Written by: Pink Brains
Compiled by: AididiaoJP, Foresight News
The existence of decentralized AI stems from the structural bottlenecks inherent in centralized AI, bottlenecks that cannot be resolved through capital and code alone.
- Computing resources are scarce and expensive
- Excessive concentration of control
- Model output is unverifiable
- Training data is becoming increasingly difficult to obtain
Computing resources are scarce and expensive
The GPU infrastructure market is projected to grow from $10 billion in 2025 to $77 billion in 2035. Data center GPUs have been sold out for several months. The decentralized computing market is projected to grow from $9 billion in 2024 to $22 billion in 2035 (Research and Markets data). This figure only holds true if you believe the shortage is a structural rather than a cyclical problem, which we believe is a structural problem.
Excessive concentration of control
ChatGPT, Gemini, Grok, and Claude are all owned and operated by a few private companies. Current AI policies assume that only a few entities with access to massive computing resources can train powerful systems. Once this assumption is broken, the landscape of who can build cutting-edge intelligence will be completely transformed.
The output result cannot be verified.
When the model makes decisions, users have no way of verifying whether the correct model is being run, whether the calculations are being performed correctly, or whether sensitive data has been leaked. This is tolerable for chatbots, but it becomes completely unacceptable when AI is handling loans, healthcare, or autonomous agents operating real-time wallets.
Acquiring training data is becoming increasingly difficult due to privacy concerns and regulations.
A centralized crawler located in a single AWS region will soon be rate-limited, geographically blocked, or fed poisoned caches. As a16z stated in its 2026 outlook, privacy is becoming "the most important moat in the crypto space."
AI needs blockchain to make intelligence open, verifiable, and economically accessible.
Decentralized AI Technology Stack Map
- Application and Service Layer: AI agents can do many things, but in the crypto space, the two dominant use cases are currently agentic finance and agentic payments.
- Middleware layer: Connecting organizations—from the framework for building and identifying agents, agent markets, to the coordination layer.
- Infrastructure Layer: The underlying resources for AI—privacy and verification layer, computation, inference, training, data, and storage.
Application and Service Layer
Agent finance translates natural language prompts into on-chain actions.
@gizatechxyz's ARMA broker has processed over $4.6 billion in brokered transaction volume in selected lending markets—running block-by-block, non-custodial on EigenLayer's AVS framework.
@Infinit_Labs runs a cluster of more than 20 professional agents that can translate intents like "earn $1,000 a month with 1 BTC" into one-click strategies on Ethereum, Solana, and Base.
@coinvestai by Liquid will execute directly embedded ChatGPT and Claude in real time, supporting trading in 500+ markets via the Model Context Protocol.
@minara integrates Hyperliquid and recently added Lighter. It runs a complete "analysis → decision → execution" transaction loop through the DMind model and 50+ integrations.
@Cod3xOrg: A network of lightweight AI agents that translates intents into on-chain transactions that are built and executed.
@Zyfai_: A self-hosted DeFAI agent that automates and optimizes yield farming, continuously rebalancing capital across protocols to chase risk-adjusted APY without human intervention.
In the prediction market arena, @SynthdataCo is a Bittensor subnet running a decentralized predictive finance intelligence network. Miner competition models short-term price uncertainty. It is already providing real-time data for products like Mode AI Quant in the Kalshi crypto market.
Proxy payment: Machine payment machine
Just as the internet became the communication layer of the digital economy, blockchain and stablecoins are becoming the settlement layer for proxy payments.
As of May 2026, x402 has processed over 173 million transactions on Base and Solana. The x402 Foundation members include Google, Visa, AWS, Circle, Anthropic, Stripe, and Cloudflare. Stripe began using it in February 2026; AWS launched native AgentCore Payments.
Buyer and seller activity is increasing, with most transactions related to genuine pay-as-you-go usage: API calls, AI inference services, agent commerce, and similar workloads. The initial hype cycle has cooled, but the underlying traction is starting to catch up.
Meanwhile, Stripe and Tempo's Machine Payments Protocol is emerging as a second track, having recorded over 411,900 transactions and 9,600 buyers since its launch.
These networks collectively demonstrate that machine-to-machine commerce is shifting in a broader direction, with software agents capable of autonomous transactions at machine speed.
Middleware layer
As the number of agents increases, the core challenge becomes coordination: how agents can discover each other, prove their identities, and conduct transactions without human intervention.
