Decentralized AI

“Amazon Web Services, the behemoth, started off offering one product and that is archival storage — S3… what they had is an incredibly valuable product that nobody was building, and a market for it. Our product is an unstoppable low cost high quality cloud that nobody is building, and we’re serving an underserved market which is the DeFi and machine learning.” — 01:07:44

“The biggest impediment for AI right now is the cloud. It’s really bad — Amazon, sorry. A[ndreessen] Horowitz came out with a report indicating that about 20 [percent] of the companies that do AI, their margins go to Amazon. It’s ridiculously expensive.” — 00:33:18

“With interchain accounts, a Secret account can directly deploy [on] Akash without needing an Akash account… adding privacy to deployments has been a thing that I always wanted. And with Akash eventually supporting secure enclaves in the future, you’re going to be able to run Secret nodes as well on Akash… and the GPUs — you can imagine Secret deployments that can do machine learning. You get into all kinds of possibilities.” — 00:41:30

“Based on trends that are happening in terms of content creation, there’s going to be a convergence of web3 and generative AI, and we want to empower that merger… having this space as a first class citizen is going to be a big win in the coming months to a year.” — 00:37:47

“GPUs and AI, which I totally believe is the future of humanity… it’s going to come by storm… in my life I use AI so much that it’s only increasing my usage of AI. So when you have such an important element of our humanity, we can’t let few companies control their supply… that’s where democratization becomes a lot more obvious… I’m hyper bullish with GPUs on Akash… this is only going to get bigger and bigger.” — 00:41:41

“There’s going to be quite a lot of value that will be exchanged in Akash in a very very short future… a simple workload on machine learning costs about 30 to 40,000.” — 00:06:30

“You can generate a whole NFT series in a matter of hours using AI. So I think you’re going to see evolution in terms of NFTs and how AI is going to influence that, when you have art pretty much a commodity here with GPUs at this point.” — 00:28:42

“My mission in life: in the next 12 months Akash will be the best platform to deliver AI workloads, like centralized, decentralized, [whatever]… because that AI is too important to be centralized.” — 00:46:35

“The major, major, major milestone I think the next 45 days is going to be Akash AI… It is going to be the best AI developer product and that’s the goal. It’s not about non-custodial nature, it’s not about crypto… AI developers are not going to use Akash because it’s decentralized; AI developers are going to use Akash because they get cheap GPUs and good developer experience.” — 00:43:52

“Web3 is incredible to attract supply because it has [an] amazing incentive model, amazing network effects, amazing distribution mechanism to attract supply — but terrible at delivering value… the web3 [is] the supply, the AI is the demand… Did you see the recent ChatGPT numbers? 1 million users within five days and 100 million users within 45 days — the growth of demand is incredible.” — 00:38:26

“In the future [access] is going to be from each other, because right now… it’s impossible to get them, not even from cloud providers, unless you know someone very higher up… I think if you think GPUs are going to be the power of the future, we need that to be decentralized, and that’s what we’re working on.” — 00:04:23

“Decentralized machine learning is extremely complicated… these machines, in order to learn, need to be very, very fast… it’s very hard to do decentralized machine learning, right? That’s why machine learning is very, very centralized.” — 01:02:16

“[It] has to be decentralized, has to be in the hands of people… The fuel for AI is GPUs. If you take away GPUs, [AI does] not exist, and whoever controls GPUs controls the world.” — 00:45:12

“Their community just celebrates stable diffusion and… render entering the AI era, where in reality Render can run one model; Akash can run about 15,000 models on Hugging Face — any model on Hugging Face you can run on Akash.” — 00:48:21

“We’re starting the incentivized testnet in a week or so — we have about 300 signups right now… a lot of them are doing machine learning, so about 150 new machine learning applications soon. So as we launch our mainnet I think we’re going to see an explosion in machine learning, because the value prop is very clear and demand is very clear.” — 00:51:55

“With AI, with the way we’re going… a permissionless, decentralized GPU network is going to be so critical to power this enormous demand that we’re getting with AI. A lot of times it’s just interacting with a chatbot… but you’re talking to an AI and that AI is using Akash in the background. You’re not going to know — you shouldn’t know. Most people don’t know Netflix runs on Amazon Web Services.” — 00:26:40

“Yes, it is possible to distribute the training across different GPUs. In fact there is a proposal on Akash discussions talking about how they plan to use about 24,000 A100 GPU hours to train across a distributed cluster… so yes, it is possible to train on geographically distributed clusters on Akash.” — 01:20:48

“One attack on Render, one state attack on Render, well, if OTA, the company behind Render, it disappears. The Render network disappears right now, right? If Akash, the company behind Akash’s Overclock Labs disappears, Akash will run, right? Overclock Labs operates one provider out of 50 other providers.” — 00:47:03

“Decentralized open systems, even though they move slower, they go further versus closed source systems move faster, but there’s a limit as to which they can scale, right? There is nothing more scalable than the worldwide web. Why? Because it’s decentralized… it’s actually going to be decentralized solutions. I have very high confidence in that.” — 00:26:39

“The race to AGI is on. The question is, who’s, is that going to be a centralized or a decentralized player? I want more equitable. I don’t want a single corporation. I want the common to own the AGI… I want to see decentralized machine learning take a central stage.” — 00:26:39

“Today I saw some stat… it said one percent of global GDP will be used for machine learning very soon… I want Akash to have a sizable market share of the one percent of GDP. That’s my metric of success for Akash.” — 01:35:03

