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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
$SPCX Worlds First Trillion-aire
$SPCX Worlds First Trillion-aire
1
652
Verified
$SPCX SPACEX EXPECTED TO CLOSE ABOVE A $2 TRILLION MARKET CAP
-
Polymarket traders now price a 71% chance that SpaceX finishes its IPO debut valued above $2 TRILLION.
© Coin Bureau
$SPCX SPACEX EXPECTED TO CLOSE ABOVE A $2 TRILLION MARKET CAP
-
Polymarket traders now price a 71% chance that SpaceX finishes its IPO debut valued above $2 TRILLION.
© Coin Bureau
-
Polymarket traders now price a 71% chance that SpaceX finishes its IPO debut valued above $2 TRILLION.
© Coin Bureau
2
2.6k
$SPCX 𝙃𝙀 𝙅𝙐𝙎𝙏 𝙀𝙉𝙏𝙀𝙍𝙀𝘿 𝙏𝙃𝙀 𝙇𝘼𝙍𝙂𝙀𝙎𝙏 𝙎𝙋𝘼𝘾𝙀𝙓 𝙎𝙃𝙊𝙍𝙏 𝙀𝙑𝙀𝙍
-
The largest SpaceX shorter “wenyu8888888”, just bet his entire account on a $5.7 Million 2x short of SPCX pre-IPO.
SpaceX goes live for public trading tomorrow. Will wenyu be right?
$SPCX 𝙃𝙀 𝙅𝙐𝙎𝙏 𝙀𝙉𝙏𝙀𝙍𝙀𝘿 𝙏𝙃𝙀 𝙇𝘼𝙍𝙂𝙀𝙎𝙏 𝙎𝙋𝘼𝘾𝙀𝙓 𝙎𝙃𝙊𝙍𝙏 𝙀𝙑𝙀𝙍
-
The largest SpaceX shorter “wenyu8888888”, just bet his entire account on a $5.7 Million 2x short of SPCX pre-IPO.
SpaceX goes live for public trading tomorrow. Will wenyu be right?
-
The largest SpaceX shorter “wenyu8888888”, just bet his entire account on a $5.7 Million 2x short of SPCX pre-IPO.
SpaceX goes live for public trading tomorrow. Will wenyu be right?
4
1.5k
Verified
$PYTH 𝙋𝙧𝙤 𝙞𝙨 𝙝𝙚𝙧𝙚, 𝙩𝙝𝙚 𝙧𝙚𝙫𝙤𝙡𝙪𝙩𝙞𝙤𝙣𝙖𝙧𝙮 𝙤𝙧𝙖𝙘𝙡𝙚 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣 𝙛𝙤𝙧 𝙞𝙣𝙨𝙩𝙞𝙩𝙪𝙩𝙞𝙤𝙣𝙖𝙡 𝙢𝙖𝙧𝙠𝙚𝙩 𝙙𝙖𝙩𝙖
-
The era of free data is officially over, and the network is now capturing real revenue.
DeFi oracles always struggled with unsustainable token emissions and zero value capture. Pyth solves this through a massive infrastructure pivot with the launch of Pyth Pro and Pyth Indices.
The network now offers 24/7 continuous pricing for US equities, metals, and oil, even when traditional markets are closed. By requiring subscriptions starting at $500 per month, they route direct value back to the Pyth DAO.
The new standard is clear: aggregate, distribute, and monetize. This is exactly how the network secures institutional demand through tier-based data plans.
Looking at the landscape, $XRP delivers rapid cross-border settlement, $ADA focuses on peer-reviewed smart contract security, and $HYPE scales decentralized perpetual trading. Pyth goes deeper by adding direct institutional data paired with a revenue-based economic model. They co-developed equity index futures with MarketVector to bring traditional assets on-chain. That is where the real edge is, separating Pyth from standard decentralized nodes.
The numbers show serious momentum as the legacy Pyth Core upgrades on July 31st to finalize this commercial transition. The network is winding down token reward emissions and using protocol revenue to execute open market purchases.
Institutions finally have continuous benchmarks for assets where the underlying market closes. This adoption combined with direct subscription mechanics creates undeniable token utility. This is the moment where DeFi oracles stop relying on inflation and become profitable data businesses.
B U L L I S H 🥂 Pyth
$PYTH 𝙋𝙧𝙤 𝙞𝙨 𝙝𝙚𝙧𝙚, 𝙩𝙝𝙚 𝙧𝙚𝙫𝙤𝙡𝙪𝙩𝙞𝙤𝙣𝙖𝙧𝙮 𝙤𝙧𝙖𝙘𝙡𝙚 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣 𝙛𝙤𝙧 𝙞𝙣𝙨𝙩𝙞𝙩𝙪𝙩𝙞𝙤𝙣𝙖𝙡 𝙢𝙖𝙧𝙠𝙚𝙩 𝙙𝙖𝙩𝙖
-
The era of free data is officially over, and the network is now capturing real revenue.