The trust gap here is the bottleneck. The agency business is estimated to be worth $1.5 trillion to $5 trillion by 2030, but adoption is limited by one thing—most users are willing to let AI do research, but few are willing to let AI actually make purchases.
Today's systems still rely on API keys, and almost no system treats agents as entities with identities.
@GoKiteAI is building a dedicated L1 that uses identity and payments as native primitives. ERC-8004 is an Ethereum standard that provides agents with portable on-chain identity and reputation that can be followed across chains.
On the market side, @virtuals_io is the operating system for the agent economy on Base. As of June 2026, it had processed over 2.38 million agent tasks, generating nearly $480 million in "agent GDP".
But the jewel in this layer is Bittensor. It's a network of specialized subnets, each a micro-economy where miners run AI models, validators score outputs, and TAO emissions flow to those who create the most useful jobs. Three mechanisms make it economically serious:
- The halving in December 2025 will reduce the daily TAO issuance from 7,200 to 3,600, corresponding to a hard cap of 21 million.
- dTAO upgrades to provide each subnet with its own Alpha token and AMM pool—market-determined emissions.
- Taoflow's upgrade (launching in November 2025) allocates emissions purely based on net staking flow. If a subnet unstakes more than it stakes, its emissions may drop to zero. The design is Darwinian.
The network now has over 128 active subnets, with the top three computing subnets reportedly achieving a combined $20 million in ARR within three months of monetization. Darwinism is the product.
Other projects focus on creating dedicated AI blockchains or providing the tools, frameworks, and incentives needed to support communities in building an AI ecosystem.
@NEARProtocol: An invisible coordination layer that combines settlement, identity, privacy, TEE, MPC, and PII protection to serve autonomous agents.
@base—the main base for "proxy economy". Base MCP allows AI tools such as Claude, ChatGPT, and Cursor to perform on-chain actions—exchanges, transfers, and DeFi interactions—on platforms such as Uniswap, Morpho, and Avantis through prompts.
@SentientAGI: Its GRID ecosystem connects agents, models, data, and computation, routing queries to expert actors to deliver the best results.
@gensynai: Verifiable ML execution, coordinating distributed hardware for training and inference while ensuring the work is trustworthy, $AI coordination network.
@SaharaAI connects data, models, agents, and rewards within a single, native AI ecosystem.
Infrastructure layer
Infrastructure is the skeleton of AI—the raw computation, inference, training, data, and privacy primitives upon which everything above depends. This is the most capital-intensive layer in the decentralized AI stack.
Decentralized computing
@akashnet runs a reverse auction market where providers bid to win your workload. New leases grew 27% in Q1 2026, reaching 43,500+, marking the third consecutive quarter of growth. Its AkashML inference service processed nearly 120 billion tokens in April, at a price 60–85% cheaper than mainstream cloud providers.
@rendernetwork reported a 428% year-over-year increase in usage.
@ionet aggregates 130,000+ GPUs from more than 130 countries on Solana.
@AethirCloud is one of the truly revenue-generating companies: it reports approximately $166 million in ARR (Q3 2025) and has delivered over 1.5 billion compute hours.
Distributed and verifiable reasoning
Inference accounts for more than 70% of AI operating costs, and Goldman Sachs predicts that proxy AI will drive a 24-fold increase in token consumption by 2030—120 trillion tokens per month.
Decentralized answers make reasoning cheap, private, and verifiable.
@AskVenice has provided over 50 billion tokens daily to more than 2 million users through its private and censorship-free models, and its moat is the model itself.
@OpenGradient has processed over 2 million verifiable inferences and generated over 500,000 zkML proofs.
@chutes_ai: Developers can deploy and scale AI models via a simple API, powered by GPU miners, at costs up to 85% cheaper than AWS. Platform revenue is converted into token demand through an automatic staking mechanism.
@dphnAI – A decentralized AI inference network. Notably, Dolphin developed the censorship-free model used by Venice AI and uses 100% of its network revenue for token buybacks.
Decentralized training
Training is the most difficult and impactful problem—it determines whether a cutting-edge model must be built within the labs of three or four companies.
@PrimeIntellect's INTELLECT-1 (10 billion parameters) was the first globally distributed training run; INTELLECT-2 (32 billion parameters) was the first distributed RL run.
@tplr_ai successfully trained Covenant-72B on 70+ distributed nodes, processing approximately 1.1 trillion tokens and reducing communication costs by 146 times.
