was a a lower percentage overall of what you're saying, it was really running what was happening for jobs and expansion and and all kinds of resources, mining especially, right? yeah.

Greg Osuri (22:25): Yeah, everything ran on railroads. So you c you mean railroads ended up being real est realistic real estate trusts. They would they would have crony they would have politicians that would, you know, bribe them and get all kinds of favors. When you have too much power.

Brad (22:40): going back to this idea of, you know, you ha you had these these you know, tycoons and they're controlling a small set of companies and you know, and maybe coordinating with each other in some way. And then I mean in a good market, in a good healthy market, competition is the antidote, right? And so

Greg Osuri (23:04): Okay.

Brad (23:05): here you have these big centralized AI firms and they are making incredible advances. I mean we see it every day, just like the f the models get better and better. And I was just reading the models are actually Getting cheaper too. The AI, it gets a lot cheaper every quarter for the same performance. but is, I mean, going back to decentralized AI, is that the competition? I was there was also a similar article in in the Wall Street Journal about electricity, and when that came about, and Edison was pushing D C you know, direct current DC, and then Westinghouse came out with AC, and Edison was talking about all the dangers of AC, but The reality was there was an alternative there. And I was just wondering if that is decentralized AI versus AI or you

Greg Osuri (23:55): Correct. So

Brad (23:56): know. Yeah.

January Jones (23:56): that decentralized AI is the D C to the A C?

Brad (24:01): Well, it's the A C

Greg Osuri (24:02): AIST PUDI.

Brad (24:02): to the D C But I don't know. What do you think about that, Greg?

January Jones (24:04): it's the A C D C D C C

Greg Osuri (24:07): Yeah, exactly. it's a great I was.

January Jones (24:10): What's your question again, Brad? Could you phrase that again?

Brad (24:12): Well, the question is, you know, when you're talking about a a small group of companies controlling, you know, the means of production or, you know, the the valuable capitalist enterprise and and then you know somebody else and they can tr they also control the prices, right? And they get all the revenue. And so then if you have competition an alternative way, maybe that provides a balances the market. So th in that way would decentralized data potentially help to reduce prices.

Greg Osuri (24:45): I it's it's happening right now. so right now the way to use AI, there are two places you can get AI from, broadly speaking. One is centralized companies, Chat GPT, Anthropic, and O X X AI, and next another one is open source models. GLM five, Deep Seek four flash four four one, Qwen family of models. they're incredible open source models that do really well, they come very close, if not Better in some cases than the centralized model. Well, open source models are free to use as long as you have a computer. Closed models are not free to use, you gotta pay them, right? So every time you use an open source model, that means less money to these companies. So they're going to do a everything in their power to make sure that you use their models because there's more money for them. It's very simple, right? The value incentives are very well aligned. As an AI user, I want to pay less money, right? I want to spend less money because it's very, very expensive. in order for me to do that, I will either go find cheap GPUs or buy GPUs if I can, which is also getting very hard because the demand is so high right now. or ideally, if I'm a gamer, I would use my existing GPUs. because a lot of these gaming PCs are getting very, very good. Like very good. I mean, I I run my own home, a A Qwen 3.6 on 5090s. Qwen 3.6 is a an incredibly capable model. It is a small model, it is not frontier, but it can do most of the work in my home, like monitoring, like video feeds, audio feeds, and a whole lot of things that I want to automate my home. I don't that I don't need to use Chat GPT. you know, we is my cost is significantly lower than most people's costs. That's because I decentralized AI, right? Because I can have local AI. And now we have a program where I'm not using my AI all the time because it's just my family usage. It it it's useful in I'm maybe using 20-30% of the time, but there's 70% of time I'm not using my own local chips that I give back to Akash Network. So I'm actually making

January Jones (26:56): Okay, well let's get into that. So

Greg Osuri (26:58): money running my own AI. I'm getting my AI for free and making money to buy more chips.

