Techandtips123
5.9k Posts
Techandtips123
Square Verified+Report
Block User
✅ PROMO - @iamdkbc ✅ Data Driven Crypto On-Chain Research & Analysis. X @Techandtips123
2025 Blockchain 100 — Independent Researcher
Traders League Badge Beginner
Top Voices
Frequent Trader
5.4 Years
20 Following
59.5K+ Followers
72.3K+ Liked
4 Badges
Posts
All
Quotes
Videos
Live
PINNED
Article
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.
11
9
74
14.2k
PINNED
Article
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
·
--
Bullish
$LINK 𝘾𝙝𝙖𝙞𝙣𝙡𝙞𝙣𝙠’𝙨 𝙝𝙤𝙡𝙙𝙚𝙧 𝙘𝙤𝙪𝙣𝙩 𝙝𝙖𝙨 𝙜𝙤𝙣𝙚 𝙥𝙖𝙧𝙖𝙗𝙤𝙡𝙞𝙘
-
Chainlink’s holder growth is suddenly accelerating in a big way. $LINK on Ethereum is now up to 892.8K non-empty wallets, adding more than 8K holders in just 5 days. At this pace, the network will cross 900K holders by end of week, and potentially reach 1M by the end of summer if this momentum continues.
🔗 The timing makes sense. Chainlink has been gaining fresh attention from real-world asset and institutional finance narratives, including Project Pangea, DTCC’s collateral work, tokenized assets, and 24/5 equity data streams.
What stands out is that this holder growth is happening while LINK’s price is still near local lows, which often points to quiet accumulation before the crowd fully notices.
© Santiment
$LINK 𝘾𝙝𝙖𝙞𝙣𝙡𝙞𝙣𝙠’𝙨 𝙝𝙤𝙡𝙙𝙚𝙧 𝙘𝙤𝙪𝙣𝙩 𝙝𝙖𝙨 𝙜𝙤𝙣𝙚 𝙥𝙖𝙧𝙖𝙗𝙤𝙡𝙞𝙘
-
Chainlink’s holder growth is suddenly accelerating in a big way. $LINK on Ethereum is now up to 892.8K non-empty wallets, adding more than 8K holders in just 5 days. At this pace, the network will cross 900K holders by end of week, and potentially reach 1M by the end of summer if this momentum continues.
🔗 The timing makes sense. Chainlink has been gaining fresh attention from real-world asset and institutional finance narratives, including Project Pangea, DTCC’s collateral work, tokenized assets, and 24/5 equity data streams.
What stands out is that this holder growth is happening while LINK’s price is still near local lows, which often points to quiet accumulation before the crowd fully notices.
© Santiment
-
Chainlink’s holder growth is suddenly accelerating in a big way. $LINK on Ethereum is now up to 892.8K non-empty wallets, adding more than 8K holders in just 5 days. At this pace, the network will cross 900K holders by end of week, and potentially reach 1M by the end of summer if this momentum continues.
🔗 The timing makes sense. Chainlink has been gaining fresh attention from real-world asset and institutional finance narratives, including Project Pangea, DTCC’s collateral work, tokenized assets, and 24/5 equity data streams.
What stands out is that this holder growth is happening while LINK’s price is still near local lows, which often points to quiet accumulation before the crowd fully notices.
© Santiment
77
$BNB 📊𝘽𝙉𝘽 𝘾𝙝𝙖𝙞𝙣 𝙨𝙩𝙖𝙗𝙡𝙚𝙘𝙤𝙞𝙣 𝙨𝙪𝙥𝙥𝙡𝙮 𝙧𝙚𝙖𝙘𝙝𝙚𝙙 $17.2𝘽, 𝙪𝙥 64% 𝙮/𝙮, 𝙬𝙞𝙩𝙝 𝙐𝙎𝘿𝙏, 𝙐𝙎𝘿𝘾, 𝙖𝙣𝙙 𝙁𝘿𝙐𝙎𝘿 𝙖𝙡𝙡 𝙚𝙭𝙥𝙖𝙣𝙙𝙞𝙣𝙜
-
Supply grew nearly 50x from under $300M in 2020, making BNB Chain the fastest-growing stablecoin network among majors, per VanEck data.
📈Stablecoin daily active users +33% YTD.
© Bitcoin.com
$BNB 📊𝘽𝙉𝘽 𝘾𝙝𝙖𝙞𝙣 𝙨𝙩𝙖𝙗𝙡𝙚𝙘𝙤𝙞𝙣 𝙨𝙪𝙥𝙥𝙡𝙮 𝙧𝙚𝙖𝙘𝙝𝙚𝙙 $17.2𝘽, 𝙪𝙥 64% 𝙮/𝙮, 𝙬𝙞𝙩𝙝 𝙐𝙎𝘿𝙏, 𝙐𝙎𝘿𝘾, 𝙖𝙣𝙙 𝙁𝘿𝙐𝙎𝘿 𝙖𝙡𝙡 𝙚𝙭𝙥𝙖𝙣𝙙𝙞𝙣𝙜
-
Supply grew nearly 50x from under $300M in 2020, making BNB Chain the fastest-growing stablecoin network among majors, per VanEck data.
📈Stablecoin daily active users +33% YTD.
© Bitcoin.com
-
Supply grew nearly 50x from under $300M in 2020, making BNB Chain the fastest-growing stablecoin network among majors, per VanEck data.
📈Stablecoin daily active users +33% YTD.
© Bitcoin.com
58
$BTC at $59,000 sits 23% below its True Market Mean of $77,000, the level that has divided bull and bear regimes.
-
Below that, the realised price near $53,400 is the next floor.
Until $77,000 is reclaimed, bounces are relief, not a turn.
© Bitfinex
$BTC at $59,000 sits 23% below its True Market Mean of $77,000, the level that has divided bull and bear regimes.
-
Below that, the realised price near $53,400 is the next floor.
Until $77,000 is reclaimed, bounces are relief, not a turn.
© Bitfinex
-
Below that, the realised price near $53,400 is the next floor.
Until $77,000 is reclaimed, bounces are relief, not a turn.
© Bitfinex
80
$BTC UAE-based Goldman Lampe Private Bank has acquired €120 million in $BTC, the institution announced Monday.
-
The purchase totals roughly $137 million, marking a significant #Bitcoin acquisition by a private bank in the region.
© Bitcoin.com
$BTC UAE-based Goldman Lampe Private Bank has acquired €120 million in $BTC , the institution announced Monday.
-
The purchase totals roughly $137 million, marking a significant #Bitcoin acquisition by a private bank in the region.
© Bitcoin.com
-
The purchase totals roughly $137 million, marking a significant #Bitcoin acquisition by a private bank in the region.
© Bitcoin.com
109
$HYPE 𝙃𝙔𝙋𝙀𝙍𝙇𝙄𝙌𝙐𝙄𝘿 𝘾𝙃𝘼𝙍𝙏𝙎 𝙇𝘼𝙉𝘿 𝙊𝙉 𝙏𝙍𝘼𝘿𝙄𝙉𝙂𝙑𝙄𝙀𝙒
-
TradingView has integrated native Hyperliquid charts, including HIP-3 markets.
This gives traders cleaner access to Hyperliquid price data directly inside one of the world’s biggest charting platforms.
© Coin Bureau
$HYPE 𝙃𝙔𝙋𝙀𝙍𝙇𝙄𝙌𝙐𝙄𝘿 𝘾𝙃𝘼𝙍𝙏𝙎 𝙇𝘼𝙉𝘿 𝙊𝙉 𝙏𝙍𝘼𝘿𝙄𝙉𝙂𝙑𝙄𝙀𝙒
-
TradingView has integrated native Hyperliquid charts, including HIP-3 markets.
This gives traders cleaner access to Hyperliquid price data directly inside one of the world’s biggest charting platforms.
© Coin Bureau
-
TradingView has integrated native Hyperliquid charts, including HIP-3 markets.
This gives traders cleaner access to Hyperliquid price data directly inside one of the world’s biggest charting platforms.
