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Deep Dive: The Decentralised AI Model Training ArenaAs the master Leonardo da Vinci once said, "Learning never exhausts the mind." But in the age of artificial intelligence, it seems learning might just exhaust our planet's supply of computational power. The AI revolution, which is on track to pour over $15.7 trillion into the global economy by 2030, is fundamentally built on two things: data and the sheer force of computation. The problem is, the scale of AI models is growing at a blistering pace, with the compute needed for training doubling roughly every five months. This has created a massive bottleneck. A small handful of giant cloud companies hold the keys to the kingdom, controlling the GPU supply and creating a system that is expensive, permissioned, and frankly, a bit fragile for something so important.
This is where the story gets interesting. We're seeing a paradigm shift, an emerging arena called Decentralized AI (DeAI) model training, which uses the core ideas of blockchain and Web3 to challenge this centralized control.
Let's look at the numbers. The market for AI training data is set to hit around $3.5 billion by 2025, growing at a clip of about 25% each year. All that data needs processing. The Blockchain AI market itself is expected to be worth nearly $681 million in 2025, growing at a healthy 23% to 28% CAGR. And if we zoom out to the bigger picture, the whole Decentralized Physical Infrastructure (DePIN) space, which DeAI is a part of, is projected to blow past $32 billion in 2025.
What this all means is that AI's hunger for data and compute is creating a huge demand. DePIN and blockchain are stepping in to provide the supply, a global, open, and economically smart network for building intelligence. We've already seen how token incentives can get people to coordinate physical hardware like wireless hotspots and storage drives; now we're applying that same playbook to the most valuable digital production process in the world: creating artificial intelligence.
I. The DeAI Stack
The push for decentralized AI stems from a deep philosophical mission to build a more open, resilient, and equitable AI ecosystem. It's about fostering innovation and resisting the concentration of power that we see today. Proponents often contrast two ways of organizing the world: a "Taxis," which is a centrally designed and controlled order, versus a "Cosmos," a decentralized, emergent order that grows from autonomous interactions.
A centralized approach to AI could create a sort of "autocomplete for life," where AI systems subtly nudge human actions and, choice by choice, wear away our ability to think for ourselves. Decentralization is the proposed antidote. It's a framework where AI is a tool to enhance human flourishing, not direct it. By spreading out control over data, models, and compute, DeAI aims to put power back into the hands of users, creators, and communities, making sure the future of intelligence is something we share, not something a few companies own.
II. Deconstructing the DeAI Stack
At its heart, you can break AI down into three basic pieces: data, compute, and algorithms. The DeAI movement is all about rebuilding each of these pillars on a decentralized foundation.
❍ Pillar 1: Decentralized Data
The fuel for any powerful AI is a massive and varied dataset. In the old model, this data gets locked away in centralized systems like Amazon Web Services or Google Cloud. This creates single points of failure, censorship risks, and makes it hard for newcomers to get access. Decentralized storage networks provide an alternative, offering a permanent, censorship-resistant, and verifiable home for AI training data.
Projects like Filecoin and Arweave are key players here. Filecoin uses a global network of storage providers, incentivizing them with tokens to reliably store data. It uses clever cryptographic proofs like Proof-of-Replication and Proof-of-Spacetime to make sure the data is safe and available. Arweave has a different take: you pay once, and your data is stored forever on an immutable "permaweb". By turning data into a public good, these networks create a solid, transparent foundation for AI development, ensuring the datasets used for training are secure and open to everyone.
❍ Pillar 2: Decentralized Compute
The biggest setback in AI right now is getting access to high-performance compute, especially GPUs. DeAI tackles this head-on by creating protocols that can gather and coordinate compute power from all over the world, from consumer-grade GPUs in people's homes to idle machines in data centers. This turns computational power from a scarce resource you rent from a few gatekeepers into a liquid, global commodity. Projects like Prime Intellect, Gensyn, and Nous Research are building the marketplaces for this new compute economy.
❍ Pillar 3: Decentralized Algorithms & Models
Getting the data and compute is one thing. The real work is in coordinating the process of training, making sure the work is done correctly, and getting everyone to collaborate in an environment where you can't necessarily trust anyone. This is where a mix of Web3 technologies comes together to form the operational core of DeAI.
Blockchain & Smart Contracts: Think of these as the unchangeable and transparent rulebook. Blockchains provide a shared ledger to track who did what, and smart contracts automatically enforce the rules and hand out rewards, so you don't need a middleman.Federated Learning: This is a key privacy-preserving technique. It lets AI models train on data scattered across different locations without the data ever having to move. Only the model updates get shared, not your personal information, which keeps user data private and secure.Tokenomics: This is the economic engine. Tokens create a mini-economy that rewards people for contributing valuable things, be it data, compute power, or improvements to the AI models. It gets everyone's incentives aligned toward the shared goal of building better AI.
The beauty of this stack is its modularity. An AI developer could grab a dataset from Arweave, use Gensyn's network for verifiable training, and then deploy the finished model on a specialized Bittensor subnet to make money. This interoperability turns the pieces of AI development into "intelligence legos," sparking a much more dynamic and innovative ecosystem than any single, closed platform ever could.
III. How Decentralized Model Training Works
Imagine the goal is to create a world-class AI chef. The old, centralized way is to lock one apprentice in a single, secret kitchen (like Google's) with a giant, secret cookbook. The decentralized way, using a technique called Federated Learning, is more like running a global cooking club.
The master recipe (the "global model") is sent to thousands of local chefs all over the world. Each chef tries the recipe in their own kitchen, using their unique local ingredients and methods ("local data"). They don't share their secret ingredients; they just make notes on how to improve the recipe ("model updates"). These notes are sent back to the club headquarters. The club then combines all the notes to create a new, improved master recipe, which gets sent out for the next round. The whole thing is managed by a transparent, automated club charter (the "blockchain"), which makes sure every chef who helps out gets credit and is rewarded fairly ("token rewards").
❍ Key Mechanisms
That analogy maps pretty closely to the technical workflow that allows for this kind of collaborative training. It’s a complex thing, but it boils down to a few key mechanisms that make it all possible.
Distributed Data Parallelism: This is the starting point. Instead of one giant computer crunching one massive dataset, the dataset is broken up into smaller pieces and distributed across many different computers (nodes) in the network. Each of these nodes gets a complete copy of the AI model to work with. This allows for a huge amount of parallel processing, dramatically speeding things up. Each node trains its model replica on its unique slice of data.Low-Communication Algorithms: A major challenge is keeping all those model replicas in sync without clogging the internet. If every node had to constantly broadcast every tiny update to every other node, it would be incredibly slow and inefficient. This is where low-communication algorithms come in. Techniques like DiLoCo (Distributed Low-Communication) allow nodes to perform hundreds of local training steps on their own before needing to synchronize their progress with the wider network. Newer methods like NoLoCo (No-all-reduce Low-Communication) go even further, replacing massive group synchronizations with a "gossip" method where nodes just periodically average their updates with a single, randomly chosen peer.Compression: To further reduce the communication burden, networks use compression techniques. This is like zipping a file before you email it. Model updates, which are just big lists of numbers, can be compressed to make them smaller and faster to send. Quantization, for example, reduces the precision of these numbers (say, from a 32-bit float to an 8-bit integer), which can shrink the data size by a factor of four or more with minimal impact on accuracy. Pruning is another method that removes unimportant connections within the model, making it smaller and more efficient.Incentive and Validation: In a trustless network, you need to make sure everyone plays fair and gets rewarded for their work. This is the job of the blockchain and its token economy. Smart contracts act as automated escrow, holding and distributing token rewards to participants who contribute useful compute or data. To prevent cheating, networks use validation mechanisms. This can involve validators randomly re-running a small piece of a node's computation to verify its correctness or using cryptographic proofs to ensure the integrity of the results. This creates a system of "Proof-of-Intelligence" where valuable contributions are verifiably rewarded.Fault Tolerance: Decentralized networks are made up of unreliable, globally distributed computers. Nodes can drop offline at any moment. The system needs to be ableto handle this without the whole training process crashing. This is where fault tolerance comes in. Frameworks like Prime Intellect's ElasticDeviceMesh allow nodes to dynamically join or leave a training run without causing a system-wide failure. Techniques like asynchronous checkpointing regularly save the model's progress, so if a node fails, the network can quickly recover from the last saved state instead of starting from scratch.
This continuous, iterative workflow fundamentally changes what an AI model is. It's no longer a static object created and owned by one company. It becomes a living system, a consensus state that is constantly being refined by a global collective. The model isn't a product; it's a protocol, collectively maintained and secured by its network.
IV. Decentralized Training Protocols
The theoretical framework of decentralized AI is now being implemented by a growing number of innovative projects, each with a unique strategy and technical approach. These protocols create a competitive arena where different models of collaboration, verification, and incentivization are being tested at scale.
❍ The Modular Marketplace: Bittensor's Subnet Ecosystem
Bittensor operates as an "internet of digital commodities," a meta-protocol hosting numerous specialized "subnets." Each subnet is a competitive, incentive-driven market for a specific AI task, from text generation to protein folding. Within this ecosystem, two subnets are particularly relevant to decentralized training.
Templar (Subnet 3) is focused on creating a permissionless and antifragile platform for decentralized pre-training. It embodies a pure, competitive approach where miners train models (currently up to 8 billion parameters, with a roadmap toward 70 billion) and are rewarded based on performance, driving a relentless race to produce the best possible intelligence.
Macrocosmos (Subnet 9) represents a significant evolution with its IOTA (Incentivised Orchestrated Training Architecture). IOTA moves beyond isolated competition toward orchestrated collaboration. It employs a hub-and-spoke architecture where an Orchestrator coordinates data- and pipeline-parallel training across a network of miners. Instead of each miner training an entire model, they are assigned specific layers of a much larger model. This division of labor allows the collective to train models at a scale far beyond the capacity of any single participant. Validators perform "shadow audits" to verify work, and a granular incentive system rewards contributions fairly, fostering a collaborative yet accountable environment.
❍ The Verifiable Compute Layer: Gensyn's Trustless Network
Gensyn's primary focus is on solving one of the hardest problems in the space: verifiable machine learning. Its protocol, built as a custom Ethereum L2 Rollup, is designed to provide cryptographic proof of correctness for deep learning computations performed on untrusted nodes.
A key innovation from Gensyn's research is NoLoCo (No-all-reduce Low-Communication), a novel optimization method for distributed training. Traditional methods require a global "all-reduce" synchronization step, which creates a bottleneck, especially on low-bandwidth networks. NoLoCo eliminates this step entirely. Instead, it uses a gossip-based protocol where nodes periodically average their model weights with a single, randomly selected peer. This, combined with a modified Nesterov momentum optimizer and random routing of activations, allows the network to converge efficiently without global synchronization, making it ideal for training over heterogeneous, internet-connected hardware. Gensyn's RL Swarm testnet application demonstrates this stack in action, enabling collaborative reinforcement learning in a decentralized setting.
❍ The Global Compute Aggregator: Prime Intellect's Open Framework
Prime Intellect is building a peer-to-peer protocol to aggregate global compute resources into a unified marketplace, effectively creating an "Airbnb for compute". Their PRIME framework is engineered for fault-tolerant, high-performance training on a network of unreliable and globally distributed workers.
The framework is built on an adapted version of the DiLoCo (Distributed Low-Communication) algorithm, which allows nodes to perform many local training steps before requiring a less frequent global synchronization. Prime Intellect has augmented this with significant engineering breakthroughs. The ElasticDeviceMesh allows nodes to dynamically join or leave a training run without crashing the system. Asynchronous checkpointing to RAM-backed filesystems minimizes downtime. Finally, they developed custom int8 all-reduce kernels, which reduce the communication payload during synchronization by a factor of four, drastically lowering bandwidth requirements. This robust technical stack enabled them to successfully orchestrate the world's first decentralized training of a 10-billion-parameter model, INTELLECT-1.
❍ The Open-Source Collective: Nous Research's Community-Driven Approach
Nous Research operates as a decentralized AI research collective with a strong open-source ethos, building its infrastructure on the Solana blockchain for its high throughput and low transaction costs.
Their flagship platform, Nous Psyche, is a decentralized training network powered by two core technologies: DisTrO (Distributed Training Over-the-Internet) and its underlying optimization algorithm, DeMo (Decoupled Momentum Optimization). Developed in collaboration with an OpenAI co-founder, these technologies are designed for extreme bandwidth efficiency, claiming a reduction of 1,000x to 10,000x compared to conventional methods. This breakthrough makes it feasible to participate in large-scale model training using consumer-grade GPUs and standard internet connections, radically democratizing access to AI development.