January Jones (27:05): Well well, you were talking about this when we were kind of warming up for the interview, but I find this quite fascinating. And so we are talking about costs. Brad was was framing it as a cost alternative to centralized AI and and having more control, but you also have your own really interesting theory that maybe people haven't thought about, which is that your home, you can be your own data center. Basically.

Greg Osuri (27:32): Mm-hmm.

January Jones (27:32): Tell me about that thesis, how that's built into Overclock and and Akash and and kind of what you do in this space.

Greg Osuri (27:40): I would propose rebranding data centers to token factories.

January Jones (27:45): Okay.

Greg Osuri (27:46): For you know.

Brad (27:48): Refinery. They're like refineries, right? I mean yeah.

Greg Osuri (27:50): Token refineries, token factories. So I'd run a token factory at home, which makes me tokens, right? Or else I had to go buy these tokens from centralized companies, which I don't want to. I it's like growing my own food, right? I mean, I'd rather grow my own food if I have time and energy and patience for it, because I know it's cleaner, it's better, cheaper. I mean, this is very straightforward. And if I if there's a way for me to sell my excess food, I can even make more money. It's the same concept, right? It's like you don't have to get your food from a from a distributor or a or a or a a a grocery store which you n may not know what the where the food came from, right? there's all kinds of things now. Like it's getting scarier, in fact, like to get to to get even asparagus from from from stores. So the good thing about having local AI is you know you have privacy. You have you know you have because it's not leaving your own network and it's cheap. It's n it doesn't cost you anything. And you can make money selling that excess compute or capacity on Akash Network. And there's so many others now that you can actually sell inference directly, right? So many other marketplaces. Open Router is a great marketplace that came out and Stripe now owns them and Akash ML is one of the providers of OpenRouter. And Vercel has them, you know, you have Hugging Face as as a as a token marketplaces now. So there's a lot of marketplaces that are popping up and their capacity is just growing. In a in a crazy manner. That's why Stripe paid seven billion dollars for a open marketplace for inference. So inference demand is not slowing. If you have compute, you can sell that right now and you can make a lot of money. We predict if you to Akash Network, I Akash ML. Akash Home Node, that's home node. It is a program you can download directly from your for your on your Windows PC that you're playing your games on, and it'll start making money from day one.

January Jones (29:39): So you're talking about, and I and the hint of this it did come through like people that have like bigger systems at home. And I come that as like, man, the gaming computer my son built and I had to pay for was a monster. I'm like, what are you doing with this? Like, and why do we have to see into it? And why are there neon lights

Greg Osuri (29:55): Yeah. Exactly.

Brad (29:57): Same. I got a few of in in this house, yeah.

January Jones (30:02): in it? And like, what is going on with this monster? But coming from that perspective,

Brad (30:06): Cool keyboard.

January Jones (30:09): Is that you're saying when people beef up their own connection at home, that there's a way for people to really be owning their own AI compute and then selling it?

Greg Osuri (30:26): Exactly. Ex you can do that right now on Akash, like home note. Akash Network. there's also a calculator as to it'll show you how much you can make. I mean we tend to be on the lower we tend to be a little more conservative on what we promise. People actually make a lot more than what the calculator shows. Like right now, fifty ninety is a going for like sixty five cents. So that's like, you know, I don't do the math, right? Like if you're giving, you know, twenty four hours, let's see. That's about like fifteen dollars per day and that's about f close to four fifty dollars a month. And add that up and you can actually recover all your costs that you spent building the gaming machine within a year. Like

January Jones (31:06): See, he wasn't enterprising like that, my son. right.

Brad (31:09): Ha ha

Greg Osuri (31:09): And you can that means you can buy more games and more GPUs. You know, think about it. Like, what is five hundred dollars for you know, I don't know how old your kid is, teenager.

Brad (31:18): Well, I mean if you think about

January Jones (31:19): Okay, teenagers of the world,

Brad (31:20): it

January Jones (31:21): teenagers of the world, make your parents proud.

Brad (31:24): That's your new market, Greg.

Greg Osuri (31:27): I know.