© Coin Bureau
1
116
𝙄𝙣𝙙𝙞𝙖'𝙨 𝙐𝙎𝘿𝙏 𝙥𝙧𝙚𝙢𝙞𝙪𝙢 𝙨𝙪𝙧𝙜𝙚𝙨 𝙖𝙗𝙤𝙫𝙚 8.5% 𝙖𝙛𝙩𝙚𝙧 𝙘𝙧𝙮𝙥𝙩𝙤 𝙧𝙚𝙢𝙞𝙩𝙩𝙖𝙣𝙘𝙚 𝙘𝙧𝙖𝙘𝙠𝙙𝙤𝙬𝙣
-
India's Enforcement Directorate raided crypto-linked remittance firms in Bengaluru, squeezing local USDT supply and pushing the premium above 8.5%, more than double the usual range.
A Parliamentary Standing Committee meeting with the RBI is set for July 2 to discuss the country's crypto regulatory path forward.
© The Economic Times
𝙄𝙣𝙙𝙞𝙖'𝙨 𝙐𝙎𝘿𝙏 𝙥𝙧𝙚𝙢𝙞𝙪𝙢 𝙨𝙪𝙧𝙜𝙚𝙨 𝙖𝙗𝙤𝙫𝙚 8.5% 𝙖𝙛𝙩𝙚𝙧 𝙘𝙧𝙮𝙥𝙩𝙤 𝙧𝙚𝙢𝙞𝙩𝙩𝙖𝙣𝙘𝙚 𝙘𝙧𝙖𝙘𝙠𝙙𝙤𝙬𝙣
-
India's Enforcement Directorate raided crypto-linked remittance firms in Bengaluru, squeezing local USDT supply and pushing the premium above 8.5%, more than double the usual range.
A Parliamentary Standing Committee meeting with the RBI is set for July 2 to discuss the country's crypto regulatory path forward.
© The Economic Times
-
India's Enforcement Directorate raided crypto-linked remittance firms in Bengaluru, squeezing local USDT supply and pushing the premium above 8.5%, more than double the usual range.
A Parliamentary Standing Committee meeting with the RBI is set for July 2 to discuss the country's crypto regulatory path forward.
© The Economic Times
97
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙘𝙡𝙞𝙣𝙜𝙨 𝙩𝙤 $60𝙠 𝙖𝙨 𝙁𝙚𝙙 𝙧𝙖𝙩𝙚 𝙝𝙞𝙠𝙚 𝙗𝙚𝙩𝙨 𝙢𝙤𝙪𝙣𝙩
-
Bitcoin is trading near $60,000 after falling more than 50% from its October 2025 peak of around $125,000. Markets are now pricing in at least one more Fed rate hike after the nomination of hawkish Kevin Warsh as Fed Chair.
© The Block
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙘𝙡𝙞𝙣𝙜𝙨 𝙩𝙤 $60𝙠 𝙖𝙨 𝙁𝙚𝙙 𝙧𝙖𝙩𝙚 𝙝𝙞𝙠𝙚 𝙗𝙚𝙩𝙨 𝙢𝙤𝙪𝙣𝙩
-
Bitcoin is trading near $60,000 after falling more than 50% from its October 2025 peak of around $125,000. Markets are now pricing in at least one more Fed rate hike after the nomination of hawkish Kevin Warsh as Fed Chair.
© The Block
-
Bitcoin is trading near $60,000 after falling more than 50% from its October 2025 peak of around $125,000. Markets are now pricing in at least one more Fed rate hike after the nomination of hawkish Kevin Warsh as Fed Chair.
© The Block
85
Article
Deep Dive: How NEAR Protocol Becoming The Biggest AI ChainThe entire premise of the cryptocurrency industry has shifted. Developers spent the past fifteen years building blockchain infrastructure exclusively for human users. They designed browser wallets for human hands. They built decentralized exchanges for human traders. They created complex financial applications requiring constant human oversight. This era is rapidly coming to an end. The industry is no longer building for human traders. Developers are now building infrastructure for artificial intelligence agents.
This new internet architecture is called the Agentic Web. The Agentic Web is a digital environment where autonomous software programs operate freely. These software programs are called AI agents. An AI agent is a piece of code that makes independent decisions. These agents perform several complex tasks:
They manage complex financial portfolios across multiple networks.They purchase proprietary data sets directly from remote servers.They execute digital tasks on behalf of human users without requiring constant supervision.
These AI agents require a frictionless financial settlement layer to transact. Traditional banking rails fail completely at this specific task. Traditional payment processors like Visa use an outdated fee structure. They charge a fixed flat fee plus a percentage of the total transaction volume. A standard credit card transaction often costs a merchant ten cents plus two percent of the purchase price.
This fee structure destroys the profit margins of machine-to-machine commerce. An AI agent often executes thousands of micro-transactions per minute. An agent might pay a fraction of a cent to query a database. If an agent executes a payment of $0.31, a ten-cent flat fee consumes a massive portion of the capital. Traditional banks also require human identity verification. A software program cannot provide a physical passport to open a corporate bank account. AI agents must use blockchain infrastructure to interact with the global economy.
NEAR Protocol is stepping in to solve this exact problem. NEAR was an artificial intelligence company before it ever became a blockchain. Illia Polosukhin is the chief executive officer and co-founder of NEAR. He is a prominent artificial intelligence researcher who previously worked at Google Research. In 2017 he co-authored a seminal academic paper titled "Attention Is All You Need."
This specific research paper introduced the Transformer architecture. The Transformer architecture serves as the fundamental blueprint for modern large language models like ChatGPT and Claude. Polosukhin and his team understand the specific technical requirements of artificial intelligence. They designed NEAR to serve as the universal orchestration and settlement layer for this new machine economy. The platform allows AI agents to own digital assets and make independent financial decisions seamlessly.
II. The Surge of NEAR Protocol in 2026
The broader market fully realized the value of this architecture in early 2026. The network experienced massive growth across all metrics. This growth appeared in market data, user adoption, and major ecosystem milestones. The shift from experimental technology to investable infrastructure became undeniable.
❍ Explosive Price Action and Liquidations
The price action in May 2026 demonstrated this market shift clearly. The native token experienced a violent vertical price rally. The price of NEAR climbed from a low of $1.25 to break the $2.80 resistance level. This upward movement severely outperformed almost all top 100 cryptocurrencies.
The rapid price appreciation liquidated millions of dollars in short positions. Traders who bet against the network were forced to buy back the token at higher prices to cover their losses. This forced buying pressure accelerated the rally further. Traders grouped the asset alongside other AI-linked digital assets following a massive earnings report from NVIDIA.
❍ Network Volume and Revenue
The price surge was completely supported by fundamental network usage. The NEAR Intents system drove massive on-chain activity. NEAR Intents is a highly efficient cross-chain transaction routing system. Blockchain analytics platform DefiLlama published new data regarding this system in May 2026.
The data revealed staggering network usage:
The NEAR Intents cross-chain system processed over $19 billion in cumulative volume.The transaction volume generated approximately $32 million in protocol fees.Daily fee revenue crossed $400,000 during peak network activity.
❍ The Arrival of Wall Street Capital
Institutional validation arrived shortly after the on-chain data improved. Wall Street began viewing NEAR Protocol as enterprise-grade infrastructure. Traditional finance products expanded rapidly to capture this demand:
Grayscale Investments filed an S-1 registration statement with the SEC on January 20, 2026.This filing seeks to convert the existing Grayscale Near Trust (GSNR) into a spot exchange-traded fund.The Bitwise NEAR Staking exchange-traded product in Europe saw significant new inflows.The Bitwise product expanded to approximately $40 million in total assets under management.The Bitwise fund saw $7 million in new capital inflows in a single week.
III. Why NEAR Protocol is Pumping So Much
Market hype can drive short-term price action. Sustainable growth requires strong underlying tokenomics. The protocol completely overhauled its economic model in late 2025 and early 2026. These structural changes created severe deflationary pressure on the token supply.
❍ The Deflationary Fee Switch
The protocol activated a new financial mechanism on February 23, 2026. This mechanism fundamentally changed how the network handles transaction revenue. Most blockchain networks use fees to pay validators directly. The new mechanism redirects this capital entirely.
Exactly 100 percent of all fees generated by the NEAR Intents system now flow into an automated conversion mechanism. The system uses these collected protocol fees to execute open-market purchases of the NEAR token. Every single cross-chain transaction executed by an AI agent directly reduces the liquid token supply on exchanges. The protocol effectively buys its own token and removes it from circulation.
❍ The Halving Upgrade
Network inflation dictates the creation of new tokens over time. High inflation dilutes the value of existing tokens. The NEAR community executed a landmark governance shift in late 2025. The community voted to slash the maximum annual inflation of the entire network.
