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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
8
5
87
71k
Verified
Article
The Power Shift: US Housing Market Sees Record Seller ConcessionsThe real estate market in the United States is experiencing a massive change. For the last few years sellers held all the leverage and buyers just had to accept whatever terms were offered. That dynamic is officially over. Buyers are finally gaining real negotiating power across much of the country. The latest data shows exactly how much the landscape has shifted.
Sellers Are Making Deals
The most telling sign of a cooling market is when sellers start offering financial bonuses to close a deal.
A Record 46 Percent: In May a staggering 46 percent of home sellers offered concessions to buyers. This stands as the highest May reading on record.Doubling Since 2022: This percentage has completely doubled since 2022. It also marks the second consecutive annual increase.The Supply Gap: The US currently has roughly 47 percent more home sellers than buyers. This sits near the highest gap on record. Elevated mortgage rates and overall economic uncertainty continue to weigh heavily on buyer demand.
City By City Breakdown
Real estate is always local. The national average is high but some specific cities are seeing extreme shifts in buyer leverage.
Nashville Leads the Way: An incredible 76 percent of sellers in Nashville gave concessions in May. That is the highest rate among the 28 major metropolitan areas tracked.The Southern Trend: Charlotte followed closely at 71 percent. Atlanta was right behind them at 69 percent.The New York Exception: Other markets remain incredibly tight. By comparison just 3 percent of New York home sellers offered concessions last month.
Some Random Thoughts 💬
We are finally seeing a return to reality in the housing sector. The last few years created an artificial frenzy where people waived inspections and paid way over the asking price. That kind of market behavior was never sustainable. High interest rates are painful for new buyers but they are doing exactly what they are supposed to do. They are cooling down the market and forcing sellers to act reasonably. If you have capital ready and you are looking to buy in places like Nashville or Atlanta this is your moment. You can finally negotiate hard and demand repairs or rate buydowns. The balance of power is officially back in the hands of the buyer.
The Power Shift: US Housing Market Sees Record Seller Concessions
The real estate market in the United States is experiencing a massive change. For the last few years sellers held all the leverage and buyers just had to accept whatever terms were offered. That dynamic is officially over. Buyers are finally gaining real negotiating power across much of the country. The latest data shows exactly how much the landscape has shifted.
Sellers Are Making Deals
The most telling sign of a cooling market is when sellers start offering financial bonuses to close a deal.
A Record 46 Percent: In May a staggering 46 percent of home sellers offered concessions to buyers. This stands as the highest May reading on record.Doubling Since 2022: This percentage has completely doubled since 2022. It also marks the second consecutive annual increase.The Supply Gap: The US currently has roughly 47 percent more home sellers than buyers. This sits near the highest gap on record. Elevated mortgage rates and overall economic uncertainty continue to weigh heavily on buyer demand.
City By City Breakdown
Real estate is always local. The national average is high but some specific cities are seeing extreme shifts in buyer leverage.
Nashville Leads the Way: An incredible 76 percent of sellers in Nashville gave concessions in May. That is the highest rate among the 28 major metropolitan areas tracked.The Southern Trend: Charlotte followed closely at 71 percent. Atlanta was right behind them at 69 percent.The New York Exception: Other markets remain incredibly tight. By comparison just 3 percent of New York home sellers offered concessions last month.
Some Random Thoughts 💬
We are finally seeing a return to reality in the housing sector. The last few years created an artificial frenzy where people waived inspections and paid way over the asking price. That kind of market behavior was never sustainable. High interest rates are painful for new buyers but they are doing exactly what they are supposed to do. They are cooling down the market and forcing sellers to act reasonably. If you have capital ready and you are looking to buy in places like Nashville or Atlanta this is your moment. You can finally negotiate hard and demand repairs or rate buydowns. The balance of power is officially back in the hands of the buyer.
Sellers Are Making Deals
The most telling sign of a cooling market is when sellers start offering financial bonuses to close a deal.
A Record 46 Percent: In May a staggering 46 percent of home sellers offered concessions to buyers. This stands as the highest May reading on record.Doubling Since 2022: This percentage has completely doubled since 2022. It also marks the second consecutive annual increase.The Supply Gap: The US currently has roughly 47 percent more home sellers than buyers. This sits near the highest gap on record. Elevated mortgage rates and overall economic uncertainty continue to weigh heavily on buyer demand.
City By City Breakdown
Real estate is always local. The national average is high but some specific cities are seeing extreme shifts in buyer leverage.
Nashville Leads the Way: An incredible 76 percent of sellers in Nashville gave concessions in May. That is the highest rate among the 28 major metropolitan areas tracked.The Southern Trend: Charlotte followed closely at 71 percent. Atlanta was right behind them at 69 percent.The New York Exception: Other markets remain incredibly tight. By comparison just 3 percent of New York home sellers offered concessions last month.