DeFi oracles always struggled with unsustainable token emissions and zero value capture. Pyth solves this through a massive infrastructure pivot with the launch of Pyth Pro and Pyth Indices.
The network now offers 24/7 continuous pricing for US equities, metals, and oil, even when traditional markets are closed. By requiring subscriptions starting at $500 per month, they route direct value back to the Pyth DAO.
The new standard is clear: aggregate, distribute, and monetize. This is exactly how the network secures institutional demand through tier-based data plans.
Looking at the landscape, $XRP delivers rapid cross-border settlement, $ADA focuses on peer-reviewed smart contract security, and $HYPE scales decentralized perpetual trading. Pyth goes deeper by adding direct institutional data paired with a revenue-based economic model. They co-developed equity index futures with MarketVector to bring traditional assets on-chain. That is where the real edge is, separating Pyth from standard decentralized nodes.
The numbers show serious momentum as the legacy Pyth Core upgrades on July 31st to finalize this commercial transition. The network is winding down token reward emissions and using protocol revenue to execute open market purchases.
Institutions finally have continuous benchmarks for assets where the underlying market closes. This adoption combined with direct subscription mechanics creates undeniable token utility. This is the moment where DeFi oracles stop relying on inflation and become profitable data businesses.
B U L L I S H 🥂 Pyth
-
The era of free data is officially over, and the network is now capturing real revenue.
DeFi oracles always struggled with unsustainable token emissions and zero value capture. Pyth solves this through a massive infrastructure pivot with the launch of Pyth Pro and Pyth Indices.
The network now offers 24/7 continuous pricing for US equities, metals, and oil, even when traditional markets are closed. By requiring subscriptions starting at $500 per month, they route direct value back to the Pyth DAO.
The new standard is clear: aggregate, distribute, and monetize. This is exactly how the network secures institutional demand through tier-based data plans.
Looking at the landscape, $XRP delivers rapid cross-border settlement, $ADA focuses on peer-reviewed smart contract security, and $HYPE scales decentralized perpetual trading. Pyth goes deeper by adding direct institutional data paired with a revenue-based economic model. They co-developed equity index futures with MarketVector to bring traditional assets on-chain. That is where the real edge is, separating Pyth from standard decentralized nodes.
The numbers show serious momentum as the legacy Pyth Core upgrades on July 31st to finalize this commercial transition. The network is winding down token reward emissions and using protocol revenue to execute open market purchases.
Institutions finally have continuous benchmarks for assets where the underlying market closes. This adoption combined with direct subscription mechanics creates undeniable token utility. This is the moment where DeFi oracles stop relying on inflation and become profitable data businesses.
B U L L I S H 🥂 Pyth
2
1.5k
Verified
Article
An Introduction to Binance bStocksWell it is stock time. It feels strange because we are fully in the trenches of crypto and suddenly everyone is talking about stocks. Sad but true, the traditional stock market outperformed the crypto market by a huge margin the last few years and crypto traders like us are waiting for our turn. But many of us do both and want benefits from the two from one place. This my friend is a tricky favor to ask.
To trade crypto and stock at one place is the ultimate satisfaction for any trader and especially for lazy traders like me, who always want many things served on a single platter.
Enter Binance bStocks.
❍ What Are Binance bStocks?
Think of your favorite US companies. Tesla, Nvidia, Circle, and Micron. Now imagine buying their shares just like you buy Bitcoin or Ethereum on a weekend. That is exactly what Binance bStocks brings to the table. These are tokenized securities. Every single bStock token you hold is backed 1:1 by a real US share sitting safely with a regulated custodian.
You are not buying a synthetic derivative or a risky contract. You get real exposure to the underlying stock. The main difference is that these stocks now live on the blockchain. Specifically they operate as BNB Smart Chain tokens using the BEP 677 standard. This gives you the superpower to treat them just like normal crypto assets.
❍ Why This Changes The Game For Us
We all know the headache of the traditional stock market. You wait for the opening bell. You suffer through weekend closures. You deal with settlement delays. Binance bStocks completely shatters those old walls.
Here is what makes this launch a massive upgrade for your portfolio.
Round The Clock Trading : Crypto never sleeps and now your stocks do not either. You can trade bStocks 24/7 on the Binance spot market. Whether it is a Sunday night or a public holiday, you can react to global news instantly. No more waiting for Monday morning to secure your profits or cut your losses.Instant Settlement : Traditional brokers make you wait a full business day just to settle a trade. With bStocks, transactions settle in under a second. The moment you hit the buy button, the tokenized share is in your wallet.Start Small With Fractional Shares : US stocks can be expensive. A single share of a major tech company might cost hundreds of dollars. Binance lets you dive in with as little as $5. You can buy a fraction of a Tesla or Nvidia share and build your wealth slowly without needing a massive bankroll.Zero Conversion Fees : You can swap instantly between your real equities and bStocks at a perfect 1:1 ratio. Binance charges absolutely zero conversion fees for this process. It is a seamless bridge between the traditional finance world and the crypto ecosystem.Full Self Custody : This is a huge win for the core crypto crowd. You do not have to keep your bStocks on the exchange. You can withdraw them directly to any compatible BNB Smart Chain wallet. You have total control over your assets while still holding traditional stock exposure.