The inflation rate dropped dramatically from 5 percent down to 2.5 percent. This upgrade permanently restricted the new token supply. The network cut its emission schedule in half right as the artificial intelligence narrative spiked demand. Decreased supply and increased demand create an inevitable upward price trajectory.
❍ The 56x Valuation Disconnect
Arthur Hayes popularized a powerful valuation narrative for the network. Hayes is a prominent industry analyst and the co-founder of the BitMEX exchange. He published an investment thesis highlighting a massive valuation disconnect. He argued the market incorrectly prices NEAR as an old Layer 1 network rather than a highly profitable cross-chain execution business.
Hayes introduced a comparative financial framework to prove his point:
LayerZero is a leading interoperability protocol generating roughly $2.0 million in annualized fees.The NEAR Intents system generates roughly $36.3 million in annualized fees.NEAR Intents generates approximately 18 times the revenue of LayerZero.
Despite this overwhelming revenue dominance, the market gave LayerZero a higher valuation multiple. Hayes calculated that when combining base-chain gas and Intents fees, NEAR trades at a 56x annualized fee multiple. This makes NEAR fundamentally cheaper than competing networks like Ethereum, Solana, and Sui.
IV. The Agentic Stack of NEAR
A strong tokenomic model requires superior technical infrastructure. NEAR provides the specific tools that AI agents require. This combination of developer tools is called the Agentic Stack. The Agentic Stack makes NEAR the only logical home for autonomous software programs.
❍ Nightshade 3.0 and Massive Throughput
A standard blockchain processes transactions sequentially. This causes severe network congestion during high usage. NEAR solves this with Nightshade 3.0 Sharding. Sharding splits the blockchain network into smaller parallel pieces. Each piece processes its own transactions independently.
NEAR Protocol expanded its architecture to feature 9 parallel shards on the mainnet in 2025 and 2026. This expansion pushed network capabilities to unprecedented levels. In a publicly verifiable benchmark test, the network achieved massive throughput:
The system processed over 1,000,000 transactions per second.The test utilized 70 simulated shards across Google Cloud C4D virtual machines.The processors used were standard AMD EPYC chips without specialized hardware.The network maintained a 1.2-second finality rate.
❍ IronClaw and Trusted Execution Environments
Centralized artificial intelligence models lack transparency and security. The operating company can read user prompts and view sensitive financial data. NEAR solves this problem through User-Owned AI. In early 2026, NEAR AI officially launched IronClaw.
IronClaw is a highly secure open-source AI agent runtime environment. It is written entirely in the Rust programming language. Rust enforces memory safety at the compilation stage. This eliminates common vulnerabilities like buffer overflows entirely. IronClaw deploys AI agents inside encrypted hardware-secured areas called Trusted Execution Environments. The data remains fully encrypted in memory from the moment the system boots. The cloud provider and the node operator cannot see the data.
❍ Automatic Data Anonymization
The stack also includes automatic data anonymization. Advanced technology automatically scrubs sensitive information from user prompts. It scrubs passwords, API keys, and financial data before the prompt routes to large language models. The network uses real-time leak detection. It actively scans all outbound network traffic. If the system detects any data resembling a secret heading out, it automatically blocks the transmission.
V. How NEAR Protocol is Killing the Competition
The blockchain industry remains highly competitive. Ethereum holds the majority of decentralized finance liquidity. Solana provides excellent execution speed. NEAR Protocol contrasts itself against both networks by focusing on a massive technical differentiator. NEAR is hiding the blockchain entirely from the user.
❍ True Chain Abstraction
The industry struggles constantly with network fragmentation. Users must download specific browser wallets for specific networks. They must buy native tokens to pay for gas fees. They must use complex bridge protocols. AI agents also struggle with this fragmentation. Programming an agent to manage gas tokens across twelve different networks is incredibly complex.
NEAR solves this through True Chain Abstraction. While Solana fights to optimize its base-layer speed, NEAR makes the base layer invisible. The mechanism powering this is the NEAR Intents system. NEAR Intents uses a novel transaction architecture. Users and AI agents simply declare a desired outcome. A user can request to swap USDC on Ethereum for SOL on Solana. The user does not need to hold Ethereum to pay for gas. The user signs the request once.
❍ The Unified Liquidity Layer
The network routes the request to a decentralized network of third-party solvers. These solvers are professional market makers. They handle all the bridging and wrapping in the background automatically. This architecture creates a Unified Liquidity Layer. It effectively kills traditional bridge protocols.
By unifying fragmented liquidity, NEAR provides developers with an unparalleled advantage. A developer integrates a single API. That single integration connects their application to capital across the entire cryptocurrency ecosystem. It connects to Ethereum, Solana, Bitcoin, Arbitrum, and dozens of other networks simultaneously.
❍ The Mathematics of Micro-Payments
This unified architecture delivers a massive cost advantage. Artificial intelligence agents require extremely low transaction fees to operate profitably. Crypto market maker Keyrock published a comprehensive research report in May 2026. The data revealed critical metrics about machine commerce:
The average payment amount executed by an AI agent is exactly $0.31.Transferring assets on efficient blockchain infrastructure costs a fraction of a cent.An average transaction fee costs approximately $0.0001.This fee represents roughly 0.03 percent of a $0.31 transaction.
This near-zero transaction cost makes machine-to-machine commerce mathematically viable. Software can finally pay software without destroying its own profit margin.
VI. The Super-App Consumer Layer
Superior technology is utterly useless without actual human adoption. A network must prove its technology can reach normal consumers. NEAR Protocol proves this through major integrations and consumer applications. The technology is actively reaching tens of millions of humans right now.
❍ The Brave Browser Integration
The most significant consumer milestone occurred in March 2026. The Brave Browser integrated the NEAR Intents system directly into its native architecture. The Brave Browser boasts over 70 million monthly active users globally. The integration rolled out in the v1.88 desktop release.
This integration placed native cross-chain execution directly into the browser. Users experience several new capabilities:
Users can swap assets across Bitcoin, Solana, and Ethereum natively.Users can execute shielded Zcash transactions natively for total privacy.Users do not need to download third-party extensions or manage gas tokens.
❍ Cosmose AI and the Kai-Ching System
The protocol supports the rise of consumer Super-Apps. These applications provide a standard web experience while using blockchain rails in the background. Cosmose AI is a technology company that tracks offline retail shopping habits. The company previously partnered with Stripe for its payment processing.
Cosmose terminated its partnership with Stripe and migrated entirely to the NEAR Protocol. The company launched an e-commerce application called KaiKai. The application uses a custom cryptocurrency payment system called Kai-Ching. The founder of Cosmose reported that the blockchain-based payment system reduces transaction costs massively. It is roughly 50 times cheaper than using Stripe or PayPal. By late 2025, Kai-Ching handled 2 million monthly active users and 700,000 daily active wallets.
❍ Universal Authentication
Consumers using these Super-Apps do not need to understand blockchain mechanics. They experience frictionless onboarding through a feature called Fast Auth. Traditional blockchains require users to memorize complex 24-word seed phrases. Fast Auth eliminates this massive barrier.
A user simply registers for an application using an email address or facial recognition. The protocol handles all cryptographic signatures securely in the backend. Integrations with companies like Privy allow users to authenticate once. After one authentication, the user can interact with any connected chain. This architecture effectively ends the era of network-specific wallets.
VII. Will NEAR Survive?
An objective research report must stress-test its own thesis. A network displaying massive growth also carries systemic risks. NEAR Protocol faces several severe challenges that could halt its momentum entirely.
❍ The Centralized Stablecoin Risk
The most immediate existential threat is the USDC choke point. The artificial intelligence economy relies on stable currency. AI agents do not want to hold volatile assets. The May 2026 Keyrock report dropped a staggering statistic regarding this behavior. The data confirmed that 98.6 percent of all AI agent on-chain payments settle strictly in USDC.
USDC is a centralized stablecoin issued by a corporate entity named Circle. This means NEAR's entire artificial intelligence economy is dangerously centralized around one private company. Circle must comply strictly with United States regulators. If regulators restrict the usage of USDC in smart contracts, the agent economy halts immediately.
❍ The Narrowing Abstraction Moat
The second major threat is the narrowing abstraction moat. NEAR Protocol currently dominates the chain abstraction narrative. However, competitors are waking up to this reality rapidly. Solana is actively building native cross-chain tools to retain its user base. The Ethereum ecosystem hosts heavyweights like LayerZero. If competing chains successfully abstract their own complexity, NEAR loses its biggest unique selling proposition.