❍ The Pluralistic Future: Pluralis AI's Protocol Learning
Pluralis AI is tackling a higher-level challenge: not just how to train models, but how to align them with diverse and pluralistic human values in a privacy-preserving manner.
Their PluralLLM framework introduces a federated learning-based approach to preference alignment, a task traditionally handled by centralized methods like Reinforcement Learning from Human Feedback (RLHF). With PluralLLM, different user groups can collaboratively train a preference predictor model without ever sharing their sensitive, underlying preference data. The framework uses Federated Averaging to aggregate these preference updates, achieving faster convergence and better alignment scores than centralized methods while preserving both privacy and fairness.
Their overarching concept of Protocol Learning further ensures that no single participant can obtain the complete model, solving critical intellectual property and trust issues inherent in collaborative AI development.
While the decentralized AI training arena holds a promising Future, its path to mainstream adoption is filled with significant challenges. The technical complexity of managing and synchronizing computations across thousands of unreliable nodes remains a formidable engineering hurdle. Furthermore, the lack of clear legal and regulatory frameworks for decentralized autonomous systems and collectively owned intellectual property creates uncertainty for developers and investors alike.
Ultimately, for these networks to achieve long-term viability, they must evolve beyond speculation and attract real, paying customers for their computational services, thereby generating sustainable, protocol-driven revenue. And we believe they'll eventually cross the road even before our speculation.
Deep Dive: The Decentralised AI Model Training Arena
As the master Leonardo da Vinci once said, "Learning never exhausts the mind." But in the age of artificial intelligence, it seems learning might just exhaust our planet's supply of computational power. The AI revolution, which is on track to pour over $15.7 trillion into the global economy by 2030, is fundamentally built on two things: data and the sheer force of computation. The problem is, the scale of AI models is growing at a blistering pace, with the compute needed for training doubling roughly every five months. This has created a massive bottleneck. A small handful of giant cloud companies hold the keys to the kingdom, controlling the GPU supply and creating a system that is expensive, permissioned, and frankly, a bit fragile for something so important.
This is where the story gets interesting. We're seeing a paradigm shift, an emerging arena called Decentralized AI (DeAI) model training, which uses the core ideas of blockchain and Web3 to challenge this centralized control.
Let's look at the numbers. The market for AI training data is set to hit around $3.5 billion by 2025, growing at a clip of about 25% each year. All that data needs processing. The Blockchain AI market itself is expected to be worth nearly $681 million in 2025, growing at a healthy 23% to 28% CAGR. And if we zoom out to the bigger picture, the whole Decentralized Physical Infrastructure (DePIN) space, which DeAI is a part of, is projected to blow past $32 billion in 2025.
What this all means is that AI's hunger for data and compute is creating a huge demand. DePIN and blockchain are stepping in to provide the supply, a global, open, and economically smart network for building intelligence. We've already seen how token incentives can get people to coordinate physical hardware like wireless hotspots and storage drives; now we're applying that same playbook to the most valuable digital production process in the world: creating artificial intelligence.
I. The DeAI Stack
The push for decentralized AI stems from a deep philosophical mission to build a more open, resilient, and equitable AI ecosystem. It's about fostering innovation and resisting the concentration of power that we see today. Proponents often contrast two ways of organizing the world: a "Taxis," which is a centrally designed and controlled order, versus a "Cosmos," a decentralized, emergent order that grows from autonomous interactions.
A centralized approach to AI could create a sort of "autocomplete for life," where AI systems subtly nudge human actions and, choice by choice, wear away our ability to think for ourselves. Decentralization is the proposed antidote. It's a framework where AI is a tool to enhance human flourishing, not direct it. By spreading out control over data, models, and compute, DeAI aims to put power back into the hands of users, creators, and communities, making sure the future of intelligence is something we share, not something a few companies own.
II. Deconstructing the DeAI Stack
At its heart, you can break AI down into three basic pieces: data, compute, and algorithms. The DeAI movement is all about rebuilding each of these pillars on a decentralized foundation.
❍ Pillar 1: Decentralized Data
The fuel for any powerful AI is a massive and varied dataset. In the old model, this data gets locked away in centralized systems like Amazon Web Services or Google Cloud. This creates single points of failure, censorship risks, and makes it hard for newcomers to get access. Decentralized storage networks provide an alternative, offering a permanent, censorship-resistant, and verifiable home for AI training data.
Projects like Filecoin and Arweave are key players here. Filecoin uses a global network of storage providers, incentivizing them with tokens to reliably store data. It uses clever cryptographic proofs like Proof-of-Replication and Proof-of-Spacetime to make sure the data is safe and available. Arweave has a different take: you pay once, and your data is stored forever on an immutable "permaweb". By turning data into a public good, these networks create a solid, transparent foundation for AI development, ensuring the datasets used for training are secure and open to everyone.
❍ Pillar 2: Decentralized Compute
The biggest setback in AI right now is getting access to high-performance compute, especially GPUs. DeAI tackles this head-on by creating protocols that can gather and coordinate compute power from all over the world, from consumer-grade GPUs in people's homes to idle machines in data centers. This turns computational power from a scarce resource you rent from a few gatekeepers into a liquid, global commodity. Projects like Prime Intellect, Gensyn, and Nous Research are building the marketplaces for this new compute economy.
❍ Pillar 3: Decentralized Algorithms & Models
Getting the data and compute is one thing. The real work is in coordinating the process of training, making sure the work is done correctly, and getting everyone to collaborate in an environment where you can't necessarily trust anyone. This is where a mix of Web3 technologies comes together to form the operational core of DeAI.
Blockchain & Smart Contracts: Think of these as the unchangeable and transparent rulebook. Blockchains provide a shared ledger to track who did what, and smart contracts automatically enforce the rules and hand out rewards, so you don't need a middleman.Federated Learning: This is a key privacy-preserving technique. It lets AI models train on data scattered across different locations without the data ever having to move. Only the model updates get shared, not your personal information, which keeps user data private and secure.Tokenomics: This is the economic engine. Tokens create a mini-economy that rewards people for contributing valuable things, be it data, compute power, or improvements to the AI models. It gets everyone's incentives aligned toward the shared goal of building better AI.
The beauty of this stack is its modularity. An AI developer could grab a dataset from Arweave, use Gensyn's network for verifiable training, and then deploy the finished model on a specialized Bittensor subnet to make money. This interoperability turns the pieces of AI development into "intelligence legos," sparking a much more dynamic and innovative ecosystem than any single, closed platform ever could.
III. How Decentralized Model Training Works
Imagine the goal is to create a world-class AI chef. The old, centralized way is to lock one apprentice in a single, secret kitchen (like Google's) with a giant, secret cookbook. The decentralized way, using a technique called Federated Learning, is more like running a global cooking club.
The master recipe (the "global model") is sent to thousands of local chefs all over the world. Each chef tries the recipe in their own kitchen, using their unique local ingredients and methods ("local data"). They don't share their secret ingredients; they just make notes on how to improve the recipe ("model updates"). These notes are sent back to the club headquarters. The club then combines all the notes to create a new, improved master recipe, which gets sent out for the next round. The whole thing is managed by a transparent, automated club charter (the "blockchain"), which makes sure every chef who helps out gets credit and is rewarded fairly ("token rewards").
❍ Key Mechanisms
That analogy maps pretty closely to the technical workflow that allows for this kind of collaborative training. It’s a complex thing, but it boils down to a few key mechanisms that make it all possible.
Distributed Data Parallelism: This is the starting point. Instead of one giant computer crunching one massive dataset, the dataset is broken up into smaller pieces and distributed across many different computers (nodes) in the network. Each of these nodes gets a complete copy of the AI model to work with. This allows for a huge amount of parallel processing, dramatically speeding things up. Each node trains its model replica on its unique slice of data.Low-Communication Algorithms: A major challenge is keeping all those model replicas in sync without clogging the internet. If every node had to constantly broadcast every tiny update to every other node, it would be incredibly slow and inefficient. This is where low-communication algorithms come in. Techniques like DiLoCo (Distributed Low-Communication) allow nodes to perform hundreds of local training steps on their own before needing to synchronize their progress with the wider network. Newer methods like NoLoCo (No-all-reduce Low-Communication) go even further, replacing massive group synchronizations with a "gossip" method where nodes just periodically average their updates with a single, randomly chosen peer.Compression: To further reduce the communication burden, networks use compression techniques. This is like zipping a file before you email it. Model updates, which are just big lists of numbers, can be compressed to make them smaller and faster to send. Quantization, for example, reduces the precision of these numbers (say, from a 32-bit float to an 8-bit integer), which can shrink the data size by a factor of four or more with minimal impact on accuracy. Pruning is another method that removes unimportant connections within the model, making it smaller and more efficient.Incentive and Validation: In a trustless network, you need to make sure everyone plays fair and gets rewarded for their work. This is the job of the blockchain and its token economy. Smart contracts act as automated escrow, holding and distributing token rewards to participants who contribute useful compute or data. To prevent cheating, networks use validation mechanisms. This can involve validators randomly re-running a small piece of a node's computation to verify its correctness or using cryptographic proofs to ensure the integrity of the results. This creates a system of "Proof-of-Intelligence" where valuable contributions are verifiably rewarded.Fault Tolerance: Decentralized networks are made up of unreliable, globally distributed computers. Nodes can drop offline at any moment. The system needs to be ableto handle this without the whole training process crashing. This is where fault tolerance comes in. Frameworks like Prime Intellect's ElasticDeviceMesh allow nodes to dynamically join or leave a training run without causing a system-wide failure. Techniques like asynchronous checkpointing regularly save the model's progress, so if a node fails, the network can quickly recover from the last saved state instead of starting from scratch.
This continuous, iterative workflow fundamentally changes what an AI model is. It's no longer a static object created and owned by one company. It becomes a living system, a consensus state that is constantly being refined by a global collective. The model isn't a product; it's a protocol, collectively maintained and secured by its network.
IV. Decentralized Training Protocols
The theoretical framework of decentralized AI is now being implemented by a growing number of innovative projects, each with a unique strategy and technical approach. These protocols create a competitive arena where different models of collaboration, verification, and incentivization are being tested at scale.
❍ The Modular Marketplace: Bittensor's Subnet Ecosystem
Bittensor operates as an "internet of digital commodities," a meta-protocol hosting numerous specialized "subnets." Each subnet is a competitive, incentive-driven market for a specific AI task, from text generation to protein folding. Within this ecosystem, two subnets are particularly relevant to decentralized training.
Templar (Subnet 3) is focused on creating a permissionless and antifragile platform for decentralized pre-training. It embodies a pure, competitive approach where miners train models (currently up to 8 billion parameters, with a roadmap toward 70 billion) and are rewarded based on performance, driving a relentless race to produce the best possible intelligence.
Macrocosmos (Subnet 9) represents a significant evolution with its IOTA (Incentivised Orchestrated Training Architecture). IOTA moves beyond isolated competition toward orchestrated collaboration. It employs a hub-and-spoke architecture where an Orchestrator coordinates data- and pipeline-parallel training across a network of miners. Instead of each miner training an entire model, they are assigned specific layers of a much larger model. This division of labor allows the collective to train models at a scale far beyond the capacity of any single participant. Validators perform "shadow audits" to verify work, and a granular incentive system rewards contributions fairly, fostering a collaborative yet accountable environment.
❍ The Verifiable Compute Layer: Gensyn's Trustless Network
Gensyn's primary focus is on solving one of the hardest problems in the space: verifiable machine learning. Its protocol, built as a custom Ethereum L2 Rollup, is designed to provide cryptographic proof of correctness for deep learning computations performed on untrusted nodes.
A key innovation from Gensyn's research is NoLoCo (No-all-reduce Low-Communication), a novel optimization method for distributed training. Traditional methods require a global "all-reduce" synchronization step, which creates a bottleneck, especially on low-bandwidth networks. NoLoCo eliminates this step entirely. Instead, it uses a gossip-based protocol where nodes periodically average their model weights with a single, randomly selected peer. This, combined with a modified Nesterov momentum optimizer and random routing of activations, allows the network to converge efficiently without global synchronization, making it ideal for training over heterogeneous, internet-connected hardware. Gensyn's RL Swarm testnet application demonstrates this stack in action, enabling collaborative reinforcement learning in a decentralized setting.