“It’s funny that Akash seems like GPUs overnight, but if you look at our white paper, the first section that talks about motivation — we talk about Akash, it’s primarily built for machine learning. This is published in 2017, 2018.” — 00:24:21

“Their business model is to make money on you. You are what they’re selling… you are what is training ChatGPT. The question is, do you want to do that and live in a world [of] feudalism — that means very few people having a lot of control and a lot of power over freedom of choice and liberty?… Akash has to be successful for this future that we want.” — 01:38:36

“We saw a full foundation model proposal to run on Akash that consumes about 24,000 A100 [hours] — the first time ever a foundational model is being trained on a decentralized cloud. The notion today that foundational training is a lot better on centralized versus decentralized is going to be debunked.” — 00:28:57

“GPUs are the new oil, and whoever has GPUs essentially controls their destiny in terms of their AI. I’m a big believer in sovereignty when it comes to AI. I’m a big believer in decentralized compute networks where I should own the A100s or H100s that train my AI, and I want to control the cost.” — 00:36:08

“We essentially theorized this supercloud idea in 2018 in a white paper called Akash Network… we were the first ones to even introduce the idea what a functional supercloud would look like, and Akash I believe is the only supercloud as far as I know.” — 00:22:00

“If you’re telling me the gatekeepers of this AI — which is essentially the people that have GPUs — are someone that has their shareholders’ interest at their heart, versus the consumer or the society or humanity… [that] is troubling for me, a lot of us.” — 00:15:29

“We’re also seeing some of the decentralized networks now training on Akash, right? So like BitTensor and networks like that. So Akash, I mean, I predict Akash to be the platform of choice for decentralized machine learning to thrive.” — 00:22:10

“If you ask me, I think it’s going to take a while by the time you can see something practical in this area… open source AI is doing well and open source AI is not on-chain AI — Akash is great for open source AI. We’re at least like five to six years away from on-chain AI.” — 00:56:58

“GPUs are the fuel for AI. Without GPUs there is no machine learning or AI. So if you think AI is going to be the most important technology stack of our modern society, access to AI is heavily controlled by few players, and that’s not a future I want to take.” — 00:10:06

“They talk about how they couldn’t find compute on Amazon, but they could find compute on Akash. So I think for the first time we have something so critical that Amazon of the world doesn’t. And we have an incredible shot to get this right.” — 01:05:51

“Today I’m going to talk about why crypto is saving AI and why AI needs crypto — and not the other way around.” — 00:00:05

“I believe humans will do the right thing when given that they have agency to do the right thing — and not some five unknown board members that take over the most powerful AI for a few days and we have no idea what’s going to happen. That’s why I believe proof of stake networks, or crypto networks, are a lot more poised to prevent such a Skynet in the future.” — 00:24:18

“All the way from GPU access to the layers in the middle — when you have verification and fault tolerance and caching and load balancing and all the good stuff — to the UIs and UXs of the world.” — 00:13:38

“If you look at ZK… last I checked, for something like a Stable Diffusion verification, a ZK proof will take 40 minutes to verify that the result came back exact. So if you’re waiting 40 minutes before you can get your image generated…” — 00:37:18

“The app layer absolutely captures the most value — it’s ridiculous to say that any other layer does… it really comes down to: does the product work or not… $2 H100s, beat that… A company that’s going to use Nvidia is going to be a lot more valuable than Nvidia.” — 00:33:03

“Bittensor, for me, is the orchestration layer for AI… it gives you this orchestration layer that comprises different subnets that can talk to each other and create larger systems… every subnet offers something of value, and Subnet 27 offers the spice — the most critical aspect of AI training and inference.” — 00:32:50

“What happens when you have an unstoppable GPU network that functions only on crypto, where when machines are used there’s no way to tell who [is] the machine or a human, and when machines are used no one can stop them.” — 00:28:52

“Thumper is another startup that did a full foundational model training from scratch on Akash using 24,000 A100 hours, which is a first example, I think, a decentralized network is used to train a foundation model. It took about two months to train.” — 00:16:30

“I think we’re seeing a lot of progress in the decentralized training space… especially a lot of the research papers that are coming out, like DiLoCo and whatnot, that propose training on heterogenous, heavily distributed compute — so seems to be promising.” — 01:30:48

“No one solved distributed training. Google DeepMind wrote a paper on it — even they haven’t solved distributed training… if someone comes and says they solved distributed training for machine learning, no they have not. That’s just the reality.” — 06:58:42

“I think we’ve been seeing a lot of progress in the decentralized training space. I’m very, very positive, especially a lot of the research papers that are coming out, like DiLoCo and whatnot, that propose training on heterogeneous, heavily distributed compute — seems to be promising.” — 01:08:14

“People make crazy claims… ‘we solved distributed training’ — no, no one solved distributed training. Google DeepMind wrote a paper on it; even they haven’t solved distributed training… if someone comes and says they solved distributed training for machine learning, no they have not. That’s just the reality.” — 04:19:16

“I think we’re seeing a lot of progress in the decentralized training space. I’m very very positive, especially a lot of the research papers that are coming out, like DiLoCo and whatnot, that propose training on heterogeneous, heavily distributed compute — so seems to be promising.” — 00:18:47

“I highly recommend folks go to Akash Network, [get] GPUs, and proof is in the pudding. And I think, you know, AI on blockchain is a reality now, and blockchains are definitely saving AI.” — 00:13:34