❍ How to Buy Binance bStocks
Now we get to the fun part. Buying your first tokenized stock is incredibly easy. If you know how to buy Bitcoin on Binance, you already know how to do this. Follow these simple steps to get started.
Step 1: Complete Your Verification Before you can trade tokenized securities, you need to make sure your Binance account is fully verified. This means completing the standard KYC process. Since these are tied to real US stocks, compliance is mandatory.
Step 2: Fund Your Account You need some stablecoins to make the purchase. Currently Binance pairs these tokenized stocks directly with USDT. Deposit some USDT into your Spot wallet or convert your existing crypto into USDT.
Step 3: Navigate to the Spot Market Open your Binance app or website and head over to the Trade tab. Select Spot trading. In the search bar, look for the bStocks category or directly search for the ticker you want like NVDAB or TSLAB.
Step 4: Place Your Order Once you are on the trading pair screen, you will see the familiar order book. You can choose a Limit Order to buy at a specific price or a Market Order to buy instantly at the current price. Enter the amount of USDT you want to spend. Remember that you can start with just $5.
Step 5: Confirm and Hold Hit the Buy button. Because bStocks settle instantly, your tokenized shares will appear in your Spot wallet in less than a second. You are now a proud holder of a tokenized US equity.
❍ Available bStocks on Binance Right Now
To kick things off, Binance rolled out a solid list of heavy hitters. You can currently trade tokenized versions of companies that drive the modern tech world. The initial lineup includes Circle (CRCLB), Micron (MUB), NVIDIA ($NVDAB ), Sandisk ($SNDKB ), and Tesla (TSLAB).
These pairings trade directly against USDT. That means you can seamlessly move your stablecoin profits right into traditional tech stocks in a matter of seconds. Binance also enabled Spot Algo Trading Bots for these pairs. If you like automating your strategies, you can let the bots do the heavy lifting while you sleep.
We are now witnessing the absolute new fin-tech unfold right in front of us. It brings real world assets on-chain, which is the holy grail we have discussed for years. Binance is making it a reality with bStocks.
You finally have the power to diversify your portfolio with massive US tech giants without leaving your favorite crypto app. It bridges the gap perfectly for anyone who wants the stability of Wall Street mixed with the speed and freedom of Web3. Go check the spot market, grab five bucks worth of Nvidia, and experience the future of stock market by yourself.
#TradebStocks
An Introduction to Binance bStocks
Well it is stock time. It feels strange because we are fully in the trenches of crypto and suddenly everyone is talking about stocks. Sad but true, the traditional stock market outperformed the crypto market by a huge margin the last few years and crypto traders like us are waiting for our turn. But many of us do both and want benefits from the two from one place. This my friend is a tricky favor to ask.
To trade crypto and stock at one place is the ultimate satisfaction for any trader and especially for lazy traders like me, who always want many things served on a single platter.
Enter Binance bStocks.
❍ What Are Binance bStocks?
Think of your favorite US companies. Tesla, Nvidia, Circle, and Micron. Now imagine buying their shares just like you buy Bitcoin or Ethereum on a weekend. That is exactly what Binance bStocks brings to the table. These are tokenized securities. Every single bStock token you hold is backed 1:1 by a real US share sitting safely with a regulated custodian.
You are not buying a synthetic derivative or a risky contract. You get real exposure to the underlying stock. The main difference is that these stocks now live on the blockchain. Specifically they operate as BNB Smart Chain tokens using the BEP 677 standard. This gives you the superpower to treat them just like normal crypto assets.
❍ Why This Changes The Game For Us
We all know the headache of the traditional stock market. You wait for the opening bell. You suffer through weekend closures. You deal with settlement delays. Binance bStocks completely shatters those old walls.
Here is what makes this launch a massive upgrade for your portfolio.