❍ The Global Compute Aggregator: Prime Intellect's Open Framework
Prime Intellect is building a peer-to-peer protocol to aggregate global compute resources into a unified marketplace, effectively creating an "Airbnb for compute". Their PRIME framework is engineered for fault-tolerant, high-performance training on a network of unreliable and globally distributed workers.
The framework is built on an adapted version of the DiLoCo (Distributed Low-Communication) algorithm, which allows nodes to perform many local training steps before requiring a less frequent global synchronization. Prime Intellect has augmented this with significant engineering breakthroughs. The ElasticDeviceMesh allows nodes to dynamically join or leave a training run without crashing the system. Asynchronous checkpointing to RAM-backed filesystems minimizes downtime. Finally, they developed custom int8 all-reduce kernels, which reduce the communication payload during synchronization by a factor of four, drastically lowering bandwidth requirements. This robust technical stack enabled them to successfully orchestrate the world's first decentralized training of a 10-billion-parameter model, INTELLECT-1.
❍ The Open-Source Collective: Nous Research's Community-Driven Approach
Nous Research operates as a decentralized AI research collective with a strong open-source ethos, building its infrastructure on the Solana blockchain for its high throughput and low transaction costs.
Their flagship platform, Nous Psyche, is a decentralized training network powered by two core technologies: DisTrO (Distributed Training Over-the-Internet) and its underlying optimization algorithm, DeMo (Decoupled Momentum Optimization). Developed in collaboration with an OpenAI co-founder, these technologies are designed for extreme bandwidth efficiency, claiming a reduction of 1,000x to 10,000x compared to conventional methods. This breakthrough makes it feasible to participate in large-scale model training using consumer-grade GPUs and standard internet connections, radically democratizing access to AI development.
❍ The Pluralistic Future: Pluralis AI's Protocol Learning
Pluralis AI is tackling a higher-level challenge: not just how to train models, but how to align them with diverse and pluralistic human values in a privacy-preserving manner.
Their PluralLLM framework introduces a federated learning-based approach to preference alignment, a task traditionally handled by centralized methods like Reinforcement Learning from Human Feedback (RLHF). With PluralLLM, different user groups can collaboratively train a preference predictor model without ever sharing their sensitive, underlying preference data. The framework uses Federated Averaging to aggregate these preference updates, achieving faster convergence and better alignment scores than centralized methods while preserving both privacy and fairness.
Their overarching concept of Protocol Learning further ensures that no single participant can obtain the complete model, solving critical intellectual property and trust issues inherent in collaborative AI development.
While the decentralized AI training arena holds a promising Future, its path to mainstream adoption is filled with significant challenges. The technical complexity of managing and synchronizing computations across thousands of unreliable nodes remains a formidable engineering hurdle. Furthermore, the lack of clear legal and regulatory frameworks for decentralized autonomous systems and collectively owned intellectual property creates uncertainty for developers and investors alike.
Ultimately, for these networks to achieve long-term viability, they must evolve beyond speculation and attract real, paying customers for their computational services, thereby generating sustainable, protocol-driven revenue. And we believe they'll eventually cross the road even before our speculation.
This is where the story gets interesting. We're seeing a paradigm shift, an emerging arena called Decentralized AI (DeAI) model training, which uses the core ideas of blockchain and Web3 to challenge this centralized control.
Let's look at the numbers. The market for AI training data is set to hit around $3.5 billion by 2025, growing at a clip of about 25% each year. All that data needs processing. The Blockchain AI market itself is expected to be worth nearly $681 million in 2025, growing at a healthy 23% to 28% CAGR. And if we zoom out to the bigger picture, the whole Decentralized Physical Infrastructure (DePIN) space, which DeAI is a part of, is projected to blow past $32 billion in 2025.
What this all means is that AI's hunger for data and compute is creating a huge demand. DePIN and blockchain are stepping in to provide the supply, a global, open, and economically smart network for building intelligence. We've already seen how token incentives can get people to coordinate physical hardware like wireless hotspots and storage drives; now we're applying that same playbook to the most valuable digital production process in the world: creating artificial intelligence.
I. The DeAI Stack
The push for decentralized AI stems from a deep philosophical mission to build a more open, resilient, and equitable AI ecosystem. It's about fostering innovation and resisting the concentration of power that we see today. Proponents often contrast two ways of organizing the world: a "Taxis," which is a centrally designed and controlled order, versus a "Cosmos," a decentralized, emergent order that grows from autonomous interactions.
A centralized approach to AI could create a sort of "autocomplete for life," where AI systems subtly nudge human actions and, choice by choice, wear away our ability to think for ourselves. Decentralization is the proposed antidote. It's a framework where AI is a tool to enhance human flourishing, not direct it. By spreading out control over data, models, and compute, DeAI aims to put power back into the hands of users, creators, and communities, making sure the future of intelligence is something we share, not something a few companies own.
II. Deconstructing the DeAI Stack
At its heart, you can break AI down into three basic pieces: data, compute, and algorithms. The DeAI movement is all about rebuilding each of these pillars on a decentralized foundation.
❍ Pillar 1: Decentralized Data
The fuel for any powerful AI is a massive and varied dataset. In the old model, this data gets locked away in centralized systems like Amazon Web Services or Google Cloud. This creates single points of failure, censorship risks, and makes it hard for newcomers to get access. Decentralized storage networks provide an alternative, offering a permanent, censorship-resistant, and verifiable home for AI training data.
Projects like Filecoin and Arweave are key players here. Filecoin uses a global network of storage providers, incentivizing them with tokens to reliably store data. It uses clever cryptographic proofs like Proof-of-Replication and Proof-of-Spacetime to make sure the data is safe and available. Arweave has a different take: you pay once, and your data is stored forever on an immutable "permaweb". By turning data into a public good, these networks create a solid, transparent foundation for AI development, ensuring the datasets used for training are secure and open to everyone.
❍ Pillar 2: Decentralized Compute
The biggest setback in AI right now is getting access to high-performance compute, especially GPUs. DeAI tackles this head-on by creating protocols that can gather and coordinate compute power from all over the world, from consumer-grade GPUs in people's homes to idle machines in data centers. This turns computational power from a scarce resource you rent from a few gatekeepers into a liquid, global commodity. Projects like Prime Intellect, Gensyn, and Nous Research are building the marketplaces for this new compute economy.
❍ Pillar 3: Decentralized Algorithms & Models
Getting the data and compute is one thing. The real work is in coordinating the process of training, making sure the work is done correctly, and getting everyone to collaborate in an environment where you can't necessarily trust anyone. This is where a mix of Web3 technologies comes together to form the operational core of DeAI.
Blockchain & Smart Contracts: Think of these as the unchangeable and transparent rulebook. Blockchains provide a shared ledger to track who did what, and smart contracts automatically enforce the rules and hand out rewards, so you don't need a middleman.Federated Learning: This is a key privacy-preserving technique. It lets AI models train on data scattered across different locations without the data ever having to move. Only the model updates get shared, not your personal information, which keeps user data private and secure.Tokenomics: This is the economic engine. Tokens create a mini-economy that rewards people for contributing valuable things, be it data, compute power, or improvements to the AI models. It gets everyone's incentives aligned toward the shared goal of building better AI.
The beauty of this stack is its modularity. An AI developer could grab a dataset from Arweave, use Gensyn's network for verifiable training, and then deploy the finished model on a specialized Bittensor subnet to make money. This interoperability turns the pieces of AI development into "intelligence legos," sparking a much more dynamic and innovative ecosystem than any single, closed platform ever could.
III. How Decentralized Model Training Works
Imagine the goal is to create a world-class AI chef. The old, centralized way is to lock one apprentice in a single, secret kitchen (like Google's) with a giant, secret cookbook. The decentralized way, using a technique called Federated Learning, is more like running a global cooking club.
The master recipe (the "global model") is sent to thousands of local chefs all over the world. Each chef tries the recipe in their own kitchen, using their unique local ingredients and methods ("local data"). They don't share their secret ingredients; they just make notes on how to improve the recipe ("model updates"). These notes are sent back to the club headquarters. The club then combines all the notes to create a new, improved master recipe, which gets sent out for the next round. The whole thing is managed by a transparent, automated club charter (the "blockchain"), which makes sure every chef who helps out gets credit and is rewarded fairly ("token rewards").
❍ Key Mechanisms
That analogy maps pretty closely to the technical workflow that allows for this kind of collaborative training. It’s a complex thing, but it boils down to a few key mechanisms that make it all possible.
Distributed Data Parallelism: This is the starting point. Instead of one giant computer crunching one massive dataset, the dataset is broken up into smaller pieces and distributed across many different computers (nodes) in the network. Each of these nodes gets a complete copy of the AI model to work with. This allows for a huge amount of parallel processing, dramatically speeding things up. Each node trains its model replica on its unique slice of data.Low-Communication Algorithms: A major challenge is keeping all those model replicas in sync without clogging the internet. If every node had to constantly broadcast every tiny update to every other node, it would be incredibly slow and inefficient. This is where low-communication algorithms come in. Techniques like DiLoCo (Distributed Low-Communication) allow nodes to perform hundreds of local training steps on their own before needing to synchronize their progress with the wider network. Newer methods like NoLoCo (No-all-reduce Low-Communication) go even further, replacing massive group synchronizations with a "gossip" method where nodes just periodically average their updates with a single, randomly chosen peer.Compression: To further reduce the communication burden, networks use compression techniques. This is like zipping a file before you email it. Model updates, which are just big lists of numbers, can be compressed to make them smaller and faster to send. Quantization, for example, reduces the precision of these numbers (say, from a 32-bit float to an 8-bit integer), which can shrink the data size by a factor of four or more with minimal impact on accuracy. Pruning is another method that removes unimportant connections within the model, making it smaller and more efficient.Incentive and Validation: In a trustless network, you need to make sure everyone plays fair and gets rewarded for their work. This is the job of the blockchain and its token economy. Smart contracts act as automated escrow, holding and distributing token rewards to participants who contribute useful compute or data. To prevent cheating, networks use validation mechanisms. This can involve validators randomly re-running a small piece of a node's computation to verify its correctness or using cryptographic proofs to ensure the integrity of the results. This creates a system of "Proof-of-Intelligence" where valuable contributions are verifiably rewarded.Fault Tolerance: Decentralized networks are made up of unreliable, globally distributed computers. Nodes can drop offline at any moment. The system needs to be ableto handle this without the whole training process crashing. This is where fault tolerance comes in. Frameworks like Prime Intellect's ElasticDeviceMesh allow nodes to dynamically join or leave a training run without causing a system-wide failure. Techniques like asynchronous checkpointing regularly save the model's progress, so if a node fails, the network can quickly recover from the last saved state instead of starting from scratch.
This continuous, iterative workflow fundamentally changes what an AI model is. It's no longer a static object created and owned by one company. It becomes a living system, a consensus state that is constantly being refined by a global collective. The model isn't a product; it's a protocol, collectively maintained and secured by its network.
IV. Decentralized Training Protocols
The theoretical framework of decentralized AI is now being implemented by a growing number of innovative projects, each with a unique strategy and technical approach. These protocols create a competitive arena where different models of collaboration, verification, and incentivization are being tested at scale.
❍ The Modular Marketplace: Bittensor's Subnet Ecosystem
Bittensor operates as an "internet of digital commodities," a meta-protocol hosting numerous specialized "subnets." Each subnet is a competitive, incentive-driven market for a specific AI task, from text generation to protein folding. Within this ecosystem, two subnets are particularly relevant to decentralized training.
Templar (Subnet 3) is focused on creating a permissionless and antifragile platform for decentralized pre-training. It embodies a pure, competitive approach where miners train models (currently up to 8 billion parameters, with a roadmap toward 70 billion) and are rewarded based on performance, driving a relentless race to produce the best possible intelligence.
Macrocosmos (Subnet 9) represents a significant evolution with its IOTA (Incentivised Orchestrated Training Architecture). IOTA moves beyond isolated competition toward orchestrated collaboration. It employs a hub-and-spoke architecture where an Orchestrator coordinates data- and pipeline-parallel training across a network of miners. Instead of each miner training an entire model, they are assigned specific layers of a much larger model. This division of labor allows the collective to train models at a scale far beyond the capacity of any single participant. Validators perform "shadow audits" to verify work, and a granular incentive system rewards contributions fairly, fostering a collaborative yet accountable environment.