❍ The Quantum Computing Threat
The third existential risk involves complex cryptography. Chain abstraction relies heavily on secure cryptographic signatures. The system uses Multi-Party Computation networks to sign transactions across different blockchains. Traditional cryptography is highly vulnerable to future advancements in quantum computing.
If a quantum computer breaks these signatures, bad actors could drain funds across every connected chain. The developers scheduled a Post-Quantum-Safe Signing Testnet for the second quarter of 2026. The upgrade integrates FIPS-204 post-quantum cryptography directly into the blockchain. If the engineering team fails to secure these signatures against quantum threats in time, the entire cross-chain model is broken.
❍ The Enterprise AI Premium Risk
The final threat is the AI premium risk. The token currently trades at a massive premium based on future expectations. The market expects millions of enterprise-grade AI agents to utilize the blockchain. The current valuation prices the asset for absolute perfection.
Enterprise developers are highly conservative. A major corporation building an internal AI system might decide they prefer private centralized servers. Amazon Web Services provides reliable isolated infrastructure. If large corporations reject decentralized blockchain infrastructure for their AI agents, the anticipated on-chain volume will never arrive. The survival of the protocol depends entirely on whether the global economy actually requires decentralized AI or if centralized servers will win the market.
Deep Dive: How NEAR Protocol Becoming The Biggest AI Chain
The entire premise of the cryptocurrency industry has shifted. Developers spent the past fifteen years building blockchain infrastructure exclusively for human users. They designed browser wallets for human hands. They built decentralized exchanges for human traders. They created complex financial applications requiring constant human oversight. This era is rapidly coming to an end. The industry is no longer building for human traders. Developers are now building infrastructure for artificial intelligence agents.
This new internet architecture is called the Agentic Web. The Agentic Web is a digital environment where autonomous software programs operate freely. These software programs are called AI agents. An AI agent is a piece of code that makes independent decisions. These agents perform several complex tasks:
They manage complex financial portfolios across multiple networks.They purchase proprietary data sets directly from remote servers.They execute digital tasks on behalf of human users without requiring constant supervision.
These AI agents require a frictionless financial settlement layer to transact. Traditional banking rails fail completely at this specific task. Traditional payment processors like Visa use an outdated fee structure. They charge a fixed flat fee plus a percentage of the total transaction volume. A standard credit card transaction often costs a merchant ten cents plus two percent of the purchase price.
This fee structure destroys the profit margins of machine-to-machine commerce. An AI agent often executes thousands of micro-transactions per minute. An agent might pay a fraction of a cent to query a database. If an agent executes a payment of $0.31, a ten-cent flat fee consumes a massive portion of the capital. Traditional banks also require human identity verification. A software program cannot provide a physical passport to open a corporate bank account. AI agents must use blockchain infrastructure to interact with the global economy.
NEAR Protocol is stepping in to solve this exact problem. NEAR was an artificial intelligence company before it ever became a blockchain. Illia Polosukhin is the chief executive officer and co-founder of NEAR. He is a prominent artificial intelligence researcher who previously worked at Google Research. In 2017 he co-authored a seminal academic paper titled "Attention Is All You Need."
This specific research paper introduced the Transformer architecture. The Transformer architecture serves as the fundamental blueprint for modern large language models like ChatGPT and Claude. Polosukhin and his team understand the specific technical requirements of artificial intelligence. They designed NEAR to serve as the universal orchestration and settlement layer for this new machine economy. The platform allows AI agents to own digital assets and make independent financial decisions seamlessly.
II. The Surge of NEAR Protocol in 2026
The broader market fully realized the value of this architecture in early 2026. The network experienced massive growth across all metrics. This growth appeared in market data, user adoption, and major ecosystem milestones. The shift from experimental technology to investable infrastructure became undeniable.
❍ Explosive Price Action and Liquidations
The price action in May 2026 demonstrated this market shift clearly. The native token experienced a violent vertical price rally. The price of NEAR climbed from a low of $1.25 to break the $2.80 resistance level. This upward movement severely outperformed almost all top 100 cryptocurrencies.
The rapid price appreciation liquidated millions of dollars in short positions. Traders who bet against the network were forced to buy back the token at higher prices to cover their losses. This forced buying pressure accelerated the rally further. Traders grouped the asset alongside other AI-linked digital assets following a massive earnings report from NVIDIA.
❍ Network Volume and Revenue
The price surge was completely supported by fundamental network usage. The NEAR Intents system drove massive on-chain activity. NEAR Intents is a highly efficient cross-chain transaction routing system. Blockchain analytics platform DefiLlama published new data regarding this system in May 2026.
The data revealed staggering network usage:
The NEAR Intents cross-chain system processed over $19 billion in cumulative volume.The transaction volume generated approximately $32 million in protocol fees.Daily fee revenue crossed $400,000 during peak network activity.
❍ The Arrival of Wall Street Capital
Institutional validation arrived shortly after the on-chain data improved. Wall Street began viewing NEAR Protocol as enterprise-grade infrastructure. Traditional finance products expanded rapidly to capture this demand:
Grayscale Investments filed an S-1 registration statement with the SEC on January 20, 2026.This filing seeks to convert the existing Grayscale Near Trust (GSNR) into a spot exchange-traded fund.The Bitwise NEAR Staking exchange-traded product in Europe saw significant new inflows.The Bitwise product expanded to approximately $40 million in total assets under management.The Bitwise fund saw $7 million in new capital inflows in a single week.
III. Why NEAR Protocol is Pumping So Much
Market hype can drive short-term price action. Sustainable growth requires strong underlying tokenomics. The protocol completely overhauled its economic model in late 2025 and early 2026. These structural changes created severe deflationary pressure on the token supply.
❍ The Deflationary Fee Switch
The protocol activated a new financial mechanism on February 23, 2026. This mechanism fundamentally changed how the network handles transaction revenue. Most blockchain networks use fees to pay validators directly. The new mechanism redirects this capital entirely.
Exactly 100 percent of all fees generated by the NEAR Intents system now flow into an automated conversion mechanism. The system uses these collected protocol fees to execute open-market purchases of the NEAR token. Every single cross-chain transaction executed by an AI agent directly reduces the liquid token supply on exchanges. The protocol effectively buys its own token and removes it from circulation.
❍ The Halving Upgrade
Network inflation dictates the creation of new tokens over time. High inflation dilutes the value of existing tokens. The NEAR community executed a landmark governance shift in late 2025. The community voted to slash the maximum annual inflation of the entire network.
The inflation rate dropped dramatically from 5 percent down to 2.5 percent. This upgrade permanently restricted the new token supply. The network cut its emission schedule in half right as the artificial intelligence narrative spiked demand. Decreased supply and increased demand create an inevitable upward price trajectory.
❍ The 56x Valuation Disconnect
Arthur Hayes popularized a powerful valuation narrative for the network. Hayes is a prominent industry analyst and the co-founder of the BitMEX exchange. He published an investment thesis highlighting a massive valuation disconnect. He argued the market incorrectly prices NEAR as an old Layer 1 network rather than a highly profitable cross-chain execution business.
Hayes introduced a comparative financial framework to prove his point:
LayerZero is a leading interoperability protocol generating roughly $2.0 million in annualized fees.The NEAR Intents system generates roughly $36.3 million in annualized fees.NEAR Intents generates approximately 18 times the revenue of LayerZero.
Despite this overwhelming revenue dominance, the market gave LayerZero a higher valuation multiple. Hayes calculated that when combining base-chain gas and Intents fees, NEAR trades at a 56x annualized fee multiple. This makes NEAR fundamentally cheaper than competing networks like Ethereum, Solana, and Sui.
IV. The Agentic Stack of NEAR
A strong tokenomic model requires superior technical infrastructure. NEAR provides the specific tools that AI agents require. This combination of developer tools is called the Agentic Stack. The Agentic Stack makes NEAR the only logical home for autonomous software programs.
❍ Nightshade 3.0 and Massive Throughput
A standard blockchain processes transactions sequentially. This causes severe network congestion during high usage. NEAR solves this with Nightshade 3.0 Sharding. Sharding splits the blockchain network into smaller parallel pieces. Each piece processes its own transactions independently.