Round The Clock Trading : Crypto never sleeps and now your stocks do not either. You can trade bStocks 24/7 on the Binance spot market. Whether it is a Sunday night or a public holiday, you can react to global news instantly. No more waiting for Monday morning to secure your profits or cut your losses.Instant Settlement : Traditional brokers make you wait a full business day just to settle a trade. With bStocks, transactions settle in under a second. The moment you hit the buy button, the tokenized share is in your wallet.Start Small With Fractional Shares : US stocks can be expensive. A single share of a major tech company might cost hundreds of dollars. Binance lets you dive in with as little as $5. You can buy a fraction of a Tesla or Nvidia share and build your wealth slowly without needing a massive bankroll.Zero Conversion Fees : You can swap instantly between your real equities and bStocks at a perfect 1:1 ratio. Binance charges absolutely zero conversion fees for this process. It is a seamless bridge between the traditional finance world and the crypto ecosystem.Full Self Custody : This is a huge win for the core crypto crowd. You do not have to keep your bStocks on the exchange. You can withdraw them directly to any compatible BNB Smart Chain wallet. You have total control over your assets while still holding traditional stock exposure.
❍ How to Buy Binance bStocks
Now we get to the fun part. Buying your first tokenized stock is incredibly easy. If you know how to buy Bitcoin on Binance, you already know how to do this. Follow these simple steps to get started.
Step 1: Complete Your Verification Before you can trade tokenized securities, you need to make sure your Binance account is fully verified. This means completing the standard KYC process. Since these are tied to real US stocks, compliance is mandatory.
Step 2: Fund Your Account You need some stablecoins to make the purchase. Currently Binance pairs these tokenized stocks directly with USDT. Deposit some USDT into your Spot wallet or convert your existing crypto into USDT.
Step 3: Navigate to the Spot Market Open your Binance app or website and head over to the Trade tab. Select Spot trading. In the search bar, look for the bStocks category or directly search for the ticker you want like NVDAB or TSLAB.
Step 4: Place Your Order Once you are on the trading pair screen, you will see the familiar order book. You can choose a Limit Order to buy at a specific price or a Market Order to buy instantly at the current price. Enter the amount of USDT you want to spend. Remember that you can start with just $5.
Step 5: Confirm and Hold Hit the Buy button. Because bStocks settle instantly, your tokenized shares will appear in your Spot wallet in less than a second. You are now a proud holder of a tokenized US equity.
❍ Available bStocks on Binance Right Now
To kick things off, Binance rolled out a solid list of heavy hitters. You can currently trade tokenized versions of companies that drive the modern tech world. The initial lineup includes Circle (CRCLB), Micron (MUB), NVIDIA ($NVDAB ), Sandisk ($SNDKB ), and Tesla (TSLAB).
These pairings trade directly against USDT. That means you can seamlessly move your stablecoin profits right into traditional tech stocks in a matter of seconds. Binance also enabled Spot Algo Trading Bots for these pairs. If you like automating your strategies, you can let the bots do the heavy lifting while you sleep.
We are now witnessing the absolute new fin-tech unfold right in front of us. It brings real world assets on-chain, which is the holy grail we have discussed for years. Binance is making it a reality with bStocks.
You finally have the power to diversify your portfolio with massive US tech giants without leaving your favorite crypto app. It bridges the gap perfectly for anyone who wants the stability of Wall Street mixed with the speed and freedom of Web3. Go check the spot market, grab five bucks worth of Nvidia, and experience the future of stock market by yourself.
#TradebStocks
To trade crypto and stock at one place is the ultimate satisfaction for any trader and especially for lazy traders like me, who always want many things served on a single platter.
Enter Binance bStocks.
❍ What Are Binance bStocks?
Think of your favorite US companies. Tesla, Nvidia, Circle, and Micron. Now imagine buying their shares just like you buy Bitcoin or Ethereum on a weekend. That is exactly what Binance bStocks brings to the table. These are tokenized securities. Every single bStock token you hold is backed 1:1 by a real US share sitting safely with a regulated custodian.
You are not buying a synthetic derivative or a risky contract. You get real exposure to the underlying stock. The main difference is that these stocks now live on the blockchain. Specifically they operate as BNB Smart Chain tokens using the BEP 677 standard. This gives you the superpower to treat them just like normal crypto assets.
❍ Why This Changes The Game For Us
We all know the headache of the traditional stock market. You wait for the opening bell. You suffer through weekend closures. You deal with settlement delays. Binance bStocks completely shatters those old walls.
Here is what makes this launch a massive upgrade for your portfolio.
Round The Clock Trading : Crypto never sleeps and now your stocks do not either. You can trade bStocks 24/7 on the Binance spot market. Whether it is a Sunday night or a public holiday, you can react to global news instantly. No more waiting for Monday morning to secure your profits or cut your losses.Instant Settlement : Traditional brokers make you wait a full business day just to settle a trade. With bStocks, transactions settle in under a second. The moment you hit the buy button, the tokenized share is in your wallet.Start Small With Fractional Shares : US stocks can be expensive. A single share of a major tech company might cost hundreds of dollars. Binance lets you dive in with as little as $5. You can buy a fraction of a Tesla or Nvidia share and build your wealth slowly without needing a massive bankroll.Zero Conversion Fees : You can swap instantly between your real equities and bStocks at a perfect 1:1 ratio. Binance charges absolutely zero conversion fees for this process. It is a seamless bridge between the traditional finance world and the crypto ecosystem.Full Self Custody : This is a huge win for the core crypto crowd. You do not have to keep your bStocks on the exchange. You can withdraw them directly to any compatible BNB Smart Chain wallet. You have total control over your assets while still holding traditional stock exposure.