Brad (31:29): But I mean, if you think about it, like you

January Jones (31:28): Seriously, we've invested in you. Now do something

Brad (31:32): you could have solar panels and I I mean I have s some solar panels and not nearly enough to run my house, but it's like a credit on my bill. But so I'm basically selling electricity to the electric

Greg Osuri (31:44): Yes.

Brad (31:46): company. But you could just keep the electricity

January Jones (31:48): So it's the same.

Brad (31:49): and sell the compute. Theoretically, right? If really?

Greg Osuri (31:52): So much better. Compute. I have this thesis called compute as a load balancer for energy. It's way better the load balancer for energy than you than your utility companies, because you make so much more per watt, per kilowatt,

January Jones (32:07): Mm.

Greg Osuri (32:08): putting compute and abstracting energy in compute, then you would sell back to the grid. Grid, I don't know how much grid pays you, but in California they pay like two or three cents, which is per per kilowatt hour, which is it's it's it's ridiculous, right? I could Take that two or three cents, give it to my AI, and I can make 61 cents per hour. If I have my CapEx already covered, which you know, which you already bought, a gaming PC, right? And a gaming PC, we're not talking about specialist hardware. If you put specialized hardware, I would do check Pro 6000s, for example, which is in high demand, which costs about, I don't know, used to cost seven grand a pop. a Pro 6000s now will make about $2 per hour for that two cents you're getting, giving back to the utility company. Like right now.

January Jones (32:51): Mm, that's a third of minimum wage. Federal minimum wage.

Greg Osuri (32:56): Correct.

January Jones (32:56): Nuts.

Greg Osuri (32:57): Along with you getting free AI. And that's the best part. Like not paying

January Jones (33:01): Yeah.

Greg Osuri (33:02): AI companies.

January Jones (33:03): Yeah, I mean that is a different way to frame it in in showing where you do have control, because I think that is an issue with the public's perception of AI. it seems like the government can't control it, even if the companies are asking for it. and that you feel like, Well, what can I do? There's no there's no kind of way to have my own sovereignty and to figure out how I can keep Using technology, keep using AI, but also maybe stick to my values a little bit, right? And not really compromise about everything that I've I've kind of like lived my life by in the technology and the kinds of communities I've wanted to build. So let's frame decentralized AI like that. Because in all the things you're saying, you have a way of thinking about the world in a decentralized frame. So tell me about how. that r is reflected in a caution, but also the bigger picture of how that's influences your worldview that may be more relatable for some of our audience.

Greg Osuri (34:09): Yeah, when I come like when I say decentralized AI, when I say control, it all comes down to like as technology matures, it gets commoditized, right? Cloud, for example, as it matures, it got commoditized. It became so ubiquitous and so necessary for all our lives. We pay a cloud tax without even realizing we pay a cloud tax. Like Amazon, like cloud companies, Amazon being the biggest one, you know, I think every half of every dollar you spend on online services goes to the And you have no say in that that that tax you pay. similarly, I mean that cloud became AI now because most of the cloud companies or hyperscalers are AI companies now, right? AI compute providers. it just metamorphed into this new thing, and it's even bigger problem now, right? So where is the incentive? Where's the money going and who is that benefiting? Now where's the money coming from? Well, it's cheap debt.

January Jones (35:00): That's the questions. And is

Greg Osuri (35:02): Yeah.

January Jones (35:02): it supplemented, right? There's so many costs that you don't see.

Greg Osuri (35:06): Yeah, yeah, yeah.

Brad (35:07): Well also, is that going to lead to massive overcapacity?

Greg Osuri (35:12): Not necessarily. I don't think we have capacity nearly to to address the need, but I think it's going to lead to a massive demand for all things, you know, that we depend on. Energy, for example. PJM is the biggest grid in America. It serves about 13 states, right? It's the state it's the grid that serves Virginia, for example. The energy prices in Virginia, which is the biggest like data center like concentrated place, went up by five times. And who do you think is paying the cost? People. Like normal

January Jones (35:44): yeah.