NEAR Protocol expanded its architecture to feature 9 parallel shards on the mainnet in 2025 and 2026. This expansion pushed network capabilities to unprecedented levels. In a publicly verifiable benchmark test, the network achieved massive throughput:
The system processed over 1,000,000 transactions per second.The test utilized 70 simulated shards across Google Cloud C4D virtual machines.The processors used were standard AMD EPYC chips without specialized hardware.The network maintained a 1.2-second finality rate.
❍ IronClaw and Trusted Execution Environments
Centralized artificial intelligence models lack transparency and security. The operating company can read user prompts and view sensitive financial data. NEAR solves this problem through User-Owned AI. In early 2026, NEAR AI officially launched IronClaw.
IronClaw is a highly secure open-source AI agent runtime environment. It is written entirely in the Rust programming language. Rust enforces memory safety at the compilation stage. This eliminates common vulnerabilities like buffer overflows entirely. IronClaw deploys AI agents inside encrypted hardware-secured areas called Trusted Execution Environments. The data remains fully encrypted in memory from the moment the system boots. The cloud provider and the node operator cannot see the data.
❍ Automatic Data Anonymization
The stack also includes automatic data anonymization. Advanced technology automatically scrubs sensitive information from user prompts. It scrubs passwords, API keys, and financial data before the prompt routes to large language models. The network uses real-time leak detection. It actively scans all outbound network traffic. If the system detects any data resembling a secret heading out, it automatically blocks the transmission.
V. How NEAR Protocol is Killing the Competition
The blockchain industry remains highly competitive. Ethereum holds the majority of decentralized finance liquidity. Solana provides excellent execution speed. NEAR Protocol contrasts itself against both networks by focusing on a massive technical differentiator. NEAR is hiding the blockchain entirely from the user.
❍ True Chain Abstraction
The industry struggles constantly with network fragmentation. Users must download specific browser wallets for specific networks. They must buy native tokens to pay for gas fees. They must use complex bridge protocols. AI agents also struggle with this fragmentation. Programming an agent to manage gas tokens across twelve different networks is incredibly complex.
NEAR solves this through True Chain Abstraction. While Solana fights to optimize its base-layer speed, NEAR makes the base layer invisible. The mechanism powering this is the NEAR Intents system. NEAR Intents uses a novel transaction architecture. Users and AI agents simply declare a desired outcome. A user can request to swap USDC on Ethereum for SOL on Solana. The user does not need to hold Ethereum to pay for gas. The user signs the request once.
❍ The Unified Liquidity Layer
The network routes the request to a decentralized network of third-party solvers. These solvers are professional market makers. They handle all the bridging and wrapping in the background automatically. This architecture creates a Unified Liquidity Layer. It effectively kills traditional bridge protocols.
By unifying fragmented liquidity, NEAR provides developers with an unparalleled advantage. A developer integrates a single API. That single integration connects their application to capital across the entire cryptocurrency ecosystem. It connects to Ethereum, Solana, Bitcoin, Arbitrum, and dozens of other networks simultaneously.
❍ The Mathematics of Micro-Payments
This unified architecture delivers a massive cost advantage. Artificial intelligence agents require extremely low transaction fees to operate profitably. Crypto market maker Keyrock published a comprehensive research report in May 2026. The data revealed critical metrics about machine commerce:
The average payment amount executed by an AI agent is exactly $0.31.Transferring assets on efficient blockchain infrastructure costs a fraction of a cent.An average transaction fee costs approximately $0.0001.This fee represents roughly 0.03 percent of a $0.31 transaction.
This near-zero transaction cost makes machine-to-machine commerce mathematically viable. Software can finally pay software without destroying its own profit margin.
VI. The Super-App Consumer Layer
Superior technology is utterly useless without actual human adoption. A network must prove its technology can reach normal consumers. NEAR Protocol proves this through major integrations and consumer applications. The technology is actively reaching tens of millions of humans right now.
❍ The Brave Browser Integration
The most significant consumer milestone occurred in March 2026. The Brave Browser integrated the NEAR Intents system directly into its native architecture. The Brave Browser boasts over 70 million monthly active users globally. The integration rolled out in the v1.88 desktop release.
This integration placed native cross-chain execution directly into the browser. Users experience several new capabilities:
Users can swap assets across Bitcoin, Solana, and Ethereum natively.Users can execute shielded Zcash transactions natively for total privacy.Users do not need to download third-party extensions or manage gas tokens.
❍ Cosmose AI and the Kai-Ching System
The protocol supports the rise of consumer Super-Apps. These applications provide a standard web experience while using blockchain rails in the background. Cosmose AI is a technology company that tracks offline retail shopping habits. The company previously partnered with Stripe for its payment processing.
Cosmose terminated its partnership with Stripe and migrated entirely to the NEAR Protocol. The company launched an e-commerce application called KaiKai. The application uses a custom cryptocurrency payment system called Kai-Ching. The founder of Cosmose reported that the blockchain-based payment system reduces transaction costs massively. It is roughly 50 times cheaper than using Stripe or PayPal. By late 2025, Kai-Ching handled 2 million monthly active users and 700,000 daily active wallets.
❍ Universal Authentication
Consumers using these Super-Apps do not need to understand blockchain mechanics. They experience frictionless onboarding through a feature called Fast Auth. Traditional blockchains require users to memorize complex 24-word seed phrases. Fast Auth eliminates this massive barrier.
A user simply registers for an application using an email address or facial recognition. The protocol handles all cryptographic signatures securely in the backend. Integrations with companies like Privy allow users to authenticate once. After one authentication, the user can interact with any connected chain. This architecture effectively ends the era of network-specific wallets.
VII. Will NEAR Survive?
An objective research report must stress-test its own thesis. A network displaying massive growth also carries systemic risks. NEAR Protocol faces several severe challenges that could halt its momentum entirely.
❍ The Centralized Stablecoin Risk
The most immediate existential threat is the USDC choke point. The artificial intelligence economy relies on stable currency. AI agents do not want to hold volatile assets. The May 2026 Keyrock report dropped a staggering statistic regarding this behavior. The data confirmed that 98.6 percent of all AI agent on-chain payments settle strictly in USDC.
USDC is a centralized stablecoin issued by a corporate entity named Circle. This means NEAR's entire artificial intelligence economy is dangerously centralized around one private company. Circle must comply strictly with United States regulators. If regulators restrict the usage of USDC in smart contracts, the agent economy halts immediately.
❍ The Narrowing Abstraction Moat
The second major threat is the narrowing abstraction moat. NEAR Protocol currently dominates the chain abstraction narrative. However, competitors are waking up to this reality rapidly. Solana is actively building native cross-chain tools to retain its user base. The Ethereum ecosystem hosts heavyweights like LayerZero. If competing chains successfully abstract their own complexity, NEAR loses its biggest unique selling proposition.
❍ The Quantum Computing Threat
The third existential risk involves complex cryptography. Chain abstraction relies heavily on secure cryptographic signatures. The system uses Multi-Party Computation networks to sign transactions across different blockchains. Traditional cryptography is highly vulnerable to future advancements in quantum computing.
If a quantum computer breaks these signatures, bad actors could drain funds across every connected chain. The developers scheduled a Post-Quantum-Safe Signing Testnet for the second quarter of 2026. The upgrade integrates FIPS-204 post-quantum cryptography directly into the blockchain. If the engineering team fails to secure these signatures against quantum threats in time, the entire cross-chain model is broken.
❍ The Enterprise AI Premium Risk
The final threat is the AI premium risk. The token currently trades at a massive premium based on future expectations. The market expects millions of enterprise-grade AI agents to utilize the blockchain. The current valuation prices the asset for absolute perfection.
Enterprise developers are highly conservative. A major corporation building an internal AI system might decide they prefer private centralized servers. Amazon Web Services provides reliable isolated infrastructure. If large corporations reject decentralized blockchain infrastructure for their AI agents, the anticipated on-chain volume will never arrive. The survival of the protocol depends entirely on whether the global economy actually requires decentralized AI or if centralized servers will win the market.
This new internet architecture is called the Agentic Web. The Agentic Web is a digital environment where autonomous software programs operate freely. These software programs are called AI agents. An AI agent is a piece of code that makes independent decisions. These agents perform several complex tasks:
They manage complex financial portfolios across multiple networks.They purchase proprietary data sets directly from remote servers.They execute digital tasks on behalf of human users without requiring constant supervision.