❍ The Verifiable Compute Layer: Gensyn's Trustless Network
Gensyn's primary focus is on solving one of the hardest problems in the space: verifiable machine learning. Its protocol, built as a custom Ethereum L2 Rollup, is designed to provide cryptographic proof of correctness for deep learning computations performed on untrusted nodes.
A key innovation from Gensyn's research is NoLoCo (No-all-reduce Low-Communication), a novel optimization method for distributed training. Traditional methods require a global "all-reduce" synchronization step, which creates a bottleneck, especially on low-bandwidth networks. NoLoCo eliminates this step entirely. Instead, it uses a gossip-based protocol where nodes periodically average their model weights with a single, randomly selected peer. This, combined with a modified Nesterov momentum optimizer and random routing of activations, allows the network to converge efficiently without global synchronization, making it ideal for training over heterogeneous, internet-connected hardware. Gensyn's RL Swarm testnet application demonstrates this stack in action, enabling collaborative reinforcement learning in a decentralized setting.
❍ The Global Compute Aggregator: Prime Intellect's Open Framework
Prime Intellect is building a peer-to-peer protocol to aggregate global compute resources into a unified marketplace, effectively creating an "Airbnb for compute". Their PRIME framework is engineered for fault-tolerant, high-performance training on a network of unreliable and globally distributed workers.
The framework is built on an adapted version of the DiLoCo (Distributed Low-Communication) algorithm, which allows nodes to perform many local training steps before requiring a less frequent global synchronization. Prime Intellect has augmented this with significant engineering breakthroughs. The ElasticDeviceMesh allows nodes to dynamically join or leave a training run without crashing the system. Asynchronous checkpointing to RAM-backed filesystems minimizes downtime. Finally, they developed custom int8 all-reduce kernels, which reduce the communication payload during synchronization by a factor of four, drastically lowering bandwidth requirements. This robust technical stack enabled them to successfully orchestrate the world's first decentralized training of a 10-billion-parameter model, INTELLECT-1.
❍ The Open-Source Collective: Nous Research's Community-Driven Approach
Nous Research operates as a decentralized AI research collective with a strong open-source ethos, building its infrastructure on the Solana blockchain for its high throughput and low transaction costs.
Their flagship platform, Nous Psyche, is a decentralized training network powered by two core technologies: DisTrO (Distributed Training Over-the-Internet) and its underlying optimization algorithm, DeMo (Decoupled Momentum Optimization). Developed in collaboration with an OpenAI co-founder, these technologies are designed for extreme bandwidth efficiency, claiming a reduction of 1,000x to 10,000x compared to conventional methods. This breakthrough makes it feasible to participate in large-scale model training using consumer-grade GPUs and standard internet connections, radically democratizing access to AI development.
❍ The Pluralistic Future: Pluralis AI's Protocol Learning
Pluralis AI is tackling a higher-level challenge: not just how to train models, but how to align them with diverse and pluralistic human values in a privacy-preserving manner.
Their PluralLLM framework introduces a federated learning-based approach to preference alignment, a task traditionally handled by centralized methods like Reinforcement Learning from Human Feedback (RLHF). With PluralLLM, different user groups can collaboratively train a preference predictor model without ever sharing their sensitive, underlying preference data. The framework uses Federated Averaging to aggregate these preference updates, achieving faster convergence and better alignment scores than centralized methods while preserving both privacy and fairness.
Their overarching concept of Protocol Learning further ensures that no single participant can obtain the complete model, solving critical intellectual property and trust issues inherent in collaborative AI development.
While the decentralized AI training arena holds a promising Future, its path to mainstream adoption is filled with significant challenges. The technical complexity of managing and synchronizing computations across thousands of unreliable nodes remains a formidable engineering hurdle. Furthermore, the lack of clear legal and regulatory frameworks for decentralized autonomous systems and collectively owned intellectual property creates uncertainty for developers and investors alike.
Ultimately, for these networks to achieve long-term viability, they must evolve beyond speculation and attract real, paying customers for their computational services, thereby generating sustainable, protocol-driven revenue. And we believe they'll eventually cross the road even before our speculation.
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The Decentralized AI landscape Artificial intelligence (AI) has become a common term in everydays lingo, while blockchain, though often seen as distinct, is gaining prominence in the tech world, especially within the Finance space. Concepts like "AI Blockchain," "AI Crypto," and similar terms highlight the convergence of these two powerful technologies. Though distinct, AI and blockchain are increasingly being combined to drive innovation, complexity, and transformation across various industries.
The integration of AI and blockchain is creating a multi-layered ecosystem with the potential to revolutionize industries, enhance security, and improve efficiencies. Though both are different and polar opposite of each other. But, De-Centralisation of Artificial intelligence quite the right thing towards giving the authority to the people.
The Whole Decentralized AI ecosystem can be understood by breaking it down into three primary layers: the Application Layer, the Middleware Layer, and the Infrastructure Layer. Each of these layers consists of sub-layers that work together to enable the seamless creation and deployment of AI within blockchain frameworks. Let's Find out How These Actually Works......
TL;DR
Application Layer: Users interact with AI-enhanced blockchain services in this layer. Examples include AI-powered finance, healthcare, education, and supply chain solutions.Middleware Layer: This layer connects applications to infrastructure. It provides services like AI training networks, oracles, and decentralized agents for seamless AI operations.Infrastructure Layer: The backbone of the ecosystem, this layer offers decentralized cloud computing, GPU rendering, and storage solutions for scalable, secure AI and blockchain operations.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
💡Application Layer
The Application Layer is the most tangible part of the ecosystem, where end-users interact with AI-enhanced blockchain services. It integrates AI with blockchain to create innovative applications, driving the evolution of user experiences across various domains.
User-Facing Applications:
AI-Driven Financial Platforms: Beyond AI Trading Bots, platforms like Numerai leverage AI to manage decentralized hedge funds. Users can contribute models to predict stock market movements, and the best-performing models are used to inform real-world trading decisions. This democratizes access to sophisticated financial strategies and leverages collective intelligence.AI-Powered Decentralized Autonomous Organizations (DAOs): DAOstack utilizes AI to optimize decision-making processes within DAOs, ensuring more efficient governance by predicting outcomes, suggesting actions, and automating routine decisions.Healthcare dApps: Doc.ai is a project that integrates AI with blockchain to offer personalized health insights. Patients can manage their health data securely, while AI analyzes patterns to provide tailored health recommendations.Education Platforms: SingularityNET and Aletheia AI have been pioneering in using AI within education by offering personalized learning experiences, where AI-driven tutors provide tailored guidance to students, enhancing learning outcomes through decentralized platforms.
Enterprise Solutions:
AI-Powered Supply Chain: Morpheus.Network utilizes AI to streamline global supply chains. By combining blockchain's transparency with AI's predictive capabilities, it enhances logistics efficiency, predicts disruptions, and automates compliance with global trade regulations. AI-Enhanced Identity Verification: Civic and uPort integrate AI with blockchain to offer advanced identity verification solutions. AI analyzes user behavior to detect fraud, while blockchain ensures that personal data remains secure and under the control of the user.Smart City Solutions: MXC Foundation leverages AI and blockchain to optimize urban infrastructure, managing everything from energy consumption to traffic flow in real-time, thereby improving efficiency and reducing operational costs.
🏵️ Middleware Layer
The Middleware Layer connects the user-facing applications with the underlying infrastructure, providing essential services that facilitate the seamless operation of AI on the blockchain. This layer ensures interoperability, scalability, and efficiency.
AI Training Networks:
Decentralized AI training networks on blockchain combine the power of artificial intelligence with the security and transparency of blockchain technology. In this model, AI training data is distributed across multiple nodes on a blockchain network, ensuring data privacy, security, and preventing data centralization.
Ocean Protocol: This protocol focuses on democratizing AI by providing a marketplace for data sharing. Data providers can monetize their datasets, and AI developers can access diverse, high-quality data for training their models, all while ensuring data privacy through blockchain.Cortex: A decentralized AI platform that allows developers to upload AI models onto the blockchain, where they can be accessed and utilized by dApps. This ensures that AI models are transparent, auditable, and tamper-proof. Bittensor: The case of a sublayer class for such an implementation can be seen with Bittensor. It's a decentralized machine learning network where participants are incentivized to put in their computational resources and datasets. This network is underlain by the TAO token economy that rewards contributors according to the value they add to model training. This democratized model of AI training is, in actuality, revolutionizing the process by which models are developed, making it possible even for small players to contribute and benefit from leading-edge AI research.
AI Agents and Autonomous Systems:
In this sublayer, the focus is more on platforms that allow the creation and deployment of autonomous AI agents that are then able to execute tasks in an independent manner. These interact with other agents, users, and systems in the blockchain environment to create a self-sustaining AI-driven process ecosystem.
SingularityNET: A decentralized marketplace for AI services where developers can offer their AI solutions to a global audience. SingularityNET’s AI agents can autonomously negotiate, interact, and execute services, facilitating a decentralized economy of AI services.iExec: This platform provides decentralized cloud computing resources specifically for AI applications, enabling developers to run their AI algorithms on a decentralized network, which enhances security and scalability while reducing costs. Fetch.AI: One class example of this sub-layer is Fetch.AI, which acts as a kind of decentralized middleware on top of which fully autonomous "agents" represent users in conducting operations. These agents are capable of negotiating and executing transactions, managing data, or optimizing processes, such as supply chain logistics or decentralized energy management. Fetch.AI is setting the foundations for a new era of decentralized automation where AI agents manage complicated tasks across a range of industries.
AI-Powered Oracles:
Oracles are very important in bringing off-chain data on-chain. This sub-layer involves integrating AI into oracles to enhance the accuracy and reliability of the data which smart contracts depend on.
Oraichain: Oraichain offers AI-powered Oracle services, providing advanced data inputs to smart contracts for dApps with more complex, dynamic interaction. It allows smart contracts that are nimble in data analytics or machine learning models behind contract execution to relate to events taking place in the real world. Chainlink: Beyond simple data feeds, Chainlink integrates AI to process and deliver complex data analytics to smart contracts. It can analyze large datasets, predict outcomes, and offer decision-making support to decentralized applications, enhancing their functionality. Augur: While primarily a prediction market, Augur uses AI to analyze historical data and predict future events, feeding these insights into decentralized prediction markets. The integration of AI ensures more accurate and reliable predictions.
⚡ Infrastructure Layer
The Infrastructure Layer forms the backbone of the Crypto AI ecosystem, providing the essential computational power, storage, and networking required to support AI and blockchain operations. This layer ensures that the ecosystem is scalable, secure, and resilient.
Decentralized Cloud Computing:
The sub-layer platforms behind this layer provide alternatives to centralized cloud services in order to keep everything decentralized. This gives scalability and flexible computing power to support AI workloads. They leverage otherwise idle resources in global data centers to create an elastic, more reliable, and cheaper cloud infrastructure.
Akash Network: Akash is a decentralized cloud computing platform that shares unutilized computation resources by users, forming a marketplace for cloud services in a way that becomes more resilient, cost-effective, and secure than centralized providers. For AI developers, Akash offers a lot of computing power to train models or run complex algorithms, hence becoming a core component of the decentralized AI infrastructure. Ankr: Ankr offers a decentralized cloud infrastructure where users can deploy AI workloads. It provides a cost-effective alternative to traditional cloud services by leveraging underutilized resources in data centers globally, ensuring high availability and resilience.Dfinity: The Internet Computer by Dfinity aims to replace traditional IT infrastructure by providing a decentralized platform for running software and applications. For AI developers, this means deploying AI applications directly onto a decentralized internet, eliminating reliance on centralized cloud providers.
Distributed Computing Networks:
This sublayer consists of platforms that perform computations on a global network of machines in such a manner that they offer the infrastructure required for large-scale workloads related to AI processing.
Gensyn: The primary focus of Gensyn lies in decentralized infrastructure for AI workloads, providing a platform where users contribute their hardware resources to fuel AI training and inference tasks. A distributed approach can ensure the scalability of infrastructure and satisfy the demands of more complex AI applications. Hadron: This platform focuses on decentralized AI computation, where users can rent out idle computational power to AI developers. Hadron’s decentralized network is particularly suited for AI tasks that require massive parallel processing, such as training deep learning models. Hummingbot: An open-source project that allows users to create high-frequency trading bots on decentralized exchanges (DEXs). Hummingbot uses distributed computing resources to execute complex AI-driven trading strategies in real-time.