Greg Osuri (35:45): people. Five times over the last six years. And that's a crop.

January Jones (35:48): Yeah, it's supplemented. Your your energy bill goes up and that's what's got people so upset. So so reframe

Greg Osuri (35:52): Energy Exactly.

January Jones (35:54): this for me though. Like if you're mad about that, that's happening to you, that's when you first start noticing what is this AI data center thing happening, right?

Greg Osuri (36:01): Yeah. Mm-hmm.

January Jones (36:03): And so tell me, like, what does decentralized AI in a cosh network, how's the the pivot of thinking and access,

Greg Osuri (36:11): Right. So in that scenario where your AI bill is going up so much, what we are proposing is instead of building new data centers, let's go to houses, let's go to homes, let's go to places where there's abundance of energy that comes from sun or wind or whatever sources you can get, and let the people benefit directly, instead of just like, sure, AI is benefiting humanity, I agree, but not directly benefiting the people that are paying the price for AI. Which are normal people, either through energy, through water bills, whatever. I mean, I think the water thing is the whole meme, but the point is it's getting impacted heavily, right? And the people can benefit significantly by using their own AI. And they're not able to use AI because AI is very expensive. I mean, AI, you cannot afford agents. I mean, normal people cannot afford agents unless you have a strong business case. Like we spend so much money on AI, on on centralized AI because you know, we still use a good mix of Yeah, models, because we have no choice. And we are almost spending as much. I mean, we used to at one point spend as much money as to as we would spend on an engineer to pay the salaries on a model for an engineer. Like the same same pay. And that's not affordable by most people, right? So if you want AI to benefit people, the cost has to come lower. And the way to bring costs lower is to lower the energy cost and access costs. And the way we do that is by moving. AI to weigh the compute to where the energy is instead of trying to move AI to instead of instead of trying to move compute to where the energy opposite, instead of moving energy to where the compute is, right? And how we think about AI Akash Network is very straightforward. We're we've we've you know we've always looked at commodities that become utilities essentially, water, air, energy, compute.

January Jones (38:02): Yeah.

Greg Osuri (38:03): so you know

Brad (38:03): Well, if I can just try a different angle, capitalism, that's our system currently, and we have we see these giant AI firms, they're getting so much money, right? I mean They're getting revenue. They you know, people are signing up for for lots of subscriptions from them. on the other hand, it's not totally clear what their costs are versus the price they're selling it for, what their profit is. But I'm just wondering, you know, decentralized AI, does it it doesn't seem based on what we're seeing in the markets. It doesn't seem like there's a huge, you know, trade on in DeAI tokens or equity or whatever there is to invest in DeAI. I'm just curious like, how do you compete against that capital machine?

Greg Osuri (39:01): So it's DeAI is not a capital problem, in my opinion. It's not I I don't think it's it's under or overcapitalized. It is a knowledge problem. Like the beauty of DeAI or decentralized systems in general is it's not money, it's the will of the people, and that's beauty. That's the capital for decentralized AI. If today ten thousand nodes join Akash network because they all see the value in providing their nodes, that is that'll make Akash one of the biggest networks, right? How about hundred thousand? How about a hundred million? There are definitely a million PCs in the world that can join the network. So when you have so much supply coming in, I mean that's gonna attract, you know, attract the the the the companies that wanna use the supply. Right? So it's not it's not a it's not a capital problem. I don't

Brad (39:49): So it's just the market hasn't figured that out yet, is kinda what you're saying. Okay. Okay.

Greg Osuri (39:53): Exactly. It's very underpriced, very under under like literally, I mean like just yesterday BlackRock came and said, Hey, compute should be tokenized. Well, that's been our thesis for six years. I feel I feel vindicated. I feel incredible, right?

January Jones (40:06): Mm-hmm. Yeah, how do you feel about that? Black rock coming in. You feel vindicated, yeah.