❍ How to Buy Binance bStocks
Now we get to the fun part. Buying your first tokenized stock is incredibly easy. If you know how to buy Bitcoin on Binance, you already know how to do this. Follow these simple steps to get started.
Step 1: Complete Your Verification Before you can trade tokenized securities, you need to make sure your Binance account is fully verified. This means completing the standard KYC process. Since these are tied to real US stocks, compliance is mandatory.
Step 2: Fund Your Account You need some stablecoins to make the purchase. Currently Binance pairs these tokenized stocks directly with USDT. Deposit some USDT into your Spot wallet or convert your existing crypto into USDT.
Step 3: Navigate to the Spot Market Open your Binance app or website and head over to the Trade tab. Select Spot trading. In the search bar, look for the bStocks category or directly search for the ticker you want like NVDAB or TSLAB.
Step 4: Place Your Order Once you are on the trading pair screen, you will see the familiar order book. You can choose a Limit Order to buy at a specific price or a Market Order to buy instantly at the current price. Enter the amount of USDT you want to spend. Remember that you can start with just $5.
Step 5: Confirm and Hold Hit the Buy button. Because bStocks settle instantly, your tokenized shares will appear in your Spot wallet in less than a second. You are now a proud holder of a tokenized US equity.
❍ Available bStocks on Binance Right Now
To kick things off, Binance rolled out a solid list of heavy hitters. You can currently trade tokenized versions of companies that drive the modern tech world. The initial lineup includes Circle (CRCLB), Micron (MUB), NVIDIA ($NVDAB ), Sandisk ($SNDKB ), and Tesla (TSLAB).
These pairings trade directly against USDT. That means you can seamlessly move your stablecoin profits right into traditional tech stocks in a matter of seconds. Binance also enabled Spot Algo Trading Bots for these pairs. If you like automating your strategies, you can let the bots do the heavy lifting while you sleep.
We are now witnessing the absolute new fin-tech unfold right in front of us. It brings real world assets on-chain, which is the holy grail we have discussed for years. Binance is making it a reality with bStocks.
You finally have the power to diversify your portfolio with massive US tech giants without leaving your favorite crypto app. It bridges the gap perfectly for anyone who wants the stability of Wall Street mixed with the speed and freedom of Web3. Go check the spot market, grab five bucks worth of Nvidia, and experience the future of stock market by yourself.
#TradebStocks
4
2.4k
$BTC $HYPE Despite record highs in equities, crypto tokens continue to lag, with declines across all market-cap categories.
$BTC $HYPE Despite record highs in equities, crypto tokens continue to lag, with declines across all market-cap categories.
3
1.6k
Verified
🔅𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼 𝗶𝗻 𝗹𝗮𝘀𝘁 24𝗛?🔅
-
•$BTC BlackRock updates filing for Bitcoin income ETF
• $RAY Raydium treasury to reimburse exploit victims
• Digital Asset raises $355M for Canton Network
• cb launches AI-focused crypto transaction platform
• DOJ charges two in $389M crypto laundering case
• Japan advances bill to lower crypto taxes
• $HYPE Paradigm and Hyperliquid push for lighter stablecoin rules
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
🔅𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼 𝗶𝗻 𝗹𝗮𝘀𝘁 24𝗛?🔅
-
•$BTC BlackRock updates filing for Bitcoin income ETF
• $RAY Raydium treasury to reimburse exploit victims
• Digital Asset raises $355M for Canton Network
• cb launches AI-focused crypto transaction platform
• DOJ charges two in $389M crypto laundering case
• Japan advances bill to lower crypto taxes
• $HYPE Paradigm and Hyperliquid push for lighter stablecoin rules
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
-
•$BTC BlackRock updates filing for Bitcoin income ETF
• $RAY Raydium treasury to reimburse exploit victims
• Digital Asset raises $355M for Canton Network
• cb launches AI-focused crypto transaction platform
• DOJ charges two in $389M crypto laundering case
• Japan advances bill to lower crypto taxes
• $HYPE Paradigm and Hyperliquid push for lighter stablecoin rules
💡 Courtesy - Datawallet
©𝑻𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆 𝒊𝒔 𝒇𝒐𝒓 𝒊𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 𝒐𝒏𝒍𝒚 𝒂𝒏𝒅 𝒏𝒐𝒕 𝒂𝒏 𝒆𝒏𝒅𝒐𝒓𝒔𝒆𝒎𝒆𝒏𝒕 𝒐𝒇 𝒂𝒏𝒚 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒐𝒓 𝒆𝒏𝒕𝒊𝒕𝒚. 𝑻𝒉𝒆 𝒏𝒂𝒎𝒆𝒔 𝒎𝒆𝒏𝒕𝒊𝒐𝒏𝒆𝒅 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒓𝒆𝒍𝒂𝒕𝒆𝒅 𝒕𝒐 𝒖𝒔. 𝑾𝒆 𝒂𝒓𝒆 𝒏𝒐𝒕 𝒍𝒊𝒂𝒃𝒍𝒆 𝒇𝒐𝒓 𝒂𝒏𝒚 𝒍𝒐𝒔𝒔𝒆𝒔 𝒇𝒓𝒐𝒎 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒏𝒈 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒊𝒔 𝒂𝒓𝒕𝒊𝒄𝒍𝒆. 𝑻𝒉𝒊𝒔 𝒊𝒔 𝒏𝒐𝒕 𝒇𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝒂𝒅𝒗𝒊𝒄𝒆. 𝑻𝒉𝒊𝒔 𝒅𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓 𝒑𝒓𝒐𝒕𝒆𝒄𝒕𝒔 𝒃𝒐𝒕𝒉 𝒚𝒐𝒖 𝒂𝒏𝒅 𝒖𝒔.