Some Random Thoughts 💬
We are finally seeing a return to reality in the housing sector. The last few years created an artificial frenzy where people waived inspections and paid way over the asking price. That kind of market behavior was never sustainable. High interest rates are painful for new buyers but they are doing exactly what they are supposed to do. They are cooling down the market and forcing sellers to act reasonably. If you have capital ready and you are looking to buy in places like Nashville or Atlanta this is your moment. You can finally negotiate hard and demand repairs or rate buydowns. The balance of power is officially back in the hands of the buyer.
2
1.4k
$AAPL HOW APPLE'S PRICE HIKE SHOOK GLOBAL MARKETS
$AAPL HOW APPLE'S PRICE HIKE SHOOK GLOBAL MARKETS
AAPLonAlpha
AAPLUS-0.31%
2
508
Partly True
Article
The AI Edge: How Wall Street's Smart Money Is Quietly Rewiring Its ResearchArtificial intelligence is changing the way big money works. For years, people argued about whether algorithmic models could actually beat the market. Today, that debate is over. The largest financial institutions in the world are no longer just experimenting with automation. They are building it directly into their daily research workflows. The data shows that the firms managing billions of dollars are aggressively using AI to find market opportunities before anyone else.
The Ultimate Research Assistant
The biggest adoption of new technology is happening in the research departments of major firms.
The New Baseline: Approximately 52 percent of institutional investors now primarily use artificial intelligence for their daily research tasks. This data comes from a fresh Barclays survey that tracked 410 fixed income investors.Hedge Funds Take Notice: Right behind them, roughly 44 percent of hedge funds use AI as their main tool to process and analyze massive amounts of complex market data. They are using these models to spot trends that human eyes simply miss.
Modelling and Managing Risk
Big money managers are also letting software handle their worst case scenario planning.
The Hedge Fund Split: About 27 percent of hedge funds now deploy artificial intelligence specifically for financial modelling and risk analysis.Long Only Managers: Traditional long only managers are moving a bit slower, with roughly 22 percent using the technology for risk.Asset Owners: The groups that actually own the underlying assets sit at the bottom of the list, with only 17 percent trusting AI for risk management.
The Administrative Ceiling
Even though the technology is expanding quickly, certain parts of the financial world are keeping it on a tight leash.
The Ten Percent Zone: Operations, compliance, reporting, and final investment decisions each account for just 10 percent to 15 percent of AI usage across all these financial groups.Human Guardrails: Wall Street is perfectly happy letting software read documents and crunch data, but they still want a human hand on the trigger when it comes to compliance and final capital deployment.
Some Random Thoughts 💬
The race for information has always determined who wins on Wall Street. When more than half of institutional investors use artificial intelligence for research, it completely changes the game for retail traders. These big firms are using models to read thousands of earnings reports and blockchain transactions in seconds. In the crypto and DeFi spaces, we see a parallel shift where onchain data tools are becoming mandatory.
This massive corporate adoption proves that AI is not a gimmick. It is the new baseline for finding alpha. If you are still trying to analyze markets using basic charts and manual reading, you are competing against machines that never sleep. The information edge has officially moved to the code.
The AI Edge: How Wall Street's Smart Money Is Quietly Rewiring Its Research
Artificial intelligence is changing the way big money works. For years, people argued about whether algorithmic models could actually beat the market. Today, that debate is over. The largest financial institutions in the world are no longer just experimenting with automation. They are building it directly into their daily research workflows. The data shows that the firms managing billions of dollars are aggressively using AI to find market opportunities before anyone else.
The Ultimate Research Assistant
The biggest adoption of new technology is happening in the research departments of major firms.
The New Baseline: Approximately 52 percent of institutional investors now primarily use artificial intelligence for their daily research tasks. This data comes from a fresh Barclays survey that tracked 410 fixed income investors.Hedge Funds Take Notice: Right behind them, roughly 44 percent of hedge funds use AI as their main tool to process and analyze massive amounts of complex market data. They are using these models to spot trends that human eyes simply miss.
Modelling and Managing Risk
Big money managers are also letting software handle their worst case scenario planning.
The Hedge Fund Split: About 27 percent of hedge funds now deploy artificial intelligence specifically for financial modelling and risk analysis.Long Only Managers: Traditional long only managers are moving a bit slower, with roughly 22 percent using the technology for risk.Asset Owners: The groups that actually own the underlying assets sit at the bottom of the list, with only 17 percent trusting AI for risk management.
The Administrative Ceiling
Even though the technology is expanding quickly, certain parts of the financial world are keeping it on a tight leash.