These AI agents require a frictionless financial settlement layer to transact. Traditional banking rails fail completely at this specific task. Traditional payment processors like Visa use an outdated fee structure. They charge a fixed flat fee plus a percentage of the total transaction volume. A standard credit card transaction often costs a merchant ten cents plus two percent of the purchase price.
This fee structure destroys the profit margins of machine-to-machine commerce. An AI agent often executes thousands of micro-transactions per minute. An agent might pay a fraction of a cent to query a database. If an agent executes a payment of $0.31, a ten-cent flat fee consumes a massive portion of the capital. Traditional banks also require human identity verification. A software program cannot provide a physical passport to open a corporate bank account. AI agents must use blockchain infrastructure to interact with the global economy.
NEAR Protocol is stepping in to solve this exact problem. NEAR was an artificial intelligence company before it ever became a blockchain. Illia Polosukhin is the chief executive officer and co-founder of NEAR. He is a prominent artificial intelligence researcher who previously worked at Google Research. In 2017 he co-authored a seminal academic paper titled "Attention Is All You Need."
This specific research paper introduced the Transformer architecture. The Transformer architecture serves as the fundamental blueprint for modern large language models like ChatGPT and Claude. Polosukhin and his team understand the specific technical requirements of artificial intelligence. They designed NEAR to serve as the universal orchestration and settlement layer for this new machine economy. The platform allows AI agents to own digital assets and make independent financial decisions seamlessly.
II. The Surge of NEAR Protocol in 2026
The broader market fully realized the value of this architecture in early 2026. The network experienced massive growth across all metrics. This growth appeared in market data, user adoption, and major ecosystem milestones. The shift from experimental technology to investable infrastructure became undeniable.
❍ Explosive Price Action and Liquidations
The price action in May 2026 demonstrated this market shift clearly. The native token experienced a violent vertical price rally. The price of NEAR climbed from a low of $1.25 to break the $2.80 resistance level. This upward movement severely outperformed almost all top 100 cryptocurrencies.
The rapid price appreciation liquidated millions of dollars in short positions. Traders who bet against the network were forced to buy back the token at higher prices to cover their losses. This forced buying pressure accelerated the rally further. Traders grouped the asset alongside other AI-linked digital assets following a massive earnings report from NVIDIA.
❍ Network Volume and Revenue
The price surge was completely supported by fundamental network usage. The NEAR Intents system drove massive on-chain activity. NEAR Intents is a highly efficient cross-chain transaction routing system. Blockchain analytics platform DefiLlama published new data regarding this system in May 2026.
The data revealed staggering network usage:
The NEAR Intents cross-chain system processed over $19 billion in cumulative volume.The transaction volume generated approximately $32 million in protocol fees.Daily fee revenue crossed $400,000 during peak network activity.
❍ The Arrival of Wall Street Capital
Institutional validation arrived shortly after the on-chain data improved. Wall Street began viewing NEAR Protocol as enterprise-grade infrastructure. Traditional finance products expanded rapidly to capture this demand:
Grayscale Investments filed an S-1 registration statement with the SEC on January 20, 2026.This filing seeks to convert the existing Grayscale Near Trust (GSNR) into a spot exchange-traded fund.The Bitwise NEAR Staking exchange-traded product in Europe saw significant new inflows.The Bitwise product expanded to approximately $40 million in total assets under management.The Bitwise fund saw $7 million in new capital inflows in a single week.
III. Why NEAR Protocol is Pumping So Much
Market hype can drive short-term price action. Sustainable growth requires strong underlying tokenomics. The protocol completely overhauled its economic model in late 2025 and early 2026. These structural changes created severe deflationary pressure on the token supply.
❍ The Deflationary Fee Switch
The protocol activated a new financial mechanism on February 23, 2026. This mechanism fundamentally changed how the network handles transaction revenue. Most blockchain networks use fees to pay validators directly. The new mechanism redirects this capital entirely.
Exactly 100 percent of all fees generated by the NEAR Intents system now flow into an automated conversion mechanism. The system uses these collected protocol fees to execute open-market purchases of the NEAR token. Every single cross-chain transaction executed by an AI agent directly reduces the liquid token supply on exchanges. The protocol effectively buys its own token and removes it from circulation.
❍ The Halving Upgrade
Network inflation dictates the creation of new tokens over time. High inflation dilutes the value of existing tokens. The NEAR community executed a landmark governance shift in late 2025. The community voted to slash the maximum annual inflation of the entire network.
The inflation rate dropped dramatically from 5 percent down to 2.5 percent. This upgrade permanently restricted the new token supply. The network cut its emission schedule in half right as the artificial intelligence narrative spiked demand. Decreased supply and increased demand create an inevitable upward price trajectory.
❍ The 56x Valuation Disconnect
Arthur Hayes popularized a powerful valuation narrative for the network. Hayes is a prominent industry analyst and the co-founder of the BitMEX exchange. He published an investment thesis highlighting a massive valuation disconnect. He argued the market incorrectly prices NEAR as an old Layer 1 network rather than a highly profitable cross-chain execution business.
Hayes introduced a comparative financial framework to prove his point:
LayerZero is a leading interoperability protocol generating roughly $2.0 million in annualized fees.The NEAR Intents system generates roughly $36.3 million in annualized fees.NEAR Intents generates approximately 18 times the revenue of LayerZero.
Despite this overwhelming revenue dominance, the market gave LayerZero a higher valuation multiple. Hayes calculated that when combining base-chain gas and Intents fees, NEAR trades at a 56x annualized fee multiple. This makes NEAR fundamentally cheaper than competing networks like Ethereum, Solana, and Sui.
IV. The Agentic Stack of NEAR
A strong tokenomic model requires superior technical infrastructure. NEAR provides the specific tools that AI agents require. This combination of developer tools is called the Agentic Stack. The Agentic Stack makes NEAR the only logical home for autonomous software programs.
❍ Nightshade 3.0 and Massive Throughput
A standard blockchain processes transactions sequentially. This causes severe network congestion during high usage. NEAR solves this with Nightshade 3.0 Sharding. Sharding splits the blockchain network into smaller parallel pieces. Each piece processes its own transactions independently.
NEAR Protocol expanded its architecture to feature 9 parallel shards on the mainnet in 2025 and 2026. This expansion pushed network capabilities to unprecedented levels. In a publicly verifiable benchmark test, the network achieved massive throughput:
The system processed over 1,000,000 transactions per second.The test utilized 70 simulated shards across Google Cloud C4D virtual machines.The processors used were standard AMD EPYC chips without specialized hardware.The network maintained a 1.2-second finality rate.
❍ IronClaw and Trusted Execution Environments
Centralized artificial intelligence models lack transparency and security. The operating company can read user prompts and view sensitive financial data. NEAR solves this problem through User-Owned AI. In early 2026, NEAR AI officially launched IronClaw.
IronClaw is a highly secure open-source AI agent runtime environment. It is written entirely in the Rust programming language. Rust enforces memory safety at the compilation stage. This eliminates common vulnerabilities like buffer overflows entirely. IronClaw deploys AI agents inside encrypted hardware-secured areas called Trusted Execution Environments. The data remains fully encrypted in memory from the moment the system boots. The cloud provider and the node operator cannot see the data.
❍ Automatic Data Anonymization
The stack also includes automatic data anonymization. Advanced technology automatically scrubs sensitive information from user prompts. It scrubs passwords, API keys, and financial data before the prompt routes to large language models. The network uses real-time leak detection. It actively scans all outbound network traffic. If the system detects any data resembling a secret heading out, it automatically blocks the transmission.
V. How NEAR Protocol is Killing the Competition
The blockchain industry remains highly competitive. Ethereum holds the majority of decentralized finance liquidity. Solana provides excellent execution speed. NEAR Protocol contrasts itself against both networks by focusing on a massive technical differentiator. NEAR is hiding the blockchain entirely from the user.
❍ True Chain Abstraction
The industry struggles constantly with network fragmentation. Users must download specific browser wallets for specific networks. They must buy native tokens to pay for gas fees. They must use complex bridge protocols. AI agents also struggle with this fragmentation. Programming an agent to manage gas tokens across twelve different networks is incredibly complex.
NEAR solves this through True Chain Abstraction. While Solana fights to optimize its base-layer speed, NEAR makes the base layer invisible. The mechanism powering this is the NEAR Intents system. NEAR Intents uses a novel transaction architecture. Users and AI agents simply declare a desired outcome. A user can request to swap USDC on Ethereum for SOL on Solana. The user does not need to hold Ethereum to pay for gas. The user signs the request once.