@NousResearch: Its Psyche network enables fault-tolerant distributed training, and Hermes 4.3 is the first Hermes model trained on decentralized infrastructure rather than a centralized cluster.
@MacrocosmosAI's IOTA subnet (SN9) performs decentralized LLM pre-training and "at-home training," while its Data Universe subnet (SN13) handles the data layer. The DiLoCo family of low-communication algorithms enables globally distributed GPUs to collaborate without the need for ultra-fast internal networks in data centers.
Decentralized data availability and storage
As AI workloads scale up, both are becoming bottlenecks. Cutting-edge models consume massive amounts of fresh data, while storage demand has surged to the point that major hard drive suppliers are reporting that their capacity is sold out years in advance.
The economics are compelling. Decentralized storage can be 60-80% cheaper than traditional cloud providers, with networks like @Filecoin offering storage for less than $1 per TB per month, compared to around $30 for centralized alternatives.
@grass pays for idle bandwidth from 2.5 million nodes in 190 countries, allowing AI labs to capture real-time network activity.
@WalrusProtocol is a rapidly rising challenger built by @Mysten_Labs for decentralized storage and data availability—efficiently storing large “blobs” using two-dimensional erasure coding and increasingly being positioned as a persistent memory layer for AI agents.
@eigencloud: A verifiable cloud platform built around data availability, verifiable computation, and dispute resolution. Secured by restaking ETH, its theory is to enable AI agents to operate with cryptographic guarantees, making actions provable, auditable, and enforceable.
@vana – An EVM L1, Data DAOs, and Data Liquidity Pools that transform personal data into tokenizable, tradable assets.
@reppo and @oroagents build high-quality and trustworthy datasets for AI training through incentive competitions.
Privacy and Authentication Layer
Ordinary AI users cannot verify whether the model has privately processed their data, performed calculations correctly, or even used the claimed model.
By 2026, privacy and authentication will be prerequisites for AI, rather than additional features.
@nillion – a "blind computer" that uses MPC and its own Nil Message Compute to perform computations on encrypted data without decryption. Use cases include private AI inference, encrypted databases, and private RAGs (allowing AI to query proprietary knowledge bases without leaking them).
@Arcium: A decentralized confidential computing network on Solana. Use cases include Umbra (shielding transfers/private earnings) and confidential AI training on sensitive datasets.
@OasisProtocol: Privacy-first L1, using ROFL (Runtime Offchain Logic), a TEE-based framework for running verifiable, privacy-preserving off-chain computations—AI agents, model training, or oracles.
@octra: Natively supports privacy-first L1 with FHE, using the proprietary HFHE (Hypergraph FHE) scheme, designed for parallel cryptographic computation and throughput.
@eigencloud: Verify heavyweights, built on EigenLayer's restaking security. EigenAI (verifiable LLM inference is an OpenAI-compatible API for open-source models where hints and responses are provably untampered with) and EigenCompute (verifiable off-chain execution of proxy logic).
@PhalaNetwork. Cloud GPUs are powerful but not private; Phala makes workloads provable, even shielding them from Phala itself. Its core product, Phala Cloud's GPU TEE, deploys open-source models onto hardware, providing OpenAI-compatible APIs where every inference is cryptographically proven.
The future trend of decentralized AI in 2026-2027
AI demand is growing faster than infrastructure development, and AI agents are becoming the dominant growth engine—the on-chain track is ready.
Computation is transforming into an asset class, and on-chain markets are becoming its financial layer. Institutional participants are shifting from experimentation to infrastructure investment.
Token economics is becoming a structural advantage for decentralized AI in coordinating capital, computing power, and data. Opportunities are expanding from AI to robotics, autonomous machines, and physical AI.
in conclusion
Decentralized AI is growing across the main stack, including infrastructure, middleware, and applications, reflected in computing revenue, the growing agent economy, and large-scale distributed training.
However, this field is still in its early stages. Revenue often lags behind token incentives, adoption remains uneven, and while overall AI investment has surged, decentralized AI still represents only a small fraction of venture capital. Token-driven networks can be a powerful advantage, but only if the value capture design is correct.
Even so, the emergence of projects such as Bittensor, NEAR, Virtuals, Base, and Venice indicates that decentralized AI is evolving from a speculative narrative into a new paradigm that coordinates computing, data, capital, and intelligence.