The Ten Percent Zone: Operations, compliance, reporting, and final investment decisions each account for just 10 percent to 15 percent of AI usage across all these financial groups.Human Guardrails: Wall Street is perfectly happy letting software read documents and crunch data, but they still want a human hand on the trigger when it comes to compliance and final capital deployment.
Some Random Thoughts 💬
The race for information has always determined who wins on Wall Street. When more than half of institutional investors use artificial intelligence for research, it completely changes the game for retail traders. These big firms are using models to read thousands of earnings reports and blockchain transactions in seconds. In the crypto and DeFi spaces, we see a parallel shift where onchain data tools are becoming mandatory.
This massive corporate adoption proves that AI is not a gimmick. It is the new baseline for finding alpha. If you are still trying to analyze markets using basic charts and manual reading, you are competing against machines that never sleep. The information edge has officially moved to the code.
The Ultimate Research Assistant
The biggest adoption of new technology is happening in the research departments of major firms.
The New Baseline: Approximately 52 percent of institutional investors now primarily use artificial intelligence for their daily research tasks. This data comes from a fresh Barclays survey that tracked 410 fixed income investors.Hedge Funds Take Notice: Right behind them, roughly 44 percent of hedge funds use AI as their main tool to process and analyze massive amounts of complex market data. They are using these models to spot trends that human eyes simply miss.
Modelling and Managing Risk
Big money managers are also letting software handle their worst case scenario planning.
The Hedge Fund Split: About 27 percent of hedge funds now deploy artificial intelligence specifically for financial modelling and risk analysis.Long Only Managers: Traditional long only managers are moving a bit slower, with roughly 22 percent using the technology for risk.Asset Owners: The groups that actually own the underlying assets sit at the bottom of the list, with only 17 percent trusting AI for risk management.
The Administrative Ceiling
Even though the technology is expanding quickly, certain parts of the financial world are keeping it on a tight leash.
The Ten Percent Zone: Operations, compliance, reporting, and final investment decisions each account for just 10 percent to 15 percent of AI usage across all these financial groups.Human Guardrails: Wall Street is perfectly happy letting software read documents and crunch data, but they still want a human hand on the trigger when it comes to compliance and final capital deployment.
Some Random Thoughts 💬
The race for information has always determined who wins on Wall Street. When more than half of institutional investors use artificial intelligence for research, it completely changes the game for retail traders. These big firms are using models to read thousands of earnings reports and blockchain transactions in seconds. In the crypto and DeFi spaces, we see a parallel shift where onchain data tools are becoming mandatory.
This massive corporate adoption proves that AI is not a gimmick. It is the new baseline for finding alpha. If you are still trying to analyze markets using basic charts and manual reading, you are competing against machines that never sleep. The information edge has officially moved to the code.
2
1.7k
$XRP 90-𝙙𝙖𝙮 𝙍𝙚𝙖𝙡𝙞𝙯𝙚𝙙 𝙋𝙧𝙤𝙛𝙞𝙩/𝙇𝙤𝙨𝙨 𝙍𝙖𝙩𝙞𝙤 𝙞𝙨 𝙣𝙤𝙬 𝙗𝙚𝙡𝙤𝙬 1
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That means investors are realizing more losses than profits, a condition that has historically coincided with key accumulation opportunities.
© Glassnode
$XRP 90-𝙙𝙖𝙮 𝙍𝙚𝙖𝙡𝙞𝙯𝙚𝙙 𝙋𝙧𝙤𝙛𝙞𝙩/𝙇𝙤𝙨𝙨 𝙍𝙖𝙩𝙞𝙤 𝙞𝙨 𝙣𝙤𝙬 𝙗𝙚𝙡𝙤𝙬 1
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That means investors are realizing more losses than profits, a condition that has historically coincided with key accumulation opportunities.
© Glassnode
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That means investors are realizing more losses than profits, a condition that has historically coincided with key accumulation opportunities.
© Glassnode
1
1
1.2k
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙬𝙝𝙖𝙡𝙚𝙨 𝙨𝙝𝙤𝙬𝙞𝙣𝙜 𝙡𝙞𝙛𝙚, 𝙖𝙘𝙘𝙪𝙢𝙪𝙡𝙖𝙩𝙞𝙤𝙣 𝙝𝙚𝙡𝙥𝙨 𝙡𝙞𝙛𝙩 𝙥𝙧𝙞𝙘𝙚 𝙗𝙖𝙘𝙠 𝙖𝙗𝙤𝙫𝙚 $60𝙆
-
🐳 Bitcoin has reclaimed $60K after briefly dipping below the key psychological level, and whales wasted little time stepping in. The network just recorded its second-largest whale activity spike in the past two months, with 6,920 transactions over $100K and 1,438 over $1M. Historically, this kind of activity often shows up when big players see opportunity while the crowd is fearful.