“[OpenAI] makes about $2 billion a year… in revenue. Now what the emissions are for BitTensor per year? $1.5 billion… BitTensor has similar levels of revenues or emissions as open AI revenue. So we are right there at that scale.” — 01:06:22

“If someone discovered AGI or artificial general intelligence or ASI artificial super intelligence, there is no incentive for them to share that with the world. Because they can go on a stock market, public market and just rip it apart… So it’s very, very important ASI is not closed. And very important access to ASI is open and is public utility rather than corporate assets.” — 01:05:37

“If we don’t do something about it we’ll end up in a feudalistic society. The ones that have the power of AI will have the power over society. That’s how feudalism was — we let people gain power without checks.” — 00:36:33

“Mark my words: no one solved federated learning on heterogeneous GPUs… While it’s possible to train over geographically distributed clusters, it’s not practical because it takes much longer.” — 00:15:57

“Compute should be separated from state. Just like how we have a separation of money from the state with Bitcoin, separation of intelligence from the state is going to be very critical as well.” — 00:34:25

“It begins with compute, right? Access to compute is fundamental — I mean, the spice must flow… We saw promising work by Google DeepMind, the paper called DiLoCo… heterogeneous training, federated training — a lot of work happening where you can leverage GPUs that are globally distributed and optimized for latency. So when we crack that, I think we can crack AGI much sooner.” — 00:13:45

“I think AGI should be decentralized. I hope it will be, but the way the things are going right now it doesn’t look like it’s going to be.” — 00:31:20

“The best part of it is it’s on demand. It’s the only network right now that can offer on-demand H100s at a reasonable price… there are networks that could do on demand, but not general purpose compute, so Akash is the only general purpose platform.” — 00:11:30

“OpenAI has chips and Anthropic has chips and we have chips, you have chips, everybody has chips. If we all share the chips amongst ourselves we can create a much cheaper world, because now we don’t have isolated clusters but rather a shared cluster. I think compute should be a common good. When that happens the cost will be significantly lower, because now we don’t need new chips coming from Nvidia as much as we are needing now.” — 00:13:46

“We had Prime Intellect that did an integration with Akash and doing federated learning, based on a new paper called DiLoCo, which is by Google DeepMind, that really talks about how can you do heterogeneous training on distributed clusters… and running on Akash right now.” — 00:14:21

“Akash now is the de facto standard for decentralized AI. You’ll see projects like Venice AI, which is co-founded by Erik Voorhees… him embracing Akash as the DeAI layer for Venice AI, which is a privacy-optimized, censorship-resistant AI chat service.” — 00:12:20

“The notion that decentralized systems cannot be fast is just wrong… Unlike last year, this year we actually have real products — it’s not some ideas. Venice runs on Akash, Brave runs on Akash, Nous runs on Akash, Prime Intellect… this is not just some future promise, this is real product-market fit, this is real revenues, real users.” — 00:15:15

“This is the problem with relying on centralized AI — this is a live example. We cannot rely on centralized overlords because we can’t get things done. If only Anthropic would allow me to have unlimited API access — I mean, I’m paying for the API, it’s not free, but still they want to limit me [to] a million tokens per day, or else [I] have to contact the sales people.” — 01:13:10

“Decentralized AI is going to be such a reality that people don’t really understand the power what it can represent… When you remove the finite [limit] and you can connect the global devices… you’ll be able to have your fridge contribute to the global machine learning machine… the future of AI is decentralized.” — 00:31:28

“We have a lot of data — you and I, we own our own data — and the AI systems don’t have access to all the data that’s sitting in our own devices. Is there a good and easy and safe way to share this data? That’s where crypto does an incredible job in allowing for sovereignty at the same time access to your data.” — 00:01:29

“These are the digital lords we are going to be bound to. If we need compute, we need to ask these people to do AI… we’re all peasants, and this is the path we’re going towards — a future where only wealthy corporations can control access to who gets to do AI. You will be rationed, you will be provisioned, and you will have [to] answer to new digital lords. That’s the future we’re go[ing] down if [we] don’t decentralize sooner.” — 00:10:22

“Akash today is used by numerous companies, the biggest one being Nvidia. Akash, I believe, is the only protocol integrated into Nvidia products. Today you can actually go to Nvidia Brev and deploy an Akash instance. Companies like Nous, Prime Intellect… Nous Research is one of the top AI companies that recently figured out how to do distributed training.” — 00:13:17

“He talked about needing hundred billion dollars to do AI training and needing to build their own nuclear reactors to power AI data centers. These are the digital lords we are going to be bound to. If we need compute, we need to ask these people to do AI… we’re all peasants, and this is the path we’re going towards — a future where only wealthy corporations can control access to who gets to do AI. You will be rationed, you will be provisioned, and you will have [to] answer to new digital lords. That’s the future we’re go[ing] down if [we] don’t decentralize sooner.” — 00:10:22

“Nous is an incredible AI company using Akash… they figured out how to do distributed training — that is such an important puzzle for AI — and they’ve done the research on Akash. Prime Intellect, same.” — 00:39:39

“The reason I use Venice and not ChatGPT is because I know for sure something as private as my medical report, I know for sure that’s not going to be abused.” — 00:38:54

“The real risk is us as a society going back to feudalism. I call this digital feudalism… the future is bleak where a corporation or a business dictates who gets to do AI, dictates a ration of resources to use AI. And we’re kind of seeing that right now — you’re seeing Amazon and Google of the world rationing computing power… we call [it] the spice, but the spice must flow.” — 00:08:05