🅃🄴🄲🄷🄰🄽🄳🅃🄸🄿🅂123
2
1.6k
Unverified content
$SPCX $TSLA Made Elon First Trillion-aire of Planet Earth
$SPCX $TSLA Made Elon First Trillion-aire of Planet Earth
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Now it's time for $CHZ to pump +10% in once in a 4 years . #FIFAWorldCup26
Now it's time for $CHZ to pump +10% in once in a 4 years . #FIFAWorldCup26
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Unverified content
$SPCX IPO in Corner but Silently Yeet just turned the 2026 FIFA World Cup into an event you can actually predict and capitalize on
-
Sports predictions always struggled with:
> boring Web2 platforms
> clunky user interfaces
> zero crypto native feel
Yeet brings all of it together through a culture driven prediction market. DeFi mechanics, deep liquidity, and global forecasting work as one. It is backed by $7.75 million from Dragonfly to remove friction completely.
Deposit → forecast → scale
That is the new normal. Looking at the landscape:
Hyperliquid dominates decentralized perpetual trading. $ZEC focuses on deep transaction privacy. $XRP delivers rapid cross border settlement. Yeet goes deeper by shifting toward cultural verticals.
It adds the missing layer: World Cup forecasting built entirely around native Web3 risk mechanics. The numbers show serious momentum:
$7.75M funding round led by Dragonfly. Live prediction markets scaled for the 2026 FIFA World Cup. Deep integration of Berachain liquidity layers
This is the moment where on chain sports forecasting stops being a copy of Web2. It starts becoming a massive Web3 cultural standard.
Use my affiliate code 𝐭𝐞𝐜𝐡𝐚𝐧𝐝𝐭𝐢𝐩𝐬123 to unlock exciting rewards and get started today!
B U L L I S H 🥂 Yeet
$SPCX IPO in Corner but Silently Yeet just turned the 2026 FIFA World Cup into an event you can actually predict and capitalize on
-
Sports predictions always struggled with:
> boring Web2 platforms
> clunky user interfaces
> zero crypto native feel
Yeet brings all of it together through a culture driven prediction market. DeFi mechanics, deep liquidity, and global forecasting work as one. It is backed by $7.75 million from Dragonfly to remove friction completely.
Deposit → forecast → scale
That is the new normal. Looking at the landscape:
Hyperliquid dominates decentralized perpetual trading. $ZEC focuses on deep transaction privacy. $XRP delivers rapid cross border settlement. Yeet goes deeper by shifting toward cultural verticals.
It adds the missing layer: World Cup forecasting built entirely around native Web3 risk mechanics. The numbers show serious momentum:
$7.75M funding round led by Dragonfly. Live prediction markets scaled for the 2026 FIFA World Cup. Deep integration of Berachain liquidity layers
This is the moment where on chain sports forecasting stops being a copy of Web2. It starts becoming a massive Web3 cultural standard.
Use my affiliate code 𝐭𝐞𝐜𝐡𝐚𝐧𝐝𝐭𝐢𝐩𝐬123 to unlock exciting rewards and get started today!
B U L L I S H 🥂 Yeet
-
Sports predictions always struggled with:
> boring Web2 platforms
> clunky user interfaces
> zero crypto native feel
Yeet brings all of it together through a culture driven prediction market. DeFi mechanics, deep liquidity, and global forecasting work as one. It is backed by $7.75 million from Dragonfly to remove friction completely.
Deposit → forecast → scale
That is the new normal. Looking at the landscape:
Hyperliquid dominates decentralized perpetual trading. $ZEC focuses on deep transaction privacy. $XRP delivers rapid cross border settlement. Yeet goes deeper by shifting toward cultural verticals.