📈 Whale spikes don’t guarantee an immediate bounce, but they’re always worth watching after a sharp selloff. If large holders are accumulating while retail remains cautious, it could be a sign that confidence behind the scenes is much stronger than the recent price action suggests.
© Santiment
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙬𝙝𝙖𝙡𝙚𝙨 𝙨𝙝𝙤𝙬𝙞𝙣𝙜 𝙡𝙞𝙛𝙚, 𝙖𝙘𝙘𝙪𝙢𝙪𝙡𝙖𝙩𝙞𝙤𝙣 𝙝𝙚𝙡𝙥𝙨 𝙡𝙞𝙛𝙩 𝙥𝙧𝙞𝙘𝙚 𝙗𝙖𝙘𝙠 𝙖𝙗𝙤𝙫𝙚 $60𝙆
-
🐳 Bitcoin has reclaimed $60K after briefly dipping below the key psychological level, and whales wasted little time stepping in. The network just recorded its second-largest whale activity spike in the past two months, with 6,920 transactions over $100K and 1,438 over $1M. Historically, this kind of activity often shows up when big players see opportunity while the crowd is fearful.
📈 Whale spikes don’t guarantee an immediate bounce, but they’re always worth watching after a sharp selloff. If large holders are accumulating while retail remains cautious, it could be a sign that confidence behind the scenes is much stronger than the recent price action suggests.
© Santiment
-
🐳 Bitcoin has reclaimed $60K after briefly dipping below the key psychological level, and whales wasted little time stepping in. The network just recorded its second-largest whale activity spike in the past two months, with 6,920 transactions over $100K and 1,438 over $1M. Historically, this kind of activity often shows up when big players see opportunity while the crowd is fearful.
📈 Whale spikes don’t guarantee an immediate bounce, but they’re always worth watching after a sharp selloff. If large holders are accumulating while retail remains cautious, it could be a sign that confidence behind the scenes is much stronger than the recent price action suggests.
© Santiment
2
285
$AVAX AVAX saw 707K new addresses in Q2. 6X more than Q1.
$AVAX AVAX saw 707K new addresses in Q2. 6X more than Q1.
1
154
$BTC BITCOIN ETFS POST THEIR WORST WEEKLY OUTFLOW EVER
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A record $1.79 BILLION exited U.S. spot Bitcoin ETFs this week, with BlackRock's IBIT alone accounting for $1.3B of the outflows.
© SoSoValue
$BTC BITCOIN ETFS POST THEIR WORST WEEKLY OUTFLOW EVER
-
A record $1.79 BILLION exited U.S. spot Bitcoin ETFs this week, with BlackRock's IBIT alone accounting for $1.3B of the outflows.
© SoSoValue
-
A record $1.79 BILLION exited U.S. spot Bitcoin ETFs this week, with BlackRock's IBIT alone accounting for $1.3B of the outflows.
© SoSoValue
1
2
1.2k
$SOL Tokenized stock hype is helping Solana decouple from the broader crypto market, per Santiment
$SOL Tokenized stock hype is helping Solana decouple from the broader crypto market, per Santiment
2
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𝙐.𝙎. 𝘿𝘼𝙏𝘼 𝘾𝙀𝙉𝙏𝙀𝙍 𝙎𝙋𝙀𝙉𝘿𝙄𝙉𝙂 𝙉𝙊𝙒 𝙎𝙐𝙍𝙋𝘼𝙎𝙎𝙀𝙎 𝙈𝙊𝙎𝙏 𝙄𝙉𝙁𝙍𝘼𝙎𝙏𝙍𝙐𝘾𝙏𝙐𝙍𝙀 𝙋𝙍𝙊𝙅𝙀𝘾𝙏𝙎
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U.S. spending on data center construction has reached $50 BILLION, now exceeding the COMBINED spending on airports, ports, and mass transit, per Bloomberg.
The AI infrastructure boom continues to accelerate, with US data center construction spending up 357% since 2022 and now accounting for 2.3% of all U.S. construction spending.
© Coin Bureau
𝙐.𝙎. 𝘿𝘼𝙏𝘼 𝘾𝙀𝙉𝙏𝙀𝙍 𝙎𝙋𝙀𝙉𝘿𝙄𝙉𝙂 𝙉𝙊𝙒 𝙎𝙐𝙍𝙋𝘼𝙎𝙎𝙀𝙎 𝙈𝙊𝙎𝙏 𝙄𝙉𝙁𝙍𝘼𝙎𝙏𝙍𝙐𝘾𝙏𝙐𝙍𝙀 𝙋𝙍𝙊𝙅𝙀𝘾𝙏𝙎
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U.S. spending on data center construction has reached $50 BILLION, now exceeding the COMBINED spending on airports, ports, and mass transit, per Bloomberg.