Decentralized GPU Rendering:
In the case of most AI tasks, especially those with integrated graphics, and in those cases with large-scale data processing, GPU rendering is key. Such platforms offer a decentralized access to GPU resources, meaning now it would be possible to perform heavy computation tasks that do not rely on centralized services.
Render Network: The network concentrates on decentralized GPU rendering power, which is able to do AI tasks—to be exact, those executed in an intensely processing way—neural net training and 3D rendering. This enables the Render Network to leverage the world's largest pool of GPUs, offering an economic and scalable solution to AI developers while reducing the time to market for AI-driven products and services. DeepBrain Chain: A decentralized AI computing platform that integrates GPU computing power with blockchain technology. It provides AI developers with access to distributed GPU resources, reducing the cost of training AI models while ensuring data privacy. NKN (New Kind of Network): While primarily a decentralized data transmission network, NKN provides the underlying infrastructure to support distributed GPU rendering, enabling efficient AI model training and deployment across a decentralized network.
Decentralized Storage Solutions:
The management of vast amounts of data that would both be generated by and processed in AI applications requires decentralized storage. It includes platforms in this sublayer, which ensure accessibility and security in providing storage solutions.
Filecoin : Filecoin is a decentralized storage network where people can store and retrieve data. This provides a scalable, economically proven alternative to centralized solutions for the many times huge amounts of data required in AI applications. At best. At best, this sublayer would serve as an underpinning element to ensure data integrity and availability across AI-driven dApps and services. Arweave: This project offers a permanent, decentralized storage solution ideal for preserving the vast amounts of data generated by AI applications. Arweave ensures data immutability and availability, which is critical for the integrity of AI-driven applications. Storj: Another decentralized storage solution, Storj enables AI developers to store and retrieve large datasets across a distributed network securely. Storj’s decentralized nature ensures data redundancy and protection against single points of failure.
🟪 How Specific Layers Work Together?
Data Generation and Storage: Data is the lifeblood of AI. The Infrastructure Layer’s decentralized storage solutions like Filecoin and Storj ensure that the vast amounts of data generated are securely stored, easily accessible, and immutable. This data is then fed into AI models housed on decentralized AI training networks like Ocean Protocol or Bittensor.AI Model Training and Deployment: The Middleware Layer, with platforms like iExec and Ankr, provides the necessary computational power to train AI models. These models can be decentralized using platforms like Cortex, where they become available for use by dApps. Execution and Interaction: Once trained, these AI models are deployed within the Application Layer, where user-facing applications like ChainGPT and Numerai utilize them to deliver personalized services, perform financial analysis, or enhance security through AI-driven fraud detection.Real-Time Data Processing: Oracles in the Middleware Layer, like Oraichain and Chainlink, feed real-time, AI-processed data to smart contracts, enabling dynamic and responsive decentralized applications.Autonomous Systems Management: AI agents from platforms like Fetch.AI operate autonomously, interacting with other agents and systems across the blockchain ecosystem to execute tasks, optimize processes, and manage decentralized operations without human intervention.
🔼 Data Credit
> Binance Research
> Messari
> Blockworks
> Coinbase Research
> Four Pillars
> Galaxy
> Medium
The Decentralized AI landscape
Artificial intelligence (AI) has become a common term in everydays lingo, while blockchain, though often seen as distinct, is gaining prominence in the tech world, especially within the Finance space. Concepts like "AI Blockchain," "AI Crypto," and similar terms highlight the convergence of these two powerful technologies. Though distinct, AI and blockchain are increasingly being combined to drive innovation, complexity, and transformation across various industries.
The integration of AI and blockchain is creating a multi-layered ecosystem with the potential to revolutionize industries, enhance security, and improve efficiencies. Though both are different and polar opposite of each other. But, De-Centralisation of Artificial intelligence quite the right thing towards giving the authority to the people.
The Whole Decentralized AI ecosystem can be understood by breaking it down into three primary layers: the Application Layer, the Middleware Layer, and the Infrastructure Layer. Each of these layers consists of sub-layers that work together to enable the seamless creation and deployment of AI within blockchain frameworks. Let's Find out How These Actually Works......
TL;DR
Application Layer: Users interact with AI-enhanced blockchain services in this layer. Examples include AI-powered finance, healthcare, education, and supply chain solutions.Middleware Layer: This layer connects applications to infrastructure. It provides services like AI training networks, oracles, and decentralized agents for seamless AI operations.Infrastructure Layer: The backbone of the ecosystem, this layer offers decentralized cloud computing, GPU rendering, and storage solutions for scalable, secure AI and blockchain operations.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
💡Application Layer
The Application Layer is the most tangible part of the ecosystem, where end-users interact with AI-enhanced blockchain services. It integrates AI with blockchain to create innovative applications, driving the evolution of user experiences across various domains.
User-Facing Applications:
AI-Driven Financial Platforms: Beyond AI Trading Bots, platforms like Numerai leverage AI to manage decentralized hedge funds. Users can contribute models to predict stock market movements, and the best-performing models are used to inform real-world trading decisions. This democratizes access to sophisticated financial strategies and leverages collective intelligence.AI-Powered Decentralized Autonomous Organizations (DAOs): DAOstack utilizes AI to optimize decision-making processes within DAOs, ensuring more efficient governance by predicting outcomes, suggesting actions, and automating routine decisions.Healthcare dApps: Doc.ai is a project that integrates AI with blockchain to offer personalized health insights. Patients can manage their health data securely, while AI analyzes patterns to provide tailored health recommendations.Education Platforms: SingularityNET and Aletheia AI have been pioneering in using AI within education by offering personalized learning experiences, where AI-driven tutors provide tailored guidance to students, enhancing learning outcomes through decentralized platforms.
Enterprise Solutions:
AI-Powered Supply Chain: Morpheus.Network utilizes AI to streamline global supply chains. By combining blockchain's transparency with AI's predictive capabilities, it enhances logistics efficiency, predicts disruptions, and automates compliance with global trade regulations. AI-Enhanced Identity Verification: Civic and uPort integrate AI with blockchain to offer advanced identity verification solutions. AI analyzes user behavior to detect fraud, while blockchain ensures that personal data remains secure and under the control of the user.Smart City Solutions: MXC Foundation leverages AI and blockchain to optimize urban infrastructure, managing everything from energy consumption to traffic flow in real-time, thereby improving efficiency and reducing operational costs.
🏵️ Middleware Layer
The Middleware Layer connects the user-facing applications with the underlying infrastructure, providing essential services that facilitate the seamless operation of AI on the blockchain. This layer ensures interoperability, scalability, and efficiency.
AI Training Networks:
Decentralized AI training networks on blockchain combine the power of artificial intelligence with the security and transparency of blockchain technology. In this model, AI training data is distributed across multiple nodes on a blockchain network, ensuring data privacy, security, and preventing data centralization.
Ocean Protocol: This protocol focuses on democratizing AI by providing a marketplace for data sharing. Data providers can monetize their datasets, and AI developers can access diverse, high-quality data for training their models, all while ensuring data privacy through blockchain.Cortex: A decentralized AI platform that allows developers to upload AI models onto the blockchain, where they can be accessed and utilized by dApps. This ensures that AI models are transparent, auditable, and tamper-proof. Bittensor: The case of a sublayer class for such an implementation can be seen with Bittensor. It's a decentralized machine learning network where participants are incentivized to put in their computational resources and datasets. This network is underlain by the TAO token economy that rewards contributors according to the value they add to model training. This democratized model of AI training is, in actuality, revolutionizing the process by which models are developed, making it possible even for small players to contribute and benefit from leading-edge AI research.
AI Agents and Autonomous Systems:
In this sublayer, the focus is more on platforms that allow the creation and deployment of autonomous AI agents that are then able to execute tasks in an independent manner. These interact with other agents, users, and systems in the blockchain environment to create a self-sustaining AI-driven process ecosystem.
SingularityNET: A decentralized marketplace for AI services where developers can offer their AI solutions to a global audience. SingularityNET’s AI agents can autonomously negotiate, interact, and execute services, facilitating a decentralized economy of AI services.iExec: This platform provides decentralized cloud computing resources specifically for AI applications, enabling developers to run their AI algorithms on a decentralized network, which enhances security and scalability while reducing costs. Fetch.AI: One class example of this sub-layer is Fetch.AI, which acts as a kind of decentralized middleware on top of which fully autonomous "agents" represent users in conducting operations. These agents are capable of negotiating and executing transactions, managing data, or optimizing processes, such as supply chain logistics or decentralized energy management. Fetch.AI is setting the foundations for a new era of decentralized automation where AI agents manage complicated tasks across a range of industries.
AI-Powered Oracles:
Oracles are very important in bringing off-chain data on-chain. This sub-layer involves integrating AI into oracles to enhance the accuracy and reliability of the data which smart contracts depend on.
Oraichain: Oraichain offers AI-powered Oracle services, providing advanced data inputs to smart contracts for dApps with more complex, dynamic interaction. It allows smart contracts that are nimble in data analytics or machine learning models behind contract execution to relate to events taking place in the real world. Chainlink: Beyond simple data feeds, Chainlink integrates AI to process and deliver complex data analytics to smart contracts. It can analyze large datasets, predict outcomes, and offer decision-making support to decentralized applications, enhancing their functionality. Augur: While primarily a prediction market, Augur uses AI to analyze historical data and predict future events, feeding these insights into decentralized prediction markets. The integration of AI ensures more accurate and reliable predictions.
⚡ Infrastructure Layer
The Infrastructure Layer forms the backbone of the Crypto AI ecosystem, providing the essential computational power, storage, and networking required to support AI and blockchain operations. This layer ensures that the ecosystem is scalable, secure, and resilient.
Decentralized Cloud Computing:
The sub-layer platforms behind this layer provide alternatives to centralized cloud services in order to keep everything decentralized. This gives scalability and flexible computing power to support AI workloads. They leverage otherwise idle resources in global data centers to create an elastic, more reliable, and cheaper cloud infrastructure.
Akash Network: Akash is a decentralized cloud computing platform that shares unutilized computation resources by users, forming a marketplace for cloud services in a way that becomes more resilient, cost-effective, and secure than centralized providers. For AI developers, Akash offers a lot of computing power to train models or run complex algorithms, hence becoming a core component of the decentralized AI infrastructure. Ankr: Ankr offers a decentralized cloud infrastructure where users can deploy AI workloads. It provides a cost-effective alternative to traditional cloud services by leveraging underutilized resources in data centers globally, ensuring high availability and resilience.Dfinity: The Internet Computer by Dfinity aims to replace traditional IT infrastructure by providing a decentralized platform for running software and applications. For AI developers, this means deploying AI applications directly onto a decentralized internet, eliminating reliance on centralized cloud providers.
Distributed Computing Networks:
This sublayer consists of platforms that perform computations on a global network of machines in such a manner that they offer the infrastructure required for large-scale workloads related to AI processing.
Gensyn: The primary focus of Gensyn lies in decentralized infrastructure for AI workloads, providing a platform where users contribute their hardware resources to fuel AI training and inference tasks. A distributed approach can ensure the scalability of infrastructure and satisfy the demands of more complex AI applications. Hadron: This platform focuses on decentralized AI computation, where users can rent out idle computational power to AI developers. Hadron’s decentralized network is particularly suited for AI tasks that require massive parallel processing, such as training deep learning models. Hummingbot: An open-source project that allows users to create high-frequency trading bots on decentralized exchanges (DEXs). Hummingbot uses distributed computing resources to execute complex AI-driven trading strategies in real-time.
Decentralized GPU Rendering:
In the case of most AI tasks, especially those with integrated graphics, and in those cases with large-scale data processing, GPU rendering is key. Such platforms offer a decentralized access to GPU resources, meaning now it would be possible to perform heavy computation tasks that do not rely on centralized services.
Render Network: The network concentrates on decentralized GPU rendering power, which is able to do AI tasks—to be exact, those executed in an intensely processing way—neural net training and 3D rendering. This enables the Render Network to leverage the world's largest pool of GPUs, offering an economic and scalable solution to AI developers while reducing the time to market for AI-driven products and services. DeepBrain Chain: A decentralized AI computing platform that integrates GPU computing power with blockchain technology. It provides AI developers with access to distributed GPU resources, reducing the cost of training AI models while ensuring data privacy. NKN (New Kind of Network): While primarily a decentralized data transmission network, NKN provides the underlying infrastructure to support distributed GPU rendering, enabling efficient AI model training and deployment across a decentralized network.