“Nvidia happens to be a biggest user of Akash. In fact, Akash is the only crypto protocol Nvidia uses — Akash is integrated into Nvidia products. And incredible companies like Nous Research, which happens to be a top tier AI team that’s working on decentralized training, to University of Texas, to [Rochester Institute] of Technology.” — 00:12:25

“We have a lot of data — you and I, we own our own data — and the AI systems don’t have access to all the data that’s sitting in our own devices. Is there a good and easy and safe way to share this data? That’s where crypto does an incredible job in allowing for sovereignty at the same time access to your data.” — 00:01:29

“We are working right now with hardware verifications using trusted execution environments — which I was vehemently against, but I kind of gave up my fight and I started accepting TEEs as a way to go… the future of Akash verification is TEE now, and there’s a proposal out there that will be enabled very soon.” — 00:05:10

“The choice is really like, is it going to be Larry Ellisons of the world, the new digital lords of the world, that are going to control who gets the chips, to us peasants? Or is it going to be us, the people that actually rely on these chips and use AI on a daily basis…? That’s the crossroads we’re at with AI right now.” — 00:07:22

“Models’ biases are as good as the data it gets fed and the data it gets reinforced… it was heavily biased because it was reinforced by people in San Francisco… I think one of the reasons why decentralization needs to exist, more importantly transparency, is to understand these biases.” — 00:18:02

“Most of the data, if you look at it, is in devices they’re owned by people, and it’s extremely hard to get — the more sensitive the data, the harder it gets… I personally wouldn’t be comfortable giving my medical data or my diagnosis to a system that doesn’t guarantee the privacy… and decentralization is an answer.” — 00:08:35

“If you want to unlock true decentralization you have to develop techniques to overcome Sybil attacks… we looked at just the sheer amount of compute availability a single company or single entity could have — there’s a limit, there’s a bound. If you want to remove that bound you have to be able to have a peer-to-peer mechanism where the access and permission or membership to the cluster doesn’t require a permission… and the foundation for that is verification.” — 00:05:42

“We all saw what happened when five people on OpenAI board can effectively control the most powerful AI in the world for however long — for a few days. That is scary because these are not elected officials, these are relatively unknown members. A private corporation can control such a powerful AI — imagine how fragile we are.” — 00:06:33

“The biggest risk as a society we face is the risk of digital feudalism… AI is augmenting intelligence, is really extending how we think and how we work. AI is a way to buy time so we can do more as humans. So we have such an important technology piece that is controlled by so few.” — 00:05:05

“A bigger question is what happens when we finally discover AGI — there’s no incentive for the individual that has AI to share it with the world, because what happens when you have the most powerful AI, natural tendency is for you to keep it to yourself and benefit… versus sharing the technology. That’s something I think is super risky — that’s the future we have if we don’t look at how to decentralize or bring the sovereignty to the people that actually use AI.” — 00:07:16

“It’s not necessarily throwing a bigger model that may result in better intelligence… there’s a strong probability it’s going to be a lot of smaller models that may come together that may lead to an AGI.” — 00:26:41

“It’s only a matter of time that the decentralized networks that can leverage distributed compute will be challenging hyperscalers because of these technologies that are getting to main stage.” — 00:09:31

“If you ask me, six months and a year from now, sitting on the stage for Cosmoverse 2025, the story will be different, and the story could be we have 100 billion parameter model, or could be a trillion parameter model actually live — and couple of nuclear reactors under construction too.” — 00:29:39

“Concentration of power leads to stifling of innovation and will lead to something we call digital feudalism. Feudalistic societies were where few had the power on the many… with this current concentration of power we are very well going back to the feudalism, especially considering the power AI has to control your thought, to influence how you think and what you do.” — 00:07:23

“Self-cooling, continuous data centers with 30% more efficiency and half the latency to power AI: satellite data centers. So it’s not too far in the future we’re going to have data centers in space, and the question is how do these data centers need to be networked — is it going to be centralized, decentralized? I would bet they’re decentralized.” — 00:15:15

“AI training has been traditionally very centralized, in the sense that you needed collocated clusters locally present to do any meaningful training. But that changed with Akash. Today we see companies like Nous Research, companies like Prime Intellect, that actually cracked decentralized training. The first billion parameter model by Nous Research was trained on Akash; [the] first 10 billion parameter model by Prime Intellect is being currently trained on Akash.” — 00:13:54

“Can we actually create truly decentralized systems that are trained on decentralized compute networks? And the answer is yes, we can. The first example we saw was a billion parameter model by Nous Research which trained truly on distributed GPUs.” — 00:09:27

“If you can fractionalize the ownership by giving access to the LLM without opening up the weights, I think you have a business model, and folks that contribute to the training of this model, whether in compute or cash, can get this fractional ownership. So there could be an NFT that represents a fractional ownership.” — 00:28:13

“It’s very dangerous because now we are entering an era of digital feudalism, especially as we get towards AGI — whoever has AGI controls the world.” — 00:25:30

“I think you can train a good Llama equivalent for about $50 million… there’s a lot of money in crypto that, if we all pull together, we can train the next trillion parameter model fairly well.” — 00:24:46

“Elon Musk is famously known for saying how hard it is to get GPUs — him being the richest man of the world — he said something in the lines of GPUs are harder to get than drugs… Larry Ellison was last week talking about how he had dinner with Jensen Huang, the CEO of Nvidia, and literally begging him… to give GPUs, and Jensen wouldn’t give because he just don’t have them.” — 00:06:37