The AI infrastructure boom continues to accelerate, with US data center construction spending up 357% since 2022 and now accounting for 2.3% of all U.S. construction spending.
© Coin Bureau
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U.S. spending on data center construction has reached $50 BILLION, now exceeding the COMBINED spending on airports, ports, and mass transit, per Bloomberg.
The AI infrastructure boom continues to accelerate, with US data center construction spending up 357% since 2022 and now accounting for 2.3% of all U.S. construction spending.
© Coin Bureau
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135
$BTC 𝙂𝙍𝘼𝙔𝙎𝘾𝘼𝙇𝙀: 𝙎𝙏𝙍𝘼𝙏𝙀𝙂𝙔 𝙎𝙃𝙊𝙐𝙇𝘿 𝙎𝙀𝙇𝙇 $3 𝘽𝙄𝙇𝙇𝙄𝙊𝙉 𝙄𝙉 𝘽𝙏𝘾
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Grayscale’s Zach Pandl said Strategy should SELL at least $3 BILLION in BTC could cover most of its cash obligations for the next two years and restore market confidence.
Strategy now faces around $1.2 BILLION in annual preferred dividend obligations, while $STRC trades nearly 29% below par.
© Coin Bureau
$BTC 𝙂𝙍𝘼𝙔𝙎𝘾𝘼𝙇𝙀: 𝙎𝙏𝙍𝘼𝙏𝙀𝙂𝙔 𝙎𝙃𝙊𝙐𝙇𝘿 𝙎𝙀𝙇𝙇 $3 𝘽𝙄𝙇𝙇𝙄𝙊𝙉 𝙄𝙉 𝘽𝙏𝘾
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Grayscale’s Zach Pandl said Strategy should SELL at least $3 BILLION in BTC could cover most of its cash obligations for the next two years and restore market confidence.
Strategy now faces around $1.2 BILLION in annual preferred dividend obligations, while $STRC trades nearly 29% below par.
© Coin Bureau
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Grayscale’s Zach Pandl said Strategy should SELL at least $3 BILLION in BTC could cover most of its cash obligations for the next two years and restore market confidence.
Strategy now faces around $1.2 BILLION in annual preferred dividend obligations, while $STRC trades nearly 29% below par.
© Coin Bureau
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100
𝙂𝙀𝙉 𝙕'𝙎 𝙒𝙊𝙍𝙎𝙏 𝙁𝙀𝘼𝙍 𝘾𝙊𝙉𝙁𝙄𝙍𝙈𝙀𝘿 𝘼𝙎 1 𝙄𝙉 3 𝙀𝙈𝙋𝙇𝙊𝙔𝙀𝙍𝙎 𝙍𝙀𝙋𝙇𝘼𝘾𝙀 𝙀𝙉𝙏𝙍𝙔-𝙇𝙀𝙑𝙀𝙇 𝙍𝙊𝙇𝙀𝙎 𝙒𝙄𝙏𝙃 𝘼𝙄
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A survey of over 600 corporate recruiters found that one-third are already replacing entry-level positions with AI, with tech leading at 40% per Fortune.
The roles disappearing first are in coding, data processing, and customer service, where AI can now handle the routine tasks that used to go to new hires.
Graduate school applications surged 13% but an MBA "may no longer be the escape hatch it once was."
© Coin Bureau
𝙂𝙀𝙉 𝙕'𝙎 𝙒𝙊𝙍𝙎𝙏 𝙁𝙀𝘼𝙍 𝘾𝙊𝙉𝙁𝙄𝙍𝙈𝙀𝘿 𝘼𝙎 1 𝙄𝙉 3 𝙀𝙈𝙋𝙇𝙊𝙔𝙀𝙍𝙎 𝙍𝙀𝙋𝙇𝘼𝘾𝙀 𝙀𝙉𝙏𝙍𝙔-𝙇𝙀𝙑𝙀𝙇 𝙍𝙊𝙇𝙀𝙎 𝙒𝙄𝙏𝙃 𝘼𝙄
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A survey of over 600 corporate recruiters found that one-third are already replacing entry-level positions with AI, with tech leading at 40% per Fortune.
The roles disappearing first are in coding, data processing, and customer service, where AI can now handle the routine tasks that used to go to new hires.
Graduate school applications surged 13% but an MBA "may no longer be the escape hatch it once was."
© Coin Bureau
-
A survey of over 600 corporate recruiters found that one-third are already replacing entry-level positions with AI, with tech leading at 40% per Fortune.