Decentralized Storage Solutions:
The management of vast amounts of data that would both be generated by and processed in AI applications requires decentralized storage. It includes platforms in this sublayer, which ensure accessibility and security in providing storage solutions.
Filecoin : Filecoin is a decentralized storage network where people can store and retrieve data. This provides a scalable, economically proven alternative to centralized solutions for the many times huge amounts of data required in AI applications. At best. At best, this sublayer would serve as an underpinning element to ensure data integrity and availability across AI-driven dApps and services. Arweave: This project offers a permanent, decentralized storage solution ideal for preserving the vast amounts of data generated by AI applications. Arweave ensures data immutability and availability, which is critical for the integrity of AI-driven applications. Storj: Another decentralized storage solution, Storj enables AI developers to store and retrieve large datasets across a distributed network securely. Storj’s decentralized nature ensures data redundancy and protection against single points of failure.
🟪 How Specific Layers Work Together?
Data Generation and Storage: Data is the lifeblood of AI. The Infrastructure Layer’s decentralized storage solutions like Filecoin and Storj ensure that the vast amounts of data generated are securely stored, easily accessible, and immutable. This data is then fed into AI models housed on decentralized AI training networks like Ocean Protocol or Bittensor.AI Model Training and Deployment: The Middleware Layer, with platforms like iExec and Ankr, provides the necessary computational power to train AI models. These models can be decentralized using platforms like Cortex, where they become available for use by dApps. Execution and Interaction: Once trained, these AI models are deployed within the Application Layer, where user-facing applications like ChainGPT and Numerai utilize them to deliver personalized services, perform financial analysis, or enhance security through AI-driven fraud detection.Real-Time Data Processing: Oracles in the Middleware Layer, like Oraichain and Chainlink, feed real-time, AI-processed data to smart contracts, enabling dynamic and responsive decentralized applications.Autonomous Systems Management: AI agents from platforms like Fetch.AI operate autonomously, interacting with other agents and systems across the blockchain ecosystem to execute tasks, optimize processes, and manage decentralized operations without human intervention.
🔼 Data Credit
> Binance Research
> Messari
> Blockworks
> Coinbase Research
> Four Pillars
> Galaxy
> Medium
The integration of AI and blockchain is creating a multi-layered ecosystem with the potential to revolutionize industries, enhance security, and improve efficiencies. Though both are different and polar opposite of each other. But, De-Centralisation of Artificial intelligence quite the right thing towards giving the authority to the people.
The Whole Decentralized AI ecosystem can be understood by breaking it down into three primary layers: the Application Layer, the Middleware Layer, and the Infrastructure Layer. Each of these layers consists of sub-layers that work together to enable the seamless creation and deployment of AI within blockchain frameworks. Let's Find out How These Actually Works......
TL;DR
Application Layer: Users interact with AI-enhanced blockchain services in this layer. Examples include AI-powered finance, healthcare, education, and supply chain solutions.Middleware Layer: This layer connects applications to infrastructure. It provides services like AI training networks, oracles, and decentralized agents for seamless AI operations.Infrastructure Layer: The backbone of the ecosystem, this layer offers decentralized cloud computing, GPU rendering, and storage solutions for scalable, secure AI and blockchain operations.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
💡Application Layer
The Application Layer is the most tangible part of the ecosystem, where end-users interact with AI-enhanced blockchain services. It integrates AI with blockchain to create innovative applications, driving the evolution of user experiences across various domains.
User-Facing Applications:
AI-Driven Financial Platforms: Beyond AI Trading Bots, platforms like Numerai leverage AI to manage decentralized hedge funds. Users can contribute models to predict stock market movements, and the best-performing models are used to inform real-world trading decisions. This democratizes access to sophisticated financial strategies and leverages collective intelligence.AI-Powered Decentralized Autonomous Organizations (DAOs): DAOstack utilizes AI to optimize decision-making processes within DAOs, ensuring more efficient governance by predicting outcomes, suggesting actions, and automating routine decisions.Healthcare dApps: Doc.ai is a project that integrates AI with blockchain to offer personalized health insights. Patients can manage their health data securely, while AI analyzes patterns to provide tailored health recommendations.Education Platforms: SingularityNET and Aletheia AI have been pioneering in using AI within education by offering personalized learning experiences, where AI-driven tutors provide tailored guidance to students, enhancing learning outcomes through decentralized platforms.
Enterprise Solutions:
AI-Powered Supply Chain: Morpheus.Network utilizes AI to streamline global supply chains. By combining blockchain's transparency with AI's predictive capabilities, it enhances logistics efficiency, predicts disruptions, and automates compliance with global trade regulations. AI-Enhanced Identity Verification: Civic and uPort integrate AI with blockchain to offer advanced identity verification solutions. AI analyzes user behavior to detect fraud, while blockchain ensures that personal data remains secure and under the control of the user.Smart City Solutions: MXC Foundation leverages AI and blockchain to optimize urban infrastructure, managing everything from energy consumption to traffic flow in real-time, thereby improving efficiency and reducing operational costs.
🏵️ Middleware Layer
The Middleware Layer connects the user-facing applications with the underlying infrastructure, providing essential services that facilitate the seamless operation of AI on the blockchain. This layer ensures interoperability, scalability, and efficiency.
AI Training Networks:
Decentralized AI training networks on blockchain combine the power of artificial intelligence with the security and transparency of blockchain technology. In this model, AI training data is distributed across multiple nodes on a blockchain network, ensuring data privacy, security, and preventing data centralization.
Ocean Protocol: This protocol focuses on democratizing AI by providing a marketplace for data sharing. Data providers can monetize their datasets, and AI developers can access diverse, high-quality data for training their models, all while ensuring data privacy through blockchain.Cortex: A decentralized AI platform that allows developers to upload AI models onto the blockchain, where they can be accessed and utilized by dApps. This ensures that AI models are transparent, auditable, and tamper-proof. Bittensor: The case of a sublayer class for such an implementation can be seen with Bittensor. It's a decentralized machine learning network where participants are incentivized to put in their computational resources and datasets. This network is underlain by the TAO token economy that rewards contributors according to the value they add to model training. This democratized model of AI training is, in actuality, revolutionizing the process by which models are developed, making it possible even for small players to contribute and benefit from leading-edge AI research.
AI Agents and Autonomous Systems:
In this sublayer, the focus is more on platforms that allow the creation and deployment of autonomous AI agents that are then able to execute tasks in an independent manner. These interact with other agents, users, and systems in the blockchain environment to create a self-sustaining AI-driven process ecosystem.
SingularityNET: A decentralized marketplace for AI services where developers can offer their AI solutions to a global audience. SingularityNET’s AI agents can autonomously negotiate, interact, and execute services, facilitating a decentralized economy of AI services.iExec: This platform provides decentralized cloud computing resources specifically for AI applications, enabling developers to run their AI algorithms on a decentralized network, which enhances security and scalability while reducing costs. Fetch.AI: One class example of this sub-layer is Fetch.AI, which acts as a kind of decentralized middleware on top of which fully autonomous "agents" represent users in conducting operations. These agents are capable of negotiating and executing transactions, managing data, or optimizing processes, such as supply chain logistics or decentralized energy management. Fetch.AI is setting the foundations for a new era of decentralized automation where AI agents manage complicated tasks across a range of industries.
AI-Powered Oracles:
Oracles are very important in bringing off-chain data on-chain. This sub-layer involves integrating AI into oracles to enhance the accuracy and reliability of the data which smart contracts depend on.
Oraichain: Oraichain offers AI-powered Oracle services, providing advanced data inputs to smart contracts for dApps with more complex, dynamic interaction. It allows smart contracts that are nimble in data analytics or machine learning models behind contract execution to relate to events taking place in the real world. Chainlink: Beyond simple data feeds, Chainlink integrates AI to process and deliver complex data analytics to smart contracts. It can analyze large datasets, predict outcomes, and offer decision-making support to decentralized applications, enhancing their functionality. Augur: While primarily a prediction market, Augur uses AI to analyze historical data and predict future events, feeding these insights into decentralized prediction markets. The integration of AI ensures more accurate and reliable predictions.
⚡ Infrastructure Layer
The Infrastructure Layer forms the backbone of the Crypto AI ecosystem, providing the essential computational power, storage, and networking required to support AI and blockchain operations. This layer ensures that the ecosystem is scalable, secure, and resilient.
Decentralized Cloud Computing:
The sub-layer platforms behind this layer provide alternatives to centralized cloud services in order to keep everything decentralized. This gives scalability and flexible computing power to support AI workloads. They leverage otherwise idle resources in global data centers to create an elastic, more reliable, and cheaper cloud infrastructure.
Akash Network: Akash is a decentralized cloud computing platform that shares unutilized computation resources by users, forming a marketplace for cloud services in a way that becomes more resilient, cost-effective, and secure than centralized providers. For AI developers, Akash offers a lot of computing power to train models or run complex algorithms, hence becoming a core component of the decentralized AI infrastructure. Ankr: Ankr offers a decentralized cloud infrastructure where users can deploy AI workloads. It provides a cost-effective alternative to traditional cloud services by leveraging underutilized resources in data centers globally, ensuring high availability and resilience.Dfinity: The Internet Computer by Dfinity aims to replace traditional IT infrastructure by providing a decentralized platform for running software and applications. For AI developers, this means deploying AI applications directly onto a decentralized internet, eliminating reliance on centralized cloud providers.
Distributed Computing Networks:
This sublayer consists of platforms that perform computations on a global network of machines in such a manner that they offer the infrastructure required for large-scale workloads related to AI processing.
Gensyn: The primary focus of Gensyn lies in decentralized infrastructure for AI workloads, providing a platform where users contribute their hardware resources to fuel AI training and inference tasks. A distributed approach can ensure the scalability of infrastructure and satisfy the demands of more complex AI applications. Hadron: This platform focuses on decentralized AI computation, where users can rent out idle computational power to AI developers. Hadron’s decentralized network is particularly suited for AI tasks that require massive parallel processing, such as training deep learning models. Hummingbot: An open-source project that allows users to create high-frequency trading bots on decentralized exchanges (DEXs). Hummingbot uses distributed computing resources to execute complex AI-driven trading strategies in real-time.
Decentralized GPU Rendering:
In the case of most AI tasks, especially those with integrated graphics, and in those cases with large-scale data processing, GPU rendering is key. Such platforms offer a decentralized access to GPU resources, meaning now it would be possible to perform heavy computation tasks that do not rely on centralized services.
Render Network: The network concentrates on decentralized GPU rendering power, which is able to do AI tasks—to be exact, those executed in an intensely processing way—neural net training and 3D rendering. This enables the Render Network to leverage the world's largest pool of GPUs, offering an economic and scalable solution to AI developers while reducing the time to market for AI-driven products and services. DeepBrain Chain: A decentralized AI computing platform that integrates GPU computing power with blockchain technology. It provides AI developers with access to distributed GPU resources, reducing the cost of training AI models while ensuring data privacy. NKN (New Kind of Network): While primarily a decentralized data transmission network, NKN provides the underlying infrastructure to support distributed GPU rendering, enabling efficient AI model training and deployment across a decentralized network.
Decentralized Storage Solutions:
The management of vast amounts of data that would both be generated by and processed in AI applications requires decentralized storage. It includes platforms in this sublayer, which ensure accessibility and security in providing storage solutions.
Filecoin : Filecoin is a decentralized storage network where people can store and retrieve data. This provides a scalable, economically proven alternative to centralized solutions for the many times huge amounts of data required in AI applications. At best. At best, this sublayer would serve as an underpinning element to ensure data integrity and availability across AI-driven dApps and services. Arweave: This project offers a permanent, decentralized storage solution ideal for preserving the vast amounts of data generated by AI applications. Arweave ensures data immutability and availability, which is critical for the integrity of AI-driven applications. Storj: Another decentralized storage solution, Storj enables AI developers to store and retrieve large datasets across a distributed network securely. Storj’s decentralized nature ensures data redundancy and protection against single points of failure.
🟪 How Specific Layers Work Together?