“I use machine learning or AI models now to raise my kid or to do my work and pretty much even correct my grammar or every single email goes through this like AI models. So, AI is increasingly getting to be the most important layer of my life and I believe it is going to be for most of us on the planet.” — 00:00:00

“With AI, where you have intelligence that’s going to be electronic now, just like we decoupled money from the state with Bitcoin, it’s also very important to decouple intelligence from the state. And I think extending Bitcoin security to achieve that decoupling is a great future I see for Bitcoin and Akash.” — 00:01:45

“You can see a future where you host a model, pay with Bitcoin, secure on Bitcoin. Akash becomes this application layer and Babylon becomes the bridge to Bitcoin. So you have this whole stack that essentially brings intelligence to Bitcoin.” — 00:04:58

“Post-ICML we’re going to see the DeAI summer. That’s when you’re going to see all these fun stuff — the tokenized ownership, we’re going to see fully open source usable models… a lot of the companies that have been working very hard on verifiable training [and] inference will have some product that’s demonstrable. I think 2025 mid is when we take mainstream main stage for AI.” — 00:28:13

“A 10 billion parameter model is somewhere in between GPT-2 and GPT-3… what I heard from the grapevine is we’re going to have a 100 billion parameter model that’s going to begin training in the next few months, even before the end of the year, and after that we’re going to have a 500 billion and a trillion — GPT-4 is a trillion parameter model. So it’s only a few generations we’re away… and that’s catching up really quick. The only limitation is the incentives, and crypto is phenomenal at devising incentives.” — 00:26:03

“Especially with crypto now we’re entering this incredible bull phase… I predict mid 2025 is when the decentralized AI is going to shine, because we have a lot of companies… that figured out how to do verifiable machine learning… I think that’s going to explode and I think Akash being the foundation layer for this new era is going to be an incredible opportunity for Akash to really shine.” — 00:40:19

“The first billion parameter model is being trained now on Nous — by the way, using Akash. First 10 billion parameter is being trained by Prime Intellect, all using Akash… Next model is a 100 billion parameter model, and after that you’re going to see a trillion parameter model… With enough capital and with enough incentives, I don’t see a reason why training over the internet can be a profitable venture.” — 00:37:27

“Nous recently came out with this incredible technique called DisTrO, which really is a practical way of leveraging decentralized GPUs for machine learning training. For context, training has been extremely hard to do on decentralized or distributed networks because of latency requirements, but Nous has figured out how to do that on Akash Network. So Akash is unlocking this new generation of companies that are able to get resources that they couldn’t get from traditional cloud.” — 00:18:51

“The amount of compute, amount of GPUs we need by 2030 will account to about 5% global GDP that’s being spent — so 5% of global GDP will be spent on AI compute. Now do you want that money to be controlled by the few companies, like four companies, or do you, the user who uses the AI, should be in control?” — 00:01:29

“We’re at this crossroads of this incredible technology wave called AI, and if we don’t own the AI, we will be owned by the companies that give us AI. That is the impact Akash is having — Akash makes you, the user, own your compute.” — 00:02:14

“There are a lot of people working, very, very smart people working on this stuff, that I have a lot of confidence that by mid-2025, when ICML happens, pay attention to the papers that are being submitted… and I think mid-25, you’re going to see decentralized AI go mainstream, like, no doubt.” — 00:38:57

“So we have to, you know, build nuclear reactors in the next two years, which is not going to be reality. So we’re going to have to explore very seriously about distributed training. Right? So, distributed training also means distributed grid. That means you can actually go, instead of concentrating too much compute in a single data center, you distribute that compute all over.” — 00:31:27

“I might make another prediction: with the advancement of DeepSeek, I think [the] market is going to realize funding private companies billions of dollars is not the way [to] AGI, versus having a shared ownership model that taps into global liquidity… I have a strong feeling that right now, with DeepSeek, AGI is actually possible on chain versus in a private setting.” — 00:43:25

“With DeepSeek it clearly proved that open source AI is going to overtake, and DeepSeek is going to even accelerate decentralization… I think the summer is actually accelerated — maybe here, maybe late winter or early spring.” — 00:37:42

“We’re not as fast as China to build out nuclear reactors — they can do it in three years. So I feel like we have a good five-year shot for decentralization to take mainstream.” — 00:19:56

“This paradigm shift is a game changer for training, and I think accelerated our AGI timeline significantly, because now we broke the data challenge.” — 00:12:50

“The real risk is us as a society going back to feudalism — I call this digital feudalism. There was a time in history that few people had most power in Europe and peasants had to depend on these people for accessing basic things like food and water, and that is a future we’re facing right now. The future is bleak where a corporation or a business dictates who gets to do AI, dictates a rationing of resources to use AI — and we’re kind of seeing that right now, Amazon and Google of the world rationing computing power… rationing, or we call it, the spice. But the spice must flow.” — 00:08:05

“Nvidia happens to be a biggest user of Akash — in fact, Akash is the only crypto protocol Nvidia uses. Akash is integrated into Nvidia products, and incredible companies like Nous Research, which happens to be a top tier AI team that’s working on decentralized training, to University of Texas to Rochester [Institute] of Technology — Akash has a wide ecosystem in the traditional AI world and also in the decentralized world.” — 00:12:22

“There’s no doubt in my mind that crypto is the only framework to have monetizable machine intelligence… it’s going to be multiple people, multiple organizations, multiple protocols interoperating with each other, permissionless.” — 00:46:54