The roles disappearing first are in coding, data processing, and customer service, where AI can now handle the routine tasks that used to go to new hires.
Graduate school applications surged 13% but an MBA "may no longer be the escape hatch it once was."
© Coin Bureau
2
2.1k
Verified
$UNI 𝙃𝙤𝙡𝙙𝙚𝙧 𝙧𝙚𝙩𝙚𝙣𝙩𝙞𝙤𝙣 𝙞𝙨 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡 𝙩𝙚𝙨𝙩 𝙖𝙛𝙩𝙚𝙧 𝙩𝙝𝙚 𝙞𝙣𝙘𝙚𝙣𝙩𝙞𝙫𝙚 𝙘𝙮𝙘𝙡𝙚 𝙚𝙣𝙙𝙨
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Glassnode’s UNI holder-retention chart tracks retained, new, resurrected, and churned holders, alongside UNI price, from 2022 to 2025.
This way, Web3 retention should be measured as repeat behavior and capital kept, not just wallet counts.
© Glassnode
$UNI 𝙃𝙤𝙡𝙙𝙚𝙧 𝙧𝙚𝙩𝙚𝙣𝙩𝙞𝙤𝙣 𝙞𝙨 𝙩𝙝𝙚 𝙧𝙚𝙖𝙡 𝙩𝙚𝙨𝙩 𝙖𝙛𝙩𝙚𝙧 𝙩𝙝𝙚 𝙞𝙣𝙘𝙚𝙣𝙩𝙞𝙫𝙚 𝙘𝙮𝙘𝙡𝙚 𝙚𝙣𝙙𝙨
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Glassnode’s UNI holder-retention chart tracks retained, new, resurrected, and churned holders, alongside UNI price, from 2022 to 2025.
This way, Web3 retention should be measured as repeat behavior and capital kept, not just wallet counts.
© Glassnode
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Glassnode’s UNI holder-retention chart tracks retained, new, resurrected, and churned holders, alongside UNI price, from 2022 to 2025.
This way, Web3 retention should be measured as repeat behavior and capital kept, not just wallet counts.
© Glassnode
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3k
$XRP 𝙂𝙚𝙩𝙩𝙞𝙣𝙜 𝙩𝙝𝙚 𝙗𝙞𝙜𝙜𝙚𝙨𝙩 𝙐𝙥𝙙𝙖𝙩𝙚, 𝙒𝙚 𝙬𝙞𝙡𝙡 𝙨𝙚𝙚 2$ 𝙨𝙤𝙤𝙣
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Polymarket is turning the 2026 World Cup into a massive onboarding event for crypto. We are seeing prediction markets capture attention that traditional exchanges usually miss.
The volume numbers are actually crazy right now. Over $2 billion has already been traded on World Cup outcomes alone. A recent study showed that 60% of the people making these forecasts had never used crypto before. They are not looking to trade complex DeFi assets. They just want to back their favorite teams like France or Argentina. Polymarket removed the friction of wallets and gas fees so these users can just participate.
Forecast, hedge, and scale. That is the current reality.
Looking at the landscape, $XRP delivers rapid cross border settlement, $ADA focuses on smart contract security, and $HYPE scales decentralized perpetual trading. Polymarket goes deeper by adding crowdsourced truth paired with real cultural events. That is where the real edge is.
Tracking market intelligence reveals exactly why this platform is dominating. The platform handled $26.2 billion in total volume during the first quarter of this year. Wall Street is clearly paying attention to this growth.
Intercontinental Exchange even planned a $2 billion investment at an $8 billion valuation.
Institutions and retail users are both using this data to gauge real time probabilities.
We are watching prediction markets transition from a niche experiment into standard financial infrastructure. This is exactly where the real utility lies.
B U L L I S H 🥂 Polymarket
$XRP 𝙂𝙚𝙩𝙩𝙞𝙣𝙜 𝙩𝙝𝙚 𝙗𝙞𝙜𝙜𝙚𝙨𝙩 𝙐𝙥𝙙𝙖𝙩𝙚, 𝙒𝙚 𝙬𝙞𝙡𝙡 𝙨𝙚𝙚 2$ 𝙨𝙤𝙤𝙣
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Polymarket is turning the 2026 World Cup into a massive onboarding event for crypto. We are seeing prediction markets capture attention that traditional exchanges usually miss.
The volume numbers are actually crazy right now. Over $2 billion has already been traded on World Cup outcomes alone. A recent study showed that 60% of the people making these forecasts had never used crypto before. They are not looking to trade complex DeFi assets. They just want to back their favorite teams like France or Argentina. Polymarket removed the friction of wallets and gas fees so these users can just participate.
Forecast, hedge, and scale. That is the current reality.