Data Generation and Storage: Data is the lifeblood of AI. The Infrastructure Layer’s decentralized storage solutions like Filecoin and Storj ensure that the vast amounts of data generated are securely stored, easily accessible, and immutable. This data is then fed into AI models housed on decentralized AI training networks like Ocean Protocol or Bittensor.AI Model Training and Deployment: The Middleware Layer, with platforms like iExec and Ankr, provides the necessary computational power to train AI models. These models can be decentralized using platforms like Cortex, where they become available for use by dApps. Execution and Interaction: Once trained, these AI models are deployed within the Application Layer, where user-facing applications like ChainGPT and Numerai utilize them to deliver personalized services, perform financial analysis, or enhance security through AI-driven fraud detection.Real-Time Data Processing: Oracles in the Middleware Layer, like Oraichain and Chainlink, feed real-time, AI-processed data to smart contracts, enabling dynamic and responsive decentralized applications.Autonomous Systems Management: AI agents from platforms like Fetch.AI operate autonomously, interacting with other agents and systems across the blockchain ecosystem to execute tasks, optimize processes, and manage decentralized operations without human intervention.
🔼 Data Credit
> Binance Research
> Messari
> Blockworks
> Coinbase Research
> Four Pillars
> Galaxy
> Medium
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Article
The Space ETF Boom: Assets Hit $5 Billion as Retail Chases SpaceXThe financial markets are expanding into new territory right now. Investors are looking past traditional tech stocks and putting massive amounts of capital directly into the space economy. Retail traders are clearly hungry for aerospace exposure and the latest data proves they are using exchange traded funds to get it.
A Historic $5 Billion Milestone
The total amount of money managed by space focused funds is breaking records.
The New Peak: Total assets under management in space themed ETFs just crossed the $5 billion mark for the first time in history.Doubling in 2026: Since the start of the year this total asset figure has more than doubled.Massive Yearly Growth: When you look back over the last twelve months the growth is even more extreme. Total assets across these funds have surged by over 900 percent.
The $NASA ETF Phenomenon
One specific fund is driving a massive portion of this retail demand.
A $2.6 Billion Launch: The Space Innovators ETF trades under the ticker NASA. It launched very recently on March 30th but has already grown its assets to a record $2.6 billion.Rapid Price Action: The price performance of the fund matches the heavy capital inflows. The fund price has surged 50 percent since its launch.Accelerating Momentum: The buying pressure is picking up fast. The fund saw a 37 percent price increase in May alone.
The Private Market Connection
The real reason behind this massive growth comes down to a single private company.
Hunting for SpaceX: Regular investors are desperate to buy shares of SpaceX. The problem is that it remains a private company.The Retail Gateway: The NASA fund is one of the very few investment vehicles available to everyday retail investors that actually offers direct exposure to the company.The Portfolio Share: SpaceX currently accounts for 7.5 percent of the entire fund.
Some Random Thoughts 💬
We are watching retail investors force their way into private markets. For years private equity funds kept the best aerospace returns entirely for themselves. Now everyday people are using ETFs to bypass those walls. Pushing $2.6 billion into a brand new fund just to get a 7.5 percent slice of SpaceX shows incredible market conviction.
The retail sector is sending a massive signal to Wall Street right now. They are completely ready for a SpaceX initial public offering and they will buy absolutely any vehicle that gives them early access to the launchpad.
The Space ETF Boom: Assets Hit $5 Billion as Retail Chases SpaceX
The financial markets are expanding into new territory right now. Investors are looking past traditional tech stocks and putting massive amounts of capital directly into the space economy. Retail traders are clearly hungry for aerospace exposure and the latest data proves they are using exchange traded funds to get it.
A Historic $5 Billion Milestone
The total amount of money managed by space focused funds is breaking records.
The New Peak: Total assets under management in space themed ETFs just crossed the $5 billion mark for the first time in history.Doubling in 2026: Since the start of the year this total asset figure has more than doubled.Massive Yearly Growth: When you look back over the last twelve months the growth is even more extreme. Total assets across these funds have surged by over 900 percent.
The $NASA ETF Phenomenon
One specific fund is driving a massive portion of this retail demand.
A $2.6 Billion Launch: The Space Innovators ETF trades under the ticker NASA. It launched very recently on March 30th but has already grown its assets to a record $2.6 billion.Rapid Price Action: The price performance of the fund matches the heavy capital inflows. The fund price has surged 50 percent since its launch.Accelerating Momentum: The buying pressure is picking up fast. The fund saw a 37 percent price increase in May alone.
The Private Market Connection
The real reason behind this massive growth comes down to a single private company.
Hunting for SpaceX: Regular investors are desperate to buy shares of SpaceX. The problem is that it remains a private company.The Retail Gateway: The NASA fund is one of the very few investment vehicles available to everyday retail investors that actually offers direct exposure to the company.The Portfolio Share: SpaceX currently accounts for 7.5 percent of the entire fund.
Some Random Thoughts 💬
We are watching retail investors force their way into private markets. For years private equity funds kept the best aerospace returns entirely for themselves. Now everyday people are using ETFs to bypass those walls. Pushing $2.6 billion into a brand new fund just to get a 7.5 percent slice of SpaceX shows incredible market conviction.
The retail sector is sending a massive signal to Wall Street right now. They are completely ready for a SpaceX initial public offering and they will buy absolutely any vehicle that gives them early access to the launchpad.
A Historic $5 Billion Milestone
The total amount of money managed by space focused funds is breaking records.
The New Peak: Total assets under management in space themed ETFs just crossed the $5 billion mark for the first time in history.Doubling in 2026: Since the start of the year this total asset figure has more than doubled.Massive Yearly Growth: When you look back over the last twelve months the growth is even more extreme. Total assets across these funds have surged by over 900 percent.
The $NASA ETF Phenomenon
One specific fund is driving a massive portion of this retail demand.
A $2.6 Billion Launch: The Space Innovators ETF trades under the ticker NASA. It launched very recently on March 30th but has already grown its assets to a record $2.6 billion.Rapid Price Action: The price performance of the fund matches the heavy capital inflows. The fund price has surged 50 percent since its launch.Accelerating Momentum: The buying pressure is picking up fast. The fund saw a 37 percent price increase in May alone.
The Private Market Connection
The real reason behind this massive growth comes down to a single private company.
Hunting for SpaceX: Regular investors are desperate to buy shares of SpaceX. The problem is that it remains a private company.The Retail Gateway: The NASA fund is one of the very few investment vehicles available to everyday retail investors that actually offers direct exposure to the company.The Portfolio Share: SpaceX currently accounts for 7.5 percent of the entire fund.
Some Random Thoughts 💬
We are watching retail investors force their way into private markets. For years private equity funds kept the best aerospace returns entirely for themselves. Now everyday people are using ETFs to bypass those walls. Pushing $2.6 billion into a brand new fund just to get a 7.5 percent slice of SpaceX shows incredible market conviction.
The retail sector is sending a massive signal to Wall Street right now. They are completely ready for a SpaceX initial public offering and they will buy absolutely any vehicle that gives them early access to the launchpad.
1.1k
🚀 𝙏𝙤𝙥 10 𝙂𝙖𝙞𝙣𝙚𝙧𝙨 𝙞𝙣 𝙈𝙖𝙮
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+592.8%: $LAB
+291.0%: H
+96.6%: $VVV
+90.6%: GNO
+81.1%: RAIN
+80.8%: $NEAR
+77.7%: Binancelive
+76.2%: HYPE
+76.0%: INJ
+71.4%: DEXE
© Cryptorank
🚀 𝙏𝙤𝙥 10 𝙂𝙖𝙞𝙣𝙚𝙧𝙨 𝙞𝙣 𝙈𝙖𝙮
-
+592.8%: $LAB
+291.0%: H
+96.6%: $VVV
+90.6%: GNO
+81.1%: RAIN
+80.8%: $NEAR
+77.7%: Binancelive
+76.2%: HYPE
+76.0%: INJ
+71.4%: DEXE
© Cryptorank
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+592.8%: $LAB
+291.0%: H
+96.6%: $VVV
+90.6%: GNO
+81.1%: RAIN
+80.8%: $NEAR
+77.7%: Binancelive
+76.2%: HYPE
+76.0%: INJ
+71.4%: DEXE
© Cryptorank
1
1.4k
𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞
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Agentic finance is accelerating faster than anyone predicted - new projects, capabilities, and primitives keep entering the fray, and institutions are now visibly willing to back it.
On April 2nd, CB contributed the x402 protocol to the Linux Foundation with Cloudflare, Stripe, Visa, Mastercard, Google, and Circle among the backers - a clear signal we're heading toward a world where billions of agents transact on blockchains.
© Top7ICO
𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞
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Agentic finance is accelerating faster than anyone predicted - new projects, capabilities, and primitives keep entering the fray, and institutions are now visibly willing to back it.
On April 2nd, CB contributed the x402 protocol to the Linux Foundation with Cloudflare, Stripe, Visa, Mastercard, Google, and Circle among the backers - a clear signal we're heading toward a world where billions of agents transact on blockchains.
© Top7ICO
-
Agentic finance is accelerating faster than anyone predicted - new projects, capabilities, and primitives keep entering the fray, and institutions are now visibly willing to back it.
On April 2nd, CB contributed the x402 protocol to the Linux Foundation with Cloudflare, Stripe, Visa, Mastercard, Google, and Circle among the backers - a clear signal we're heading toward a world where billions of agents transact on blockchains.
© Top7ICO
1
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$BTC $12.6 𝙩𝙧𝙞𝙡𝙡𝙞𝙤𝙣 𝘾𝙝𝙖𝙧𝙡𝙚𝙨 𝙎𝙘𝙝𝙬𝙖𝙗 𝙡𝙖𝙪𝙣𝙘𝙝𝙚𝙨 24/7 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙛𝙪𝙩𝙪𝙧𝙚𝙨 𝙩𝙧𝙖𝙙𝙞𝙣𝙜 🚀
$BTC $12.6 𝙩𝙧𝙞𝙡𝙡𝙞𝙤𝙣 𝘾𝙝𝙖𝙧𝙡𝙚𝙨 𝙎𝙘𝙝𝙬𝙖𝙗 𝙡𝙖𝙪𝙣𝙘𝙝𝙚𝙨 24/7 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙛𝙪𝙩𝙪𝙧𝙚𝙨 𝙩𝙧𝙖𝙙𝙞𝙣𝙜 🚀
1
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$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙃𝙤𝙡𝙙𝙚𝙧𝙨 𝘼𝙧𝙚 𝙇𝙤𝙨𝙞𝙣𝙜 𝙈𝙤𝙣𝙚𝙮
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At $69.5k, LTH Relative Unrealized Loss sits at 15.5%. For every dollar long-term holders' bags are worth today, they are carrying roughly 15 cents in unrealized loss. At cyclical extremes, that number has exceeded 50 cents on the dollar.
Stress is present, but the long-term holder base remains far from the levels of pain that have historically marked cycle lows.
© Glassnode
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙃𝙤𝙡𝙙𝙚𝙧𝙨 𝘼𝙧𝙚 𝙇𝙤𝙨𝙞𝙣𝙜 𝙈𝙤𝙣𝙚𝙮
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At $69.5k, LTH Relative Unrealized Loss sits at 15.5%. For every dollar long-term holders' bags are worth today, they are carrying roughly 15 cents in unrealized loss. At cyclical extremes, that number has exceeded 50 cents on the dollar.
Stress is present, but the long-term holder base remains far from the levels of pain that have historically marked cycle lows.
© Glassnode
-
At $69.5k, LTH Relative Unrealized Loss sits at 15.5%. For every dollar long-term holders' bags are worth today, they are carrying roughly 15 cents in unrealized loss. At cyclical extremes, that number has exceeded 50 cents on the dollar.
Stress is present, but the long-term holder base remains far from the levels of pain that have historically marked cycle lows.
© Glassnode
1
1.3k
$BTC $50K crash calls vs a rare bottom signal.
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bitcoin:native just hit a nine week low and $1.8B was liquidated. An indicator that nailed the last 3 bottoms (2018, 2020, 2022) just flashed again. Prediction markets put $50K in June at 6%.
© Coinmarketcap
$BTC $50K crash calls vs a rare bottom signal.
-
bitcoin:native just hit a nine week low and $1.8B was liquidated. An indicator that nailed the last 3 bottoms (2018, 2020, 2022) just flashed again. Prediction markets put $50K in June at 6%.
© Coinmarketcap
-
bitcoin:native just hit a nine week low and $1.8B was liquidated. An indicator that nailed the last 3 bottoms (2018, 2020, 2022) just flashed again. Prediction markets put $50K in June at 6%.