It adds the missing layer: World Cup forecasting built entirely around native Web3 risk mechanics. The numbers show serious momentum:
$7.75M funding round led by Dragonfly. Live prediction markets scaled for the 2026 FIFA World Cup. Deep integration of Berachain liquidity layers
This is the moment where on chain sports forecasting stops being a copy of Web2. It starts becoming a massive Web3 cultural standard.
Use my affiliate code 𝐭𝐞𝐜𝐡𝐚𝐧𝐝𝐭𝐢𝐩𝐬123 to unlock exciting rewards and get started today!
B U L L I S H 🥂 Yeet
3
1.7k
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 : What Is a Private Key Compromise vs. a Smart Contract Exploit?
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 : What Is a Private Key Compromise vs. a Smart Contract Exploit?
1
2.1k
Verified
$SPCX SpaceX IPO demand has now reached over $250B, nearly 4x oversubscribed against its planned $75B raise, per Reuters.
-
That is 8.5x larger than Saudi Aramco’s record $29.4B IPO.
© Reuters / Coin Bureau
$SPCX SpaceX IPO demand has now reached over $250B, nearly 4x oversubscribed against its planned $75B raise, per Reuters.
-
That is 8.5x larger than Saudi Aramco’s record $29.4B IPO.
© Reuters / Coin Bureau
-
That is 8.5x larger than Saudi Aramco’s record $29.4B IPO.
© Reuters / Coin Bureau
1
1
4.1k
$SPCX Many crypto Nibbies are hopping around Spacex and IPOs, mate first fully understand these things. This single company is valued around 80% of whole crypto market and that includes your revolutionary chain and shitcoins.
These are twirl inside their lowkey posh enviornment , don't believe in those ugly brain content, saying this can change your life. 😂
Don't believe in if you put 10$ in Spacex IPO and you'll be same page as Elon. stay in your level this is not your ShibaPussyInu or DogwifCok.
so, respect it as a stock, do not fomo into it with 12$ bet on crypto with that
$SPCX Many crypto Nibbies are hopping around Spacex and IPOs, mate first fully understand these things. This single company is valued around 80% of whole crypto market and that includes your revolutionary chain and shitcoins.
These are twirl inside their lowkey posh enviornment , don't believe in those ugly brain content, saying this can change your life. 😂
Don't believe in if you put 10$ in Spacex IPO and you'll be same page as Elon. stay in your level this is not your ShibaPussyInu or DogwifCok.
so, respect it as a stock, do not fomo into it with 12$ bet on crypto with that
These are twirl inside their lowkey posh enviornment , don't believe in those ugly brain content, saying this can change your life. 😂
Don't believe in if you put 10$ in Spacex IPO and you'll be same page as Elon. stay in your level this is not your ShibaPussyInu or DogwifCok.
so, respect it as a stock, do not fomo into it with 12$ bet on crypto with that
11
9.8k
Verified
Article
The Leverage Explosion: US ETF Trading Volume Hits a Record $90 BillionThe financial markets are moving at a blinding pace. Investors are walking away from slow, traditional growth and jumping directly into hyper-aggressive strategies. The latest data reveals a massive spike in trading volumes for leveraged and inverse products. This activity shows that market participants are completely embracing extreme volatility.
A Historic $90 Billion Session
The sheer volume of money moving through leveraged funds is breaking every previous record.
The New Peak: Total trading volume across US-listed leveraged and inverse ETFs reached $90 billion on Tuesday. This is the highest single-day amount ever recorded.A Rapid Tripling: This trading activity did not just edge higher. The volume has more than tripled over the last 12 months.Moving the Whole Market: To grasp the scale, this $90 billion represents roughly 50% of all assets under management across the entire leveraged and inverse ETF universe. Half of the total money in this sector changed hands in just one day.
The Massive Run on SOXS
The bulk of this intense trading is concentrated in a single sector of the technology market.
Betting Against Chips: Traders heavily targeted the 3x leveraged short semiconductor ETF, known by its ticker SOXS.1.3 Billion Shares: This single fund traded over 1.3 billion shares in one session.A 20-Year Milestone: This marks the third-largest single-session volume for any US-listed ETF in the last two decades. A massive wave of capital is actively wagering on a swift decline in semiconductor stocks.
Chasing the Highs of 2008
You have to look back to major economic crises to find any trading volume that matches these levels.
The Only Competitors: The only two sessions with higher volumes occurred in the 2x leveraged long Nasdaq 100 ETF (QLD) and the 2x leveraged long S&P 500 ETF (SSO).The Crisis Baseline: Both of those funds set their absolute volume records during the darkest days of the 2008 Financial Crisis. The current environment is producing similar trading intensity without a total market meltdown.