Greg Osuri (40:12): In fact, I c commented asking like, hey, which is the most mature network that can deliver this what BlackRock is proposing? Came with Akash. I mean to Graph, yeah, you know, like you know, Grok came out with an answer.

January Jones (40:18): Ha you're like, look over here.

Brad (40:20): I think you were me you were Akash was actually mentioned in that report, I believe.

Greg Osuri (40:25): In the Grayscale report I think was mentioned, but not the Black Rock. I th I don't think BlackRock report mentioned any companies, but sure, they mentioned

Brad (40:27): the gr Okay. Okay.

Greg Osuri (40:31): e our our our white paper, basically, right? Our own pieces, right? Like

Brad (40:34): Maybe I was thinking of the Grayscale paper, but I yeah. Okay.

Greg Osuri (40:38): Right. Grayscale did mention about Akash. Grayscale did a wonderful job understanding, breaking down the structural problems.

Brad (40:44): I mean they have bags though, right? I mean they're they're i you know, they're an investor in the space. versus BlackRock

Greg Osuri (40:50): Right. But

Brad (40:53): is you know, is just a giant you know, they're they're big enough to be d

Greg Osuri (40:56): It is the biggest asset manager.

Brad (40:57): they're big enough to not be opinionated

Greg Osuri (41:00): They're the biggest asset manager in the world. I mean, there's no doubt about it. It's not it's not TC's and I felt T C's just caught up structural problem. Right.

January Jones (41:08): That's our thesis too. That's why we're doing this show, right? Because like we're seeing everybody start to pay attention to this space. And that's why it's so great to have your perspective from someone that's been in this space,

January Jones (41:20): In our endeavors to explain and understand things, like, yeah, how does this shake out at Wall Street? If we're in the beginning of a market being made, who's getting in there? How are we, you know, deciphering this information? and so today for the news roundup, I wanted to talk to someone who reads market signals for a living. So I talked to Ivo Enchev and he leads legal and product at Tilt and they build investment indexes. So I wanted to ask his take on the reports from BlackRock and Grayscale and what this really means for what's to come.

January Jones (41:59): thank you for being here, Ivo, and joining us on the DeAI news roundup.

Ivo Entchev (42:04): My pleasure, thanks for having me.

January Jones (42:07): So you are the perfect person, I think, that I've met in the crypto space over the last few years to talk about this story in particular. two of the biggest names in investing, Grayscale and BlackRock, both put out reports last week. talking about that the decentralized AI market, which we see as kind of a very broad space, really has some teeth now. They're seeing compute being such a game changer. And in fact, the same day Grayscale came out with that report, they renamed its Bitcoin miners ETF the AI Compute ETF. The BlackRock report is saying that AI agents will become crypto's next big customer. And we've been reporting about that at DeAI News. this is all about kind of seeing where everything's going. And so let's get your perspective on this. Grayscale and BlackRock, you come from a venture capital background. You're also a lawyer. This is kind of a perfect mix for market signals. Tell me how you're reading this.

Ivo Entchev (43:22): I think you're absolutely right. that this was a big week in the area of kind of you know crypto and AI. I think it's a moment that we've been waiting for for a while to see a clear path forward for the two technologies converging. you know, we often talk about AI disrupting industries because of the intelligence that comes in and and the efficiencies that are able to be gained through the use of that intelligence. but you know what what maybe people or at least ordinary people using these products don't fully understand is that underneath all of that is really a new unit of account, right? A new form of, I guess, oil or commodity for this intelligence economy, right, in the form of of

January Jones (44:08): Mm, mm. Yeah.

Ivo Entchev (44:09): compute and tokens. And isn't it I you know almost ironic, but certainly fitting that you know the industry that the pre-existing industry of crypto that denominates itself in tokens is the one to actually tokenize intelligence tokens, AI tokens, you know, in this new agentic paradigm. so I think what we saw in in both reports that you mentioned is big players identifying that we have a new asset class. that we might just call, you know, compute or a t a compute commodity that will need to be traded rapidly in an agentic economy that requires you know agents to to fund to accomplish goals i in a kind of in a native format that's not you know using credit cards or invoicing users.