❍ The Unified Liquidity Layer
The network routes the request to a decentralized network of third-party solvers. These solvers are professional market makers. They handle all the bridging and wrapping in the background automatically. This architecture creates a Unified Liquidity Layer. It effectively kills traditional bridge protocols.
By unifying fragmented liquidity, NEAR provides developers with an unparalleled advantage. A developer integrates a single API. That single integration connects their application to capital across the entire cryptocurrency ecosystem. It connects to Ethereum, Solana, Bitcoin, Arbitrum, and dozens of other networks simultaneously.
❍ The Mathematics of Micro-Payments
This unified architecture delivers a massive cost advantage. Artificial intelligence agents require extremely low transaction fees to operate profitably. Crypto market maker Keyrock published a comprehensive research report in May 2026. The data revealed critical metrics about machine commerce:
The average payment amount executed by an AI agent is exactly $0.31.Transferring assets on efficient blockchain infrastructure costs a fraction of a cent.An average transaction fee costs approximately $0.0001.This fee represents roughly 0.03 percent of a $0.31 transaction.
This near-zero transaction cost makes machine-to-machine commerce mathematically viable. Software can finally pay software without destroying its own profit margin.
VI. The Super-App Consumer Layer
Superior technology is utterly useless without actual human adoption. A network must prove its technology can reach normal consumers. NEAR Protocol proves this through major integrations and consumer applications. The technology is actively reaching tens of millions of humans right now.
❍ The Brave Browser Integration
The most significant consumer milestone occurred in March 2026. The Brave Browser integrated the NEAR Intents system directly into its native architecture. The Brave Browser boasts over 70 million monthly active users globally. The integration rolled out in the v1.88 desktop release.
This integration placed native cross-chain execution directly into the browser. Users experience several new capabilities:
Users can swap assets across Bitcoin, Solana, and Ethereum natively.Users can execute shielded Zcash transactions natively for total privacy.Users do not need to download third-party extensions or manage gas tokens.
❍ Cosmose AI and the Kai-Ching System
The protocol supports the rise of consumer Super-Apps. These applications provide a standard web experience while using blockchain rails in the background. Cosmose AI is a technology company that tracks offline retail shopping habits. The company previously partnered with Stripe for its payment processing.
Cosmose terminated its partnership with Stripe and migrated entirely to the NEAR Protocol. The company launched an e-commerce application called KaiKai. The application uses a custom cryptocurrency payment system called Kai-Ching. The founder of Cosmose reported that the blockchain-based payment system reduces transaction costs massively. It is roughly 50 times cheaper than using Stripe or PayPal. By late 2025, Kai-Ching handled 2 million monthly active users and 700,000 daily active wallets.
❍ Universal Authentication
Consumers using these Super-Apps do not need to understand blockchain mechanics. They experience frictionless onboarding through a feature called Fast Auth. Traditional blockchains require users to memorize complex 24-word seed phrases. Fast Auth eliminates this massive barrier.
A user simply registers for an application using an email address or facial recognition. The protocol handles all cryptographic signatures securely in the backend. Integrations with companies like Privy allow users to authenticate once. After one authentication, the user can interact with any connected chain. This architecture effectively ends the era of network-specific wallets.
VII. Will NEAR Survive?
An objective research report must stress-test its own thesis. A network displaying massive growth also carries systemic risks. NEAR Protocol faces several severe challenges that could halt its momentum entirely.
❍ The Centralized Stablecoin Risk
The most immediate existential threat is the USDC choke point. The artificial intelligence economy relies on stable currency. AI agents do not want to hold volatile assets. The May 2026 Keyrock report dropped a staggering statistic regarding this behavior. The data confirmed that 98.6 percent of all AI agent on-chain payments settle strictly in USDC.
USDC is a centralized stablecoin issued by a corporate entity named Circle. This means NEAR's entire artificial intelligence economy is dangerously centralized around one private company. Circle must comply strictly with United States regulators. If regulators restrict the usage of USDC in smart contracts, the agent economy halts immediately.
❍ The Narrowing Abstraction Moat
The second major threat is the narrowing abstraction moat. NEAR Protocol currently dominates the chain abstraction narrative. However, competitors are waking up to this reality rapidly. Solana is actively building native cross-chain tools to retain its user base. The Ethereum ecosystem hosts heavyweights like LayerZero. If competing chains successfully abstract their own complexity, NEAR loses its biggest unique selling proposition.
❍ The Quantum Computing Threat
The third existential risk involves complex cryptography. Chain abstraction relies heavily on secure cryptographic signatures. The system uses Multi-Party Computation networks to sign transactions across different blockchains. Traditional cryptography is highly vulnerable to future advancements in quantum computing.
If a quantum computer breaks these signatures, bad actors could drain funds across every connected chain. The developers scheduled a Post-Quantum-Safe Signing Testnet for the second quarter of 2026. The upgrade integrates FIPS-204 post-quantum cryptography directly into the blockchain. If the engineering team fails to secure these signatures against quantum threats in time, the entire cross-chain model is broken.
❍ The Enterprise AI Premium Risk
The final threat is the AI premium risk. The token currently trades at a massive premium based on future expectations. The market expects millions of enterprise-grade AI agents to utilize the blockchain. The current valuation prices the asset for absolute perfection.
Enterprise developers are highly conservative. A major corporation building an internal AI system might decide they prefer private centralized servers. Amazon Web Services provides reliable isolated infrastructure. If large corporations reject decentralized blockchain infrastructure for their AI agents, the anticipated on-chain volume will never arrive. The survival of the protocol depends entirely on whether the global economy actually requires decentralized AI or if centralized servers will win the market.
1
152
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙀𝙏𝙁 𝙅𝙪𝙣𝙚 𝙤𝙪𝙩𝙛𝙡𝙤𝙬𝙨 𝙝𝙞𝙩 𝙧𝙚𝙘𝙤𝙧𝙙 $4.1𝘽 — 𝙡𝙖𝙧𝙜𝙚𝙨𝙩 𝙢𝙤𝙣𝙩𝙝𝙡𝙮 𝙧𝙚𝙙𝙚𝙢𝙥𝙩𝙞𝙤𝙣 𝙨𝙞𝙣𝙘𝙚 𝙡𝙖𝙪𝙣𝙘𝙝
-
U.S. spot Bitcoin ETFs logged over $4.1 billion in net outflows in June 2026, topping the prior monthly record of $3.56 billion. Outflows for June 22–26 alone reached $1.79 billion.
© Bloomberg
$BTC 𝘽𝙞𝙩𝙘𝙤𝙞𝙣 𝙀𝙏𝙁 𝙅𝙪𝙣𝙚 𝙤𝙪𝙩𝙛𝙡𝙤𝙬𝙨 𝙝𝙞𝙩 𝙧𝙚𝙘𝙤𝙧𝙙 $4.1𝘽 — 𝙡𝙖𝙧𝙜𝙚𝙨𝙩 𝙢𝙤𝙣𝙩𝙝𝙡𝙮 𝙧𝙚𝙙𝙚𝙢𝙥𝙩𝙞𝙤𝙣 𝙨𝙞𝙣𝙘𝙚 𝙡𝙖𝙪𝙣𝙘𝙝
-
U.S. spot Bitcoin ETFs logged over $4.1 billion in net outflows in June 2026, topping the prior monthly record of $3.56 billion. Outflows for June 22–26 alone reached $1.79 billion.
© Bloomberg
-
U.S. spot Bitcoin ETFs logged over $4.1 billion in net outflows in June 2026, topping the prior monthly record of $3.56 billion. Outflows for June 22–26 alone reached $1.79 billion.