Some Random Thoughts 💬
Risk appetite has reached an absolute boiling point. When a single inverse ETF trades over a billion shares in a matter of hours, the market is no longer trading on corporate earnings or cash flows. This is pure momentum and speculation. Leveraged ETFs are highly complex tools. They are designed for short-term trading because their value decays rapidly over time. Seeing this much capital flood into 3x short positions tells me that investors are actively hunting for wild price swings. The warning signs from previous market peaks are flashing again. Anyone playing in this highly leveraged sandbox needs strict risk management before the tide turns.
The Leverage Explosion: US ETF Trading Volume Hits a Record $90 Billion
The financial markets are moving at a blinding pace. Investors are walking away from slow, traditional growth and jumping directly into hyper-aggressive strategies. The latest data reveals a massive spike in trading volumes for leveraged and inverse products. This activity shows that market participants are completely embracing extreme volatility.
A Historic $90 Billion Session
The sheer volume of money moving through leveraged funds is breaking every previous record.
The New Peak: Total trading volume across US-listed leveraged and inverse ETFs reached $90 billion on Tuesday. This is the highest single-day amount ever recorded.A Rapid Tripling: This trading activity did not just edge higher. The volume has more than tripled over the last 12 months.Moving the Whole Market: To grasp the scale, this $90 billion represents roughly 50% of all assets under management across the entire leveraged and inverse ETF universe. Half of the total money in this sector changed hands in just one day.
The Massive Run on SOXS
The bulk of this intense trading is concentrated in a single sector of the technology market.
Betting Against Chips: Traders heavily targeted the 3x leveraged short semiconductor ETF, known by its ticker SOXS.1.3 Billion Shares: This single fund traded over 1.3 billion shares in one session.A 20-Year Milestone: This marks the third-largest single-session volume for any US-listed ETF in the last two decades. A massive wave of capital is actively wagering on a swift decline in semiconductor stocks.
Chasing the Highs of 2008
You have to look back to major economic crises to find any trading volume that matches these levels.
The Only Competitors: The only two sessions with higher volumes occurred in the 2x leveraged long Nasdaq 100 ETF (QLD) and the 2x leveraged long S&P 500 ETF (SSO).The Crisis Baseline: Both of those funds set their absolute volume records during the darkest days of the 2008 Financial Crisis. The current environment is producing similar trading intensity without a total market meltdown.
Some Random Thoughts 💬
Risk appetite has reached an absolute boiling point. When a single inverse ETF trades over a billion shares in a matter of hours, the market is no longer trading on corporate earnings or cash flows. This is pure momentum and speculation. Leveraged ETFs are highly complex tools. They are designed for short-term trading because their value decays rapidly over time. Seeing this much capital flood into 3x short positions tells me that investors are actively hunting for wild price swings. The warning signs from previous market peaks are flashing again. Anyone playing in this highly leveraged sandbox needs strict risk management before the tide turns.
A Historic $90 Billion Session
The sheer volume of money moving through leveraged funds is breaking every previous record.
The New Peak: Total trading volume across US-listed leveraged and inverse ETFs reached $90 billion on Tuesday. This is the highest single-day amount ever recorded.A Rapid Tripling: This trading activity did not just edge higher. The volume has more than tripled over the last 12 months.Moving the Whole Market: To grasp the scale, this $90 billion represents roughly 50% of all assets under management across the entire leveraged and inverse ETF universe. Half of the total money in this sector changed hands in just one day.
The Massive Run on SOXS
The bulk of this intense trading is concentrated in a single sector of the technology market.
Betting Against Chips: Traders heavily targeted the 3x leveraged short semiconductor ETF, known by its ticker SOXS.1.3 Billion Shares: This single fund traded over 1.3 billion shares in one session.A 20-Year Milestone: This marks the third-largest single-session volume for any US-listed ETF in the last two decades. A massive wave of capital is actively wagering on a swift decline in semiconductor stocks.
Chasing the Highs of 2008
You have to look back to major economic crises to find any trading volume that matches these levels.
The Only Competitors: The only two sessions with higher volumes occurred in the 2x leveraged long Nasdaq 100 ETF (QLD) and the 2x leveraged long S&P 500 ETF (SSO).The Crisis Baseline: Both of those funds set their absolute volume records during the darkest days of the 2008 Financial Crisis. The current environment is producing similar trading intensity without a total market meltdown.
Some Random Thoughts 💬
Risk appetite has reached an absolute boiling point. When a single inverse ETF trades over a billion shares in a matter of hours, the market is no longer trading on corporate earnings or cash flows. This is pure momentum and speculation. Leveraged ETFs are highly complex tools. They are designed for short-term trading because their value decays rapidly over time. Seeing this much capital flood into 3x short positions tells me that investors are actively hunting for wild price swings. The warning signs from previous market peaks are flashing again. Anyone playing in this highly leveraged sandbox needs strict risk management before the tide turns.
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© Coinmetrics
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