“The major, major milestone — I think next 45 days — is going to be Akash AI… It is going to be the best AI developer product, and that’s the goal. It’s not about non-custodial nature, it’s not about crypto… AI developers are not going to use Akash because it’s decentralized; AI developers are going to use Akash because they get cheap GPUs and good developer experience.” — 00:44:16

“I love to see more work, more different approaches and more experimentation in the space… and really take the power away from the OpenAIs of the world. If you want to really disrupt them, you have to think of decentralization.” — 00:56:34

“Google wrote a paper called DiLoCo last year, and a company called Prime Intellect implemented [it]… they were able to train a 10 billion parameter model over the internet… another company called Nous Research is going to do a 15 billion parameter model soon… there are five other companies doing decentralized training.” — 00:20:58

“Training has been predominantly collocated… but that was until about 3 months ago… Prime Intellect, Nous Research… created by former Google DeepMind, big AI lab employees — they’ve solved distributed training fairly well. Now we have a 10 billion parameter model that’s fully distributed, training across the internet. There’s a 15 billion parameter model by Nous starting training in a couple of weeks — and they all use Akash. That’s how I know about these things.” — 00:13:25

“Now we’re getting into a bigger challenge for AI… it will be so obvious in a couple years that there’s no way we can scale AI the way we’re scaling AI. It’s obvious in the circles now, but I think it’ll be obvious to the world in a couple years that the only way to really build AI is through a decentralized system.” — 00:05:34

“It is perhaps the most important technological shift of our society… it is becoming to be a [feudalistic] society. People that have access to AI are going to win out over people that do not have access, and companies that control this access are going to amass so much power. If we don’t do something about it now, we’re going to lose out, because it’s happening very, very, very fast.” — 00:19:26

“I think Messari will eventually probably create a DML category sometime… DML is in a very, very early stages, but all of us are trying — I think we’re very close to catching up to the centralized AI.” — 00:40:14

“If you want to train advanced models, you need low latency, very minimal message-passing latency between two neurons. So if you have to do it in a decentralized way, that’s not possible… the trade-off is, yeah, you can get lower cost, but training time is going to be higher. So you’re never going to catch up to a GPT-4.” — 00:43:05

“It’s very important for machine learning to be decentralized and owned by the public. That’s established. And now the question is who is actually building this decentralized vision.” — 00:33:45

“Today GPT is trained on yours and my data, we gave for free essentially, so it’s taking our data and actually capturing quite a lot of value, and I think that’s wrong. So you should have control, sovereign, about your data and what you want to do with your data. If you want to get paid… I think that should be very primitive.” — 00:39:32

“This like new era of decentralized AI, which I believe will take off mid year, like June, July timeframe… there’s going to be an explosion of decentralized AI, but the beginning of the training, and then we’re going to go to inference… So every layer of AI will be decentralized.” — 00:47:57

“There’s enormous amount of research… Google really released a paper called [DiLoCo] and a bunch of companies actually implemented this paper and actually proved we can train a 32 billion parameter model fully distributed… you can have a cluster in New York… Singapore… San Francisco and actually contribute to a training run… That was not possible a few years ago.” — 00:26:13

“We’re still in the GPT-3 era of decentralized distributed training, but it’s progressing very fast, and only limiting factor is the amount of compute you can throw at it… how do we get the compute is through incentivization. So if you can create a viable incentive model, that’s where crypto plays a big major role — if you contribute compute, you somehow have rights in the future earnings of that model.” — 00:18:09

“If you can figure out how to do distributed training, I think that’s a big winner. So there’s a case to be made here, and that’s why I’m hyper bullish on decentralized AI, because the innovations that’s been happening over the last, let’s say, three to six months in distributed training is very promising.” — 00:17:25

“From an inference standpoint, which I believe is going to be much larger than training, as you start building more products, as AI gets more relevant into people’s lives…” — 00:12:24

“AGI — the ability to reason, plan, and learn broadly — could become the most important invention of the next 500 years. But it’s built on closed infrastructure. We risk bottlenecks, capture and fragility. We don’t just need open models. We need open infrastructure beneath them. We must decentralize the foundation before it ossifies.” — 04:26:47

“Nous pushed scale even further — a 405 billion parameter model trained in the wild, fully open source, fully decentralized. This isn’t the future. It’s the present. Together these breakthroughs prove… we can train powerful models without centralized servers, we can verify correctness without trust, and we can scale intelligence without monopolies.” — 04:31:19

“Centralized AI infrastructure creates single points of failure — physical, geopolitical and economic… We’re not just talking about uptime. We’re talking about national strategy. In a world where compute is power, open systems are the line of defense. This is how we preserve freedom in the age of digital acceleration, by decentralizing the power that drives it.” — 04:41:37

“Six months ago, decentralized AI training was a vision. Today, it’s a proven path… 405 billion parameters trained in the wild. This isn’t a science project. It is a movement… And just like Bitcoin proved for money, this proves for intelligence: we can build better systems by distributing them. Decentralization isn’t just viable, it’s inevitable.” — 00:06:46

“Until recently, decentralized AI was just considered impossible — too slow, too fragmented. But in 2025, that changed. Pluralis introduced async swarm training… Gensyn trained a 72 billion parameter model on a decentralized testnet — no campus, no cloud, just thousands of volunteers with GPUs. Bagel used ZK-LoRA… to verify decentralized fine-tuning without needing to rerun it. Nous is training a 405B parameter open model using distributed GPUs.” — 00:06:45