© Bloomberg
1
1.1k
🔅𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼 𝗶𝗻 𝗹𝗮𝘀𝘁 24𝗛?🔅
-
• Open USD coalition launches shared reserve revenue model
• UK FCA finalizes crypto rules for 2027
• SEC reviews framework for next-generation crypto ETFs
• MetaMask launches yield account on Monad
• OK debuts AI marketplace with stablecoin payments
• Sharplink buys 10,000 ETH during market weakness
• CZ says MiCA application was close to approval
💡 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?🔅
-
• Open USD coalition launches shared reserve revenue model
• UK FCA finalizes crypto rules for 2027
• SEC reviews framework for next-generation crypto ETFs
• MetaMask launches yield account on Monad
• OK debuts AI marketplace with stablecoin payments
• Sharplink buys 10,000 ETH during market weakness
• CZ says MiCA application was close to approval
💡 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
-
• Open USD coalition launches shared reserve revenue model
• UK FCA finalizes crypto rules for 2027
• SEC reviews framework for next-generation crypto ETFs
• MetaMask launches yield account on Monad
• OK debuts AI marketplace with stablecoin payments
• Sharplink buys 10,000 ETH during market weakness
• CZ says MiCA application was close to approval
💡 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
147
Verified
$ENA 𝙎𝙩𝙖𝙗𝙡𝙚𝙘𝙤𝙞𝙣𝙓 (𝙀𝙩𝙝𝙚𝙣𝙖 𝙞𝙣𝙛𝙧𝙖𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚) 𝙘𝙤𝙢𝙥𝙡𝙚𝙩𝙚𝙨 𝙎𝙋𝘼𝘾 𝙢𝙚𝙧𝙜𝙚𝙧, 𝙡𝙞𝙨𝙩𝙨 𝙤𝙣 𝙉𝙖𝙨𝙙𝙖𝙦 𝙖𝙨 "𝙐𝙎𝘿𝙀"
-
StablecoinX, the first publicly listed stablecoin infrastructure company focused on the Ethena ecosystem, completed its merger with TLGY Acquisition Corp and began trading on Nasdaq under ticker "USDE." The company holds about 3B ENA tokens, roughly 20% of total supply.
© globenewswire
$ENA 𝙎𝙩𝙖𝙗𝙡𝙚𝙘𝙤𝙞𝙣𝙓 (𝙀𝙩𝙝𝙚𝙣𝙖 𝙞𝙣𝙛𝙧𝙖𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚) 𝙘𝙤𝙢𝙥𝙡𝙚𝙩𝙚𝙨 𝙎𝙋𝘼𝘾 𝙢𝙚𝙧𝙜𝙚𝙧, 𝙡𝙞𝙨𝙩𝙨 𝙤𝙣 𝙉𝙖𝙨𝙙𝙖𝙦 𝙖𝙨 "𝙐𝙎𝘿𝙀"
-
StablecoinX, the first publicly listed stablecoin infrastructure company focused on the Ethena ecosystem, completed its merger with TLGY Acquisition Corp and began trading on Nasdaq under ticker "USDE." The company holds about 3B ENA tokens, roughly 20% of total supply.
© globenewswire
-
StablecoinX, the first publicly listed stablecoin infrastructure company focused on the Ethena ecosystem, completed its merger with TLGY Acquisition Corp and began trading on Nasdaq under ticker "USDE." The company holds about 3B ENA tokens, roughly 20% of total supply.
© globenewswire
1
3
1.8k
$BTC Michael Saylor's Strategy announces a new framework to "preserve long-term Bitcoin exposure" with a "BTC monetization program" 👀
© Coindesk
$BTC Michael Saylor's Strategy announces a new framework to "preserve long-term Bitcoin exposure" with a "BTC monetization program" 👀
© Coindesk
© Coindesk
2
166
$BTC Strategy increased its USD Reserve by $300M to $1.4B, plans continued replenishment to support Digital Credit securities credit quality.
-
The company also acquired 520 #Bitcoin for $35M, bringing total Bitcoin Reserve to 847,363 $BTC
© Bitcoin.com
$BTC Strategy increased its USD Reserve by $300M to $1.4B, plans continued replenishment to support Digital Credit securities credit quality.
-
The company also acquired 520 #Bitcoin for $35M, bringing total Bitcoin Reserve to 847,363 $BTC
© Bitcoin.com
-
The company also acquired 520 #Bitcoin for $35M, bringing total Bitcoin Reserve to 847,363 $BTC
© Bitcoin.com
1
173
USD/JPY is near a multi-decade high while the BOJ hikes to 1%, its highest since 1995.
-
A hawkish Fed is tightening harder, keeping the pair bid. Cheap yen funds leveraged risk bets.
If that carry trade unwinds, crypto, a tail-risk asset, sells off first.
© Bitfinex
USD/JPY is near a multi-decade high while the BOJ hikes to 1%, its highest since 1995.
-
A hawkish Fed is tightening harder, keeping the pair bid. Cheap yen funds leveraged risk bets.
If that carry trade unwinds, crypto, a tail-risk asset, sells off first.
© Bitfinex
-
A hawkish Fed is tightening harder, keeping the pair bid. Cheap yen funds leveraged risk bets.
If that carry trade unwinds, crypto, a tail-risk asset, sells off first.
© Bitfinex
1
90
$SOL $SPCX Another weekly all-time high for tokenized RWA volume on @Solana_Official at $1.42B
-
© Blockworks
$SOL $SPCX Another weekly all-time high for tokenized RWA volume on @Solana Official at $1.42B
-
© Blockworks
-
© Blockworks
SOL-0.10%
SPCXUS+0.66%
1
2
779
Companies are burning through their entire annual AI budgets in a single quarter.
Companies are burning through their entire annual AI budgets in a single quarter.
NOWUS+1.11%
1
149
Partly True
$HYPE $BNB $ONDO Centralized crypto exchanges are no longer simply venues for buying and selling digital assets.
-
Leading platforms are evolving into broader financial ecosystems where crypto trading sits alongside payments, savings, yield, cards, and increasingly access to traditional financial assets through RWA perpetuals, tokenized equities, and real-share trading.
In 2026, crypto exchanges processed nearly $1T year-to-date across RWA perpetuals, with Binance accounting for 60.9% of total volume.
© Coindesk
$HYPE $BNB $ONDO Centralized crypto exchanges are no longer simply venues for buying and selling digital assets.
-
Leading platforms are evolving into broader financial ecosystems where crypto trading sits alongside payments, savings, yield, cards, and increasingly access to traditional financial assets through RWA perpetuals, tokenized equities, and real-share trading.
In 2026, crypto exchanges processed nearly $1T year-to-date across RWA perpetuals, with Binance accounting for 60.9% of total volume.
© Coindesk
-
Leading platforms are evolving into broader financial ecosystems where crypto trading sits alongside payments, savings, yield, cards, and increasingly access to traditional financial assets through RWA perpetuals, tokenized equities, and real-share trading.
In 2026, crypto exchanges processed nearly $1T year-to-date across RWA perpetuals, with Binance accounting for 60.9% of total volume.
© Coindesk
1
847
Verified
$TAO DCG-backed Yuma has launched the Yuma Total Market Fund, giving institutional investors exposure to Bittensor's TAO token and AI-focused subnets.
© Coinmarketcap
$TAO DCG-backed Yuma has launched the Yuma Total Market Fund, giving institutional investors exposure to Bittensor's TAO token and AI-focused subnets.
© Coinmarketcap
© Coinmarketcap
1
4
541
Log in to explore more content
Join global crypto users on Binance Square
⚡️ Get latest and useful information about crypto.
💬 Trusted by the world’s largest crypto exchange.
👍 Discover real insights from verified creators.
Email / Phone number
Trending Topics
OilPriceFalls
218,600 views
1,615 Discussing
#oilpricefalls
𝗡𝗘𝗪𝗦
📰
President Trump demands gasoline retailers immediately drop their prices as oil falls
to $68 per barrel.
Trump warns of big problems ahead if retailers continue gouging American drivers and called out California’s heavy gas taxes.
──────────────────────────────
TRUMP'S POST:
"Gasoline Retailers must get their Prices down, IMMEDIATELY! They’re too high considering that Oil is now at $68 a Barrel, and heading south. The Retailers must quickly react to this statement, and do what they know is right — DROP YOUR PRICE FOR OUR GREAT AMERICAN PEOPLE! There will be no gauging, which is totally illegal. If Retailers don’t do this, big problems lie ahead! Start targeting around the $2.50 a Gallon number, and California should stop charging such heavy Taxes on their Gasoline. Soon the Tax will be higher than the Product itself, and the United States will not stand for it, nor will the People of California, who are being abused by these ridiculous Taxes, and by their own Government. President DONALD J. TRUMP"
$MUon
$B2
$MAGMA
Its Afridi Official
·2 Likes
·2.2k views
SpotSilverRises3%To$60.10
567 views
43 Discussing
KoreanWonWeakestSince2009
63 views
27 Discussing