January Jones (45:08): Yeah, well the way you're setting that up, to me I'm thinking, okay, we have to give agents machine money so they can trade and process things fast enough to pay for their own compute. Like what is happening here?

Ivo Entchev (45:28): Yeah, I I think that's essentially it. as as agents become more prevalent, I think we're going to recognize that you know, existing institutions and infrastructures that we have are just not adequate. you know, that we need, you know, machine readable, machine accessible, swift rails for these agents to accomplish their goals. and you know, these large institutions who put out the reports I think are looking ahead and recognizing that the most efficient way for the agent economy to work i is for agents and themselves to to acquire compute tokens to trade them to fund themselves and to to deploy them you know th they're visions of this future that are you know depending on where you sit exciting or you know scary, but you can imagine a point when you'll be able to literally invest in an agent and that agent's activities online and any return that agent is is is able to to achieve and it certainly is part of that process it will need to trade its own compute and we're already seeing the beginnings of that asset class emerging and it's very likely to grow and and and and really become one of the dominant asset classes in the world.

January Jones (47:02): Yeah, I think one of the things that's interesting is to the timing. And so we're having kind of this validation. on the show earlier. we talked to Greg from a Kosh network. He's feeling himself. He got named in that Grayscale report. he's like, Yeah, I told you guys I've been on that, right? about this decentralized compute and the markets and and the power needed.

Ivo Entchev (47:29): Well, i i first of all I do think it's emboldening to to anyone in the space that who has been anticipating this moment of intersection and has been identifying this asset class as one that should appear natively on chain and be accessible to agents. And as you pointed out, I think the space was was quite early with some of these deep end projects focusing specifically on compute and and being able to aggregate compute through you know decentralized networks and and supply where it was needed through through a completely different business model, I think when we talk about the maturation of the asset class, right, there are priors. Like we can look at Bitcoin, I think, as an example of a n of a completely new asset class, right? Did not exist. emerging asset, as those who bet early will tell you, you know, one that with enormous upside that that was invested in by by true believers at the outset, but required a lot of intermediate kind of you know, successes, right? and and ecosystem partners to to develop in order to to lead it to its ultimate sort of triumph, you know, is remarkably a a j a a a government sanctioned, you know, digital asset, and

January Jones (48:55): Yeah.

Ivo Entchev (48:56): sanctioned in a good way, right? but you know, there's a process of maturation, right, that these these assets go through this that that starts with, you know, spot trading. that, you know, as those will remember who were in the space early on, is is often dislocated, meaning between venues. You know, there were people who did quite well in the early days of Bitcoin, for example, just arbitraging the price of Bitcoin across crypto exchanges. You know, that that was just the very nascent stage. And then you have of course, you know, price infrastructure develop. You have this arbitrage opportunities being eroded. and you know futures markets and then eventually etfs and and and these sorts of things and I suspect we'll see the exact same path here arguably on an accelerated basis for compute commodities. What I found interesting just because I work currently more in the index space is that we're seeing now the emergence of indices, price indices for AI compute. and and people are interested in knowing what what is the price of compute at any given time and they consult indices for that information. that to me is is just another indication that we are very fast looking ahead to the maturation of this of this asset class into something that you might hold in your ETF eventually.

January Jones (50:19): Well, thank you so much for joining us today on the DeAI News Roundup.

Ivo Entchev (50:24): My pleasure. Thank you for having me.

January Jones (50:25): Alright, well, thank you so much for joining us. check out our stories. We're publishing all the time on DeAINews.com. we're on socials. We have a TikTok, even though we're old. we're also doing X and LinkedIn and YouTube. And we're really posting great clips from all our guests. And you could also find extended interviews and hear more from them than what we do produce in the show. So please check us out there, and try to learn some more about what we're doing. I'm January Jones. Thanks for being with us here on the DeAI News Journal.

Brad (51:04): And I'm Brad Keoun.