“When compute lives in a dozen hyperscale zones, it’s easy to surveil, easy to disrupt, easy to control. But when compute lives in 12,000 locations it’s resilient, it’s redundant, and it’s harder to censor, harder to kill, and harder to co-opt… We don’t win the AI age by concentrating compute. We win by distributing it. And that’s the American advantage.” — 00:15:43

“AGI — the ability to reason, plan, and learn broadly — could become the most important invention of the next 500 years. But it’s built on closed infrastructure. We risk bottlenecks, capture and fragility. We don’t just need open models. We need open infrastructure beneath them. We must decentralize the foundation before it ossifies.” — 00:02:12

“Nous pushed scale even further — a 405 billion parameter model trained in the wild, fully open source, fully decentralized. This isn’t the future. It’s the present. Together, these breakthroughs prove… we can train powerful models without centralized servers. We can verify correctness without trust. And we can scale intelligence without monopolies.” — 00:07:17

“We built AkashML, a distributed AI runtime designed to unlock real performance from globally scattered resources. It’s GPU native, it’s container first, and it’s lightning fast… We support inference today, fine-tuning tomorrow, and fully training soon after. Whether you’re running a star node in Austin or a micro grid in Nairobi, the experience is seamless.” — 00:00:02

“Unlike other platforms that may remove unprofitable models without warning, AkashML surfaces real-time costs. If a model becomes expensive to maintain, users can choose to either attract more usage or shift to self-hosting. We empower communities to keep models alive… We’re not just building infrastructure — we’re restoring control to the people who create and rely on these models.” — 00:01:29

“This shift does much more than fix our energy bottleneck. It reshapes access. Developers can build independently of big tech without begging for compute. These infrastructure policies would level the field for smaller players to build and deploy advanced AI models, decentralizing opportunity itself.” — 00:04:21

“The biggest model is 72 billion parameter model. For comparison, GPT-3 was 150 billion parameter model. So we’re very, very close to GPT-3… So if you were to ask me where distributed training is, it’s about pre-ChatGPT-3. And by end of the year, it will be ChatGPT-3 level.” — 00:19:37

“Can we actually reduce the amount of energy bandwidth that’s needed between nodes?… They were able to reduce communication or improve bandwidth requirements by 875x. So from, I think, like 80 gigabytes to, like, 70 megabytes.” — 00:14:16

“Why last year there was dismissal of this? No one even thought this was possible. This year, there are five papers that are all presenting at ICML. That’s a big shift because that is acceptance by traditional AI.” — 00:22:26

“In the meantime, I think decentralized AI is the only way. I looked at every possibility. Lay it out, and there is no magic solution to get more energy in America in the next four years.” — 00:40:30

“There is a lot of work that indicates distributed training will become a reality by end of the year, and by end of the year we will produce a model as good as GPT-3. By end of next year, or maybe going into 2027, there’s a good chance that we’ll be able to produce a state-of-the-art model fully trained distributed — or decentralized rather.” — 00:21:50

“I have high conviction over the next 3 to 5 years decentralized AI has the best shot to make a mark in the world. We got the best shot right now. If we don’t make progress in the next two years, I don’t think decentralized AI will take off… People are going to use a solution because there’s no other option — that’s really the best way to sell a product.” — 01:21:05

“We all know Bitcoin is the largest supercomputer in the world. Why? Because there’s an incentive structure to contribute compute to Bitcoin. Similarly, if there’s an incentive structure to contribute your compute to train a model, there is a possibility we may actually out-compete OpenAIs of the world, because now you have the public.” — 00:21:50

“We’re like under the radar, which I really like… because I think we’re about to blow up very soon in this decentralized AI space.” — 01:10:30

“There’s a lot of work that happened in the last few years on low communication algorithms, asynchronous algorithms, fault tolerance, all coming together. I believe in the next six [months] to one year you’ll actually see a truly decentralized model, or a model that’s trained on truly decentralized networks… it’s not decentralized yet, but it will be, very very very soon.” — 00:19:08

“Continuous non-disruptive verification is key. That means without affecting the workload that’s deployed on the GPU… you got to continuously verify, and no one has solved the problem, and I think we’re close to solving the problem and doing it in fully open source and decentralized manner.” — 00:29:58

“I feel like it’s going to be a new chapter post Mainnet 14 upgrade… that is going to set a new cadence in terms of how fast we’ll be shipping features. And that made me comfortable enough to commit to a public roadmap that I published on my Twitter… Verifiable compute I think is huge… virtual machines — again, 90% of AI uses virtual machines. So I’m really excited for these two capabilities, and I think the demand will catch up.” — 00:22:16

“Akash is like that very strong blockchain but very hard to make changes. It’s great if you’re Bitcoin — no changes to the blockchain — but we’re not Bitcoin, we’re AI. AI market demands a lot of change. We wanted new incentives, we wanted the burn mechanism… we want all these new capabilities that we said we’re going to do on the roadmap, but we discovered that we can’t really do much without upgrading the blockchain.” — 00:17:12

“A concrete decentralized training milestone is Nous Research trained a 15 billion parameter LLM over the public internet on volunteer machines and donated instances in about 10 days and about 11,000 steps, matching centralized run accuracy using WAN-aware optimizers. This demonstrates that with the right comms and async strategy, frontier class models can train beyond a single hyperscaler.” — 00:13:16