© Coinmarketcap
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$ZEC activates the NU6.2 hard fork after patching a zero-knowledge proof vulnerability in its Orchard shielded pool that could have enabled double-spending, resolved with no user funds lost or supply inflated.
© Coindesk
$ZEC activates the NU6.2 hard fork after patching a zero-knowledge proof vulnerability in its Orchard shielded pool that could have enabled double-spending, resolved with no user funds lost or supply inflated.
© Coindesk
© Coindesk
2
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𝙏𝙤𝙥 10 𝙋𝙧𝙞𝙫𝙖𝙘𝙮 𝘽𝙡𝙤𝙘𝙠𝙘𝙝𝙖𝙞𝙣 𝘾𝙤𝙞𝙣𝙨 𝙗𝙮 𝙈𝙖𝙧𝙠𝙚𝙩 𝘾𝙖𝙥𝙞𝙩𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣
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$CC
$H
$NIGHT
$XTZ
$STRK
© Generation Crypto
𝙏𝙤𝙥 10 𝙋𝙧𝙞𝙫𝙖𝙘𝙮 𝘽𝙡𝙤𝙘𝙠𝙘𝙝𝙖𝙞𝙣 𝘾𝙤𝙞𝙣𝙨 𝙗𝙮 𝙈𝙖𝙧𝙠𝙚𝙩 𝘾𝙖𝙥𝙞𝙩𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣
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$CC
$H
$NIGHT
$XTZ
$STRK
© Generation Crypto
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$CC
$H
$NIGHT
$XTZ
$STRK
© Generation Crypto
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$BTC 60k is My Final Dance, Next Floor 48k , Most Alts Are Heavily Oversold.
$BTC 60k is My Final Dance, Next Floor 48k , Most Alts Are Heavily Oversold.
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$HYPE 𝙂𝙧𝙖𝙮𝙨𝙘𝙖𝙡𝙚 𝙡𝙖𝙪𝙣𝙘𝙝𝙚𝙨 𝙡𝙤𝙬𝙚𝙨𝙩-𝙛𝙚𝙚 𝙐.𝙎. 𝙃𝙮𝙥𝙚𝙧𝙡𝙞𝙦𝙪𝙞𝙙 𝙀𝙏𝙁 𝙖𝙨 𝙘𝙤𝙢𝙥𝙚𝙩𝙞𝙩𝙞𝙤𝙣 𝙝𝙚𝙖𝙩𝙨 𝙪𝙥 𝙖𝙧𝙤𝙪𝙣𝙙 𝙃𝙔𝙋𝙀
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Grayscale’s HYPG launched June 3, with a 0.29% fee, the lowest of any US HYPE product.
The fund undercuts 21Shares’ 0.30% THYP and Bitwise’s BHYP, deepening a HYPE ETF price war.
HYPG is the third U.S.-listed Hyperliquid fund, with HYPE ETFs already topping $132 million in inflows.
© Bitcoin.com
$HYPE 𝙂𝙧𝙖𝙮𝙨𝙘𝙖𝙡𝙚 𝙡𝙖𝙪𝙣𝙘𝙝𝙚𝙨 𝙡𝙤𝙬𝙚𝙨𝙩-𝙛𝙚𝙚 𝙐.𝙎. 𝙃𝙮𝙥𝙚𝙧𝙡𝙞𝙦𝙪𝙞𝙙 𝙀𝙏𝙁 𝙖𝙨 𝙘𝙤𝙢𝙥𝙚𝙩𝙞𝙩𝙞𝙤𝙣 𝙝𝙚𝙖𝙩𝙨 𝙪𝙥 𝙖𝙧𝙤𝙪𝙣𝙙 𝙃𝙔𝙋𝙀
-
Grayscale’s HYPG launched June 3, with a 0.29% fee, the lowest of any US HYPE product.
The fund undercuts 21Shares’ 0.30% THYP and Bitwise’s BHYP, deepening a HYPE ETF price war.
HYPG is the third U.S.-listed Hyperliquid fund, with HYPE ETFs already topping $132 million in inflows.
© Bitcoin.com
-
Grayscale’s HYPG launched June 3, with a 0.29% fee, the lowest of any US HYPE product.
The fund undercuts 21Shares’ 0.30% THYP and Bitwise’s BHYP, deepening a HYPE ETF price war.
HYPG is the third U.S.-listed Hyperliquid fund, with HYPE ETFs already topping $132 million in inflows.
© Bitcoin.com
3
2.4k
🔅𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼 𝗶𝗻 𝗹𝗮𝘀𝘁 24𝗛?🔅
-
• $BTC falls below $66K in sharp market selloff
• Spot Bitcoin ETFs extend record outflow streak
• $ETH drops below $1.9K amid broad weakness
• CLARITY Act added to Senate agenda
• Crypto liquidations surpass $1.7B
• Hong Kong advances virtual asset licensing framework
• June heats up with major TGEs and network upgrades
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
🔅𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼 𝗶𝗻 𝗹𝗮𝘀𝘁 24𝗛?🔅
-
• $BTC falls below $66K in sharp market selloff
• Spot Bitcoin ETFs extend record outflow streak
• $ETH drops below $1.9K amid broad weakness
• CLARITY Act added to Senate agenda
• Crypto liquidations surpass $1.7B
• Hong Kong advances virtual asset licensing framework
• June heats up with major TGEs and network upgrades
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
-
• $BTC falls below $66K in sharp market selloff
• Spot Bitcoin ETFs extend record outflow streak
• $ETH drops below $1.9K amid broad weakness
• CLARITY Act added to Senate agenda
• Crypto liquidations surpass $1.7B
• Hong Kong advances virtual asset licensing framework
• June heats up with major TGEs and network upgrades
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
1
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1.6k
$BTC 𝙏𝙝𝙚 𝙙𝙚𝙨𝙘𝙚𝙣𝙩 𝙤𝙛 𝙘𝙧𝙮𝙥𝙩𝙤 𝙥𝙧𝙞𝙘𝙚𝙨, 𝙥𝙖𝙧𝙩𝙞𝙘𝙪𝙡𝙖𝙧𝙡𝙮 𝘽𝙞𝙩𝙘𝙤𝙞𝙣’𝙨 -13% 𝙙𝙧𝙤𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙥𝙖𝙨𝙩 𝙬𝙚𝙚𝙠, 𝙘𝙖𝙣 𝙗𝙚 𝙖𝙩𝙩𝙧𝙞𝙗𝙪𝙩𝙚𝙙 𝙥𝙧𝙞𝙢𝙖𝙧𝙞𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝙙𝙪𝙢𝙥𝙞𝙣𝙜 𝙗𝙮 𝙠𝙚𝙮 𝙨𝙩𝙖𝙠𝙚𝙝𝙤𝙡𝙙𝙚𝙧𝙨:
-
🐳🦈 Bitcoin whales and sharks (holding 10-10K $BTC) have dumped -24,602 coins (-18%) in the past week.
🐟🦐 Bitcoin micro traders (holding under 0.01 $BTC) have accumulated +61 coins (+12%) in the past week.
© Santiment
$BTC 𝙏𝙝𝙚 𝙙𝙚𝙨𝙘𝙚𝙣𝙩 𝙤𝙛 𝙘𝙧𝙮𝙥𝙩𝙤 𝙥𝙧𝙞𝙘𝙚𝙨, 𝙥𝙖𝙧𝙩𝙞𝙘𝙪𝙡𝙖𝙧𝙡𝙮 𝘽𝙞𝙩𝙘𝙤𝙞𝙣’𝙨 -13% 𝙙𝙧𝙤𝙥 𝙞𝙣 𝙩𝙝𝙚 𝙥𝙖𝙨𝙩 𝙬𝙚𝙚𝙠, 𝙘𝙖𝙣 𝙗𝙚 𝙖𝙩𝙩𝙧𝙞𝙗𝙪𝙩𝙚𝙙 𝙥𝙧𝙞𝙢𝙖𝙧𝙞𝙡𝙮 𝙩𝙤 𝙩𝙝𝙚 𝙙𝙪𝙢𝙥𝙞𝙣𝙜 𝙗𝙮 𝙠𝙚𝙮 𝙨𝙩𝙖𝙠𝙚𝙝𝙤𝙡𝙙𝙚𝙧𝙨:
-
🐳🦈 Bitcoin whales and sharks (holding 10-10K $BTC ) have dumped -24,602 coins (-18%) in the past week.
🐟🦐 Bitcoin micro traders (holding under 0.01 $BTC ) have accumulated +61 coins (+12%) in the past week.
© Santiment
-
🐳🦈 Bitcoin whales and sharks (holding 10-10K $BTC ) have dumped -24,602 coins (-18%) in the past week.
🐟🦐 Bitcoin micro traders (holding under 0.01 $BTC ) have accumulated +61 coins (+12%) in the past week.
© Santiment
4
1.7k
$SOL 𝙈𝙖𝙮 𝙬𝙖𝙨 𝙖𝙣𝙤𝙩𝙝𝙚𝙧 𝙧𝙚𝙢𝙞𝙣𝙙𝙚𝙧 𝙩𝙝𝙖𝙩 𝙖𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣 𝙧𝙚𝙫𝙚𝙣𝙪𝙚 𝙢𝙖𝙩𝙩𝙚𝙧𝙨 𝙢𝙤𝙧𝙚 𝙩𝙝𝙖𝙣 𝙣𝙖𝙧𝙧𝙖𝙩𝙞𝙫𝙚
-
Solana led all chains with $91M in app revenue, followed by Hyperliquid at $53M and Ethereum at $52M.
Capital continues to flow toward ecosystems where users are actively generating fees rather than just speculating on future adoption.
© Defillama
$SOL 𝙈𝙖𝙮 𝙬𝙖𝙨 𝙖𝙣𝙤𝙩𝙝𝙚𝙧 𝙧𝙚𝙢𝙞𝙣𝙙𝙚𝙧 𝙩𝙝𝙖𝙩 𝙖𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣 𝙧𝙚𝙫𝙚𝙣𝙪𝙚 𝙢𝙖𝙩𝙩𝙚𝙧𝙨 𝙢𝙤𝙧𝙚 𝙩𝙝𝙖𝙣 𝙣𝙖𝙧𝙧𝙖𝙩𝙞𝙫𝙚
-
Solana led all chains with $91M in app revenue, followed by Hyperliquid at $53M and Ethereum at $52M.
Capital continues to flow toward ecosystems where users are actively generating fees rather than just speculating on future adoption.
© Defillama
-
Solana led all chains with $91M in app revenue, followed by Hyperliquid at $53M and Ethereum at $52M.
Capital continues to flow toward ecosystems where users are actively generating fees rather than just speculating on future adoption.
© Defillama
1
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$BTC Bitcoin Sinks to $66,000 as $1.35B in Long Liquidations Accelerate Market Selloff
$BTC Bitcoin Sinks to $66,000 as $1.35B in Long Liquidations Accelerate Market Selloff
2
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Coins to put in your Watchlist This Dip
Coins to put in your Watchlist This Dip
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$BTC Bitcoin has fallen to rank 14 among the world’s top assets by market cap, now sitting around $1.36 TRILLION.
$BTC is still ahead of most major public companies, but the drop shows how much pressure the market is under after the latest pullback.
© Coin Bureau
$BTC Bitcoin has fallen to rank 14 among the world’s top assets by market cap, now sitting around $1.36 TRILLION.
$BTC is still ahead of most major public companies, but the drop shows how much pressure the market is under after the latest pullback.
© Coin Bureau
$BTC is still ahead of most major public companies, but the drop shows how much pressure the market is under after the latest pullback.
© Coin Bureau
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$NVDA $MU $AAPL The U.S. technology sector has surged 42% in the last two months, marking its biggest two-month rally in 24 years as AI and semiconductor stocks lead the market higher.
© Cointelegraph
$NVDA $MU $AAPL The U.S. technology sector has surged 42% in the last two months, marking its biggest two-month rally in 24 years as AI and semiconductor stocks lead the market higher.
© Cointelegraph
© Cointelegraph
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$GOOGL 🚨 LATEST: Google plans an $80 billion stock sale to help fund its AI expansion and growing tax obligations as spending on AI infrastructure accelerates.
$GOOGL 🚨 LATEST: Google plans an $80 billion stock sale to help fund its AI expansion and growing tax obligations as spending on AI infrastructure accelerates.
1
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