Looking at the landscape, $XRP delivers rapid cross border settlement, $ADA focuses on smart contract security, and $HYPE scales decentralized perpetual trading. Polymarket goes deeper by adding crowdsourced truth paired with real cultural events. That is where the real edge is.
Tracking market intelligence reveals exactly why this platform is dominating. The platform handled $26.2 billion in total volume during the first quarter of this year. Wall Street is clearly paying attention to this growth.
Intercontinental Exchange even planned a $2 billion investment at an $8 billion valuation.
Institutions and retail users are both using this data to gauge real time probabilities.
We are watching prediction markets transition from a niche experiment into standard financial infrastructure. This is exactly where the real utility lies.
B U L L I S H 🥂 Polymarket
-
Polymarket is turning the 2026 World Cup into a massive onboarding event for crypto. We are seeing prediction markets capture attention that traditional exchanges usually miss.
The volume numbers are actually crazy right now. Over $2 billion has already been traded on World Cup outcomes alone. A recent study showed that 60% of the people making these forecasts had never used crypto before. They are not looking to trade complex DeFi assets. They just want to back their favorite teams like France or Argentina. Polymarket removed the friction of wallets and gas fees so these users can just participate.
Forecast, hedge, and scale. That is the current reality.
Looking at the landscape, $XRP delivers rapid cross border settlement, $ADA focuses on smart contract security, and $HYPE scales decentralized perpetual trading. Polymarket goes deeper by adding crowdsourced truth paired with real cultural events. That is where the real edge is.
Tracking market intelligence reveals exactly why this platform is dominating. The platform handled $26.2 billion in total volume during the first quarter of this year. Wall Street is clearly paying attention to this growth.
Intercontinental Exchange even planned a $2 billion investment at an $8 billion valuation.
Institutions and retail users are both using this data to gauge real time probabilities.
We are watching prediction markets transition from a niche experiment into standard financial infrastructure. This is exactly where the real utility lies.
B U L L I S H 🥂 Polymarket
1
3
2.7k
Verified
$ADA 𝘾𝙖𝙧𝙙𝙖𝙣𝙤 𝙖𝙘𝙩𝙞𝙫𝙚 𝙖𝙙𝙙𝙧𝙚𝙨𝙨𝙚𝙨 𝙖𝙣𝙙 𝙙𝙞𝙨𝙘𝙪𝙨𝙨𝙞𝙤𝙣𝙨 𝙨𝙥𝙞𝙠𝙚 𝙖𝙨 𝙥𝙧𝙞𝙘𝙚 𝙝𝙞𝙩𝙨 𝙡𝙤𝙬𝙚𝙨𝙩 𝙡𝙚𝙫𝙚𝙡 𝙨𝙞𝙣𝙘𝙚 2020
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🔥 Cardano has suddenly become one of crypto’s biggest conversation pieces as on-chain activity explodes for a second time this month, even while $ADA trades near its lowest price since December, 2020. Daily active addresses and social dominance have both surged as traders react to the extreme volatility facing crypto’s #18 market cap.
📉 Much of the growing FUD stems from Charles Hoskinson’s recent warnings that more Cardano projects could fail, his decision to step back from public involvement, and ongoing governance disputes over treasury funding that have divided the community.
© Santiment
$ADA 𝘾𝙖𝙧𝙙𝙖𝙣𝙤 𝙖𝙘𝙩𝙞𝙫𝙚 𝙖𝙙𝙙𝙧𝙚𝙨𝙨𝙚𝙨 𝙖𝙣𝙙 𝙙𝙞𝙨𝙘𝙪𝙨𝙨𝙞𝙤𝙣𝙨 𝙨𝙥𝙞𝙠𝙚 𝙖𝙨 𝙥𝙧𝙞𝙘𝙚 𝙝𝙞𝙩𝙨 𝙡𝙤𝙬𝙚𝙨𝙩 𝙡𝙚𝙫𝙚𝙡 𝙨𝙞𝙣𝙘𝙚 2020
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🔥 Cardano has suddenly become one of crypto’s biggest conversation pieces as on-chain activity explodes for a second time this month, even while $ADA trades near its lowest price since December, 2020. Daily active addresses and social dominance have both surged as traders react to the extreme volatility facing crypto’s #18 market cap.
📉 Much of the growing FUD stems from Charles Hoskinson’s recent warnings that more Cardano projects could fail, his decision to step back from public involvement, and ongoing governance disputes over treasury funding that have divided the community.
© Santiment
-
🔥 Cardano has suddenly become one of crypto’s biggest conversation pieces as on-chain activity explodes for a second time this month, even while $ADA trades near its lowest price since December, 2020. Daily active addresses and social dominance have both surged as traders react to the extreme volatility facing crypto’s #18 market cap.