“If compute access is functionally gated by energy, we risk centralization by default — only a few players can pay the bill. So here’s the constraint in one line: AI demand is exponential; our energy response can’t be linear. We need architectures that use energy smarter — spreading load, lifting utilization, and collocating compute with abundant, cheaper renewables.” — 00:04:26

“An alternative is thousands of nodes — small data centers, campuses, labs, and individual providers cooperating over the internet to train one model. That is not science fiction. We’ve seen precedent at internet scale: Folding@home volunteers once peaked around 2.4 exaflops, outpacing the top supercomputer at that time. With the right algorithms and verification, we can turn the internet itself into a capable, resilient training fabric for PyTorch jobs.” — 00:06:39

“We’re not just reducing cost, we’re expanding who gets to build — that aligns with the open source ethos that made PyTorch what it is… We can turn idleness into intelligence… Make the internet a cluster, and we make clean energy a scheduling signal.” — 00:19:10

“Training is not there yet — I wouldn’t call training vaporware, that’s really promising… decentralized inference is complete vaporware. There hasn’t been any verifiable… inference that’s actually real.” — 00:49:21

“There have been 100 compute marketplaces that came after Akash. Most of them are just not decentralized in any manner — they’re just SaaS apps. So ignore anything that’s closed source, ignore [anything that’s not on-chain]… just because something has a token doesn’t mean it’s decentralized. So all that is junk.” — 00:45:43

“A big favorite for verifiability today is trusted execution environments, where it is vendor-attested workloads, essentially attested at the chip level, and with memory encryption it’s impossible to at least see inside a workload when it is encrypted at the memory level.” — 00:17:25

“The real interesting stuff I think in AI is distributed training… There hasn’t been a platform where you go and be like, ‘Hey, I’m going to contribute my computer to this training run, I want some tokens back.’ That’s the holy grail we’re working towards. No one has quite figured that out… nothing cohesive in terms of a framework that can bring all the primitives together… and make it as easy or as interesting as DeFi was during DeFi summer.” — 00:47:08

“My take is there is no other future that’s not decentralized, because the current model is not only failing, it cannot scale. There’s really no solution to the energy problem — it takes a Manhattan project level effort… we have competition from a nation state level, which is China, that’s building a gigawatt of solar capacity every 36 hours… The only way to solve that is to figure out how to distribute workloads.” — 00:08:01

“People say crypto AI is dead. I’m like, well, no, we’re just getting started. All the junk is gone — that’s supposed to happen… But I think the next version of crypto AI is going to solve distributed training. It’s going to solve distributed training in such ways that you would never see it coming from a million miles — it’s going to hit you hard.” — 00:25:22

“We, as a distributed training landscape, are maybe at a GPT-2 level in terms of our technological progress… that’s going to take some time and some resources and incentives. My big question is not the technology — it’s what is the incentive for someone to provide the compute to train a model… and I think that’s where we’re entering the crypto AI space, and where Akash fits in — the infrastructure layer for the entire thing.” — 00:14:39

“I think distributed training will be a thing. And of course we’re not going to replace the major labs doing their own thing, but with distillation, all these techniques these days, I don’t know what kind of mo[a]t the major big labs are going to continue having.” — 03:20:06

“I feel like 6 months to a year’s time we’re going to see distributed clusters and distributed training going to be a thing because the technology is not there yet. I mean, we’re kind of there, but not really there yet.” — 00:34:20

“On the bright side, distributed training actually is taking the center stage. We saw a new model by a subnet in Bittensor called Covenant, which did a 72 billion parameter model, which is not state-of-the-art but definitely better than what Llama 70 billion parameter model was… it’s not hard to see where you have a distributed grid and that grid contributing to a larger distributed network for AI as a possible solution.” — 02:14:33

“I’ve been talking to several labs — I don’t want to name who, they’re under NDAs — everybody’s looking at distributed training as a serious potential. If we figure out an incentive structure for distributed training — that’s where crypto has a phenomenal role — I don’t see how distributed training will [not] take main stage in a couple of years. And when that happens, bet you, you want GPUs in a farmland.” — 02:15:15

“Frankly, a 70 billion parameter model can be now trained on a fully distributed network and that’s a big deal… that 70B was trained by Meta in their massive data centers. You don’t need a Meta to produce a 70 billion parameter model… I’m very excited for decentralized training to succeed where your home computers can be leveraged, because your energy cost is going to be a bigger variable than anything.” — 00:42:10

“They trained a 72 billion parameter model which is better than Llama 70B… there are a lot of models that are trained in a fully distributed manner… that I believe will achieve SOTA in like two to three years. It’s not there yet.” — 00:26:49

“I was at ICML last year. I gave a talk about training and distributed training becoming a real thing, because if we have to rely on these companies to give us intelligence, then we lost as a society. And that message seems to resonate quite well with a lot of folks in AI.” — 00:35:29

“These moratoriums, these energy challenges, regulatory challenges are going to force distributed training. So I’m extremely bullish in terms of where this is going.” — 00:43:38

“If we crack heterogeneous compute, that means you can mix and match older GPUs and newer GPUs, then you have significantly more supply available… As that becomes real, I think you’re going to see decentralized network come to light in ways that was not obvious before.” — 00:34:15

“Today, training workloads are evolving to a point that can work on distributed networks, heterogeneous networks, highly fault tolerant. We’re seeing companies like Pluralis, for example, training fairly large or usable models on fully decentralized networks.” — 00:39:02