📉 Much of the growing FUD stems from Charles Hoskinson’s recent warnings that more Cardano projects could fail, his decision to step back from public involvement, and ongoing governance disputes over treasury funding that have divided the community.
© Santiment
2
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1.4k
𝘽𝙞𝙣𝙖𝙣𝙘𝙚 𝙎𝙪𝙨𝙥𝙚𝙣𝙙𝙨 𝙀𝙐 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙨 𝘼𝙛𝙩𝙚𝙧 𝙈𝙞𝙨𝙨𝙞𝙣𝙜 𝙈𝙞𝘾𝘼 𝘿𝙚𝙖𝙙𝙡𝙞𝙣𝙚
𝘽𝙞𝙣𝙖𝙣𝙘𝙚 𝙎𝙪𝙨𝙥𝙚𝙣𝙙𝙨 𝙀𝙐 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙨 𝘼𝙛𝙩𝙚𝙧 𝙈𝙞𝙨𝙨𝙞𝙣𝙜 𝙈𝙞𝘾𝘼 𝘿𝙚𝙖𝙙𝙡𝙞𝙣𝙚
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2k
𝐔𝐩𝐜𝐨𝐦𝐢𝐧𝐠 𝐓𝐨𝐤𝐞𝐧 𝐔𝐧𝐥𝐨𝐜𝐤𝐬 𝐉𝐮𝐥𝐲 1–31, 2026
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$RAIN → $812M on July 11
$HYPE → $630M on July 6
$PUMP → $117M on July 12
$CC → $95.3M ongoing daily
$WLD → $64.9M ongoing daily
$GRAM → $57.5M on July 23
$BEAT → $49.7M on July 1
$TRUMP → $46.0M ongoing daily
$M → $39.2M on July 3
$ADI → $33.6M on July 9
© Top7ICO
𝐔𝐩𝐜𝐨𝐦𝐢𝐧𝐠 𝐓𝐨𝐤𝐞𝐧 𝐔𝐧𝐥𝐨𝐜𝐤𝐬 𝐉𝐮𝐥𝐲 1–31, 2026
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$RAIN → $812M on July 11
$HYPE → $630M on July 6
$PUMP → $117M on July 12
$CC → $95.3M ongoing daily
$WLD → $64.9M ongoing daily
$GRAM → $57.5M on July 23
$BEAT → $49.7M on July 1
$TRUMP → $46.0M ongoing daily
$M → $39.2M on July 3
$ADI → $33.6M on July 9
© Top7ICO
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$RAIN → $812M on July 11
$HYPE → $630M on July 6
$PUMP → $117M on July 12
$CC → $95.3M ongoing daily
$WLD → $64.9M ongoing daily
$GRAM → $57.5M on July 23
$BEAT → $49.7M on July 1
$TRUMP → $46.0M ongoing daily
$M → $39.2M on July 3
$ADI → $33.6M on July 9
© Top7ICO
1
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$BTC $ETH 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙖𝙣𝙙 𝙚𝙩𝙝𝙚𝙧 𝙀𝙏𝙁𝙨 𝙗𝙡𝙚𝙚𝙙 ≈$500𝙈 𝙞𝙣 𝙤𝙪𝙩𝙛𝙡𝙤𝙬𝙨 𝙤𝙣 𝙅𝙪𝙣𝙚 24
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Spot Bitcoin ETFs saw $469M in net outflows on June 24, while Ethereum spot ETFs lost another $30.2M.
Fidelity's FETH led ETH outflows with $15.7M, while Grayscale's Bitcoin Mini Trust was the only notable inflow at $23.6M.
© Coinglass
$BTC $ETH 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙖𝙣𝙙 𝙚𝙩𝙝𝙚𝙧 𝙀𝙏𝙁𝙨 𝙗𝙡𝙚𝙚𝙙 ≈$500𝙈 𝙞𝙣 𝙤𝙪𝙩𝙛𝙡𝙤𝙬𝙨 𝙤𝙣 𝙅𝙪𝙣𝙚 24
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Spot Bitcoin ETFs saw $469M in net outflows on June 24, while Ethereum spot ETFs lost another $30.2M.
Fidelity's FETH led ETH outflows with $15.7M, while Grayscale's Bitcoin Mini Trust was the only notable inflow at $23.6M.
© Coinglass
-
Spot Bitcoin ETFs saw $469M in net outflows on June 24, while Ethereum spot ETFs lost another $30.2M.
Fidelity's FETH led ETH outflows with $15.7M, while Grayscale's Bitcoin Mini Trust was the only notable inflow at $23.6M.
© Coinglass
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$ETH ETH is about to see its worst 3-quarter run ever
$ETH ETH is about to see its worst 3-quarter run ever
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