Author: Pink Brains
Compiled by: AididiaoJP, Foresight News
Decentralized AI exists because centralized AI has structural bottlenecks that cannot be solved by capital and code alone:
- Computing resources are scarce and expensive
- Excessive concentration of control
- Unverifiable model outputs
- Increasing difficulty in acquiring training data
Computing resources are scarce and expensive
GPU infrastructure is projected to grow from $100 billion in 2025 to $770 billion by 2035. Data center GPUs have been sold out for months. The decentralized computing market is expected to grow from $90 billion in 2024 to $220 billion by 2035 (Research and Markets data). This figure only holds if you believe the shortage is structural rather than cyclical, and we believe it is structural.
Excessive concentration of control
ChatGPT, Gemini, Grok, and Claude are owned and operated by a handful of private companies. Current AI policy assumes that only a few entities capable of centralizing massive computing resources can train powerful systems. Once this assumption is broken, the landscape of who can build frontier intelligence will fundamentally change.
Output results are unverifiable
When a model makes a decision, users cannot verify if the correct model was run, if the computation was correctly executed, or if sensitive data was leaked. This might be tolerable for chatbots, but it becomes completely unacceptable when AI handles loans, healthcare, or when autonomous agents operate real-time wallets.
Acquiring training data is increasingly difficult, due to privacy concerns and regulation
A centralized crawler located in a single AWS region will quickly be rate-limited, geo-blocked, or fed poisoned caches. As a16z stated in their 2026 outlook, privacy is becoming "crypto's most important moat."
AI needs blockchain to make intelligence open, verifiable, and economically accessible.
The Decentralized AI Tech Stack Map
- Application & Service Layer: AI agents can do many things, but in the crypto space, the two dominant use cases currently are Agentic Finance and Agentic Payments.
- Middleware Layer: The connective tissue—from frameworks for building and identifying agents, agent marketplaces, to coordination layers.
- Infrastructure Layer: The foundational resources for AI—the privacy & verification layer, computing, inference, training, data, and storage.
Application & Service Layer
Agentic Finance transforms natural language prompts into on-chain actions.
@gizatechxyz's ARMA agent has already processed over $4.6 billion in agent transaction volume across selected lending markets—running block by block on EigenLayer's AVS framework, non-custodial.
@Infinit_Labs runs a cluster of over 20 specialized agents that can translate intentions like "earn $1000 monthly with 1 BTC" into one-click strategies on Ethereum, Solana, and Base.
@coinvestai by Liquid embeds real-time execution directly into ChatGPT and Claude, supporting trading in 500+ markets via the Model Context Protocol.
@minara integrates Hyperliquid and recently joined Lighter. It runs a full "analysis → decision → execution" trading loop using its DMind model and 50+ integrations.
@Cod3xOrg: A network of lightweight AI agents that can translate intent into on-chain transactions for building and execution.
@Zyfai_: A self-custodial DeFAI agent that automates and optimizes yield farming, continuously rebalancing capital across protocols to chase risk-adjusted APY without human intervention.
In prediction markets, @SynthdataCo is a Bittensor subnet running a decentralized predictive financial intelligence network. Miners compete to model short-term price uncertainty. It's already providing real-time data for products like Mode AI Quant in Kalshi's crypto markets.
Agentic Payments: Machine-to-Machine Payments
Just as the internet became the communication layer for the digital economy, blockchain and stablecoins are becoming the settlement layer for agentic payments.
As of May 2026, x402 has processed over 173 million transactions on Base and Solana. 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 tied to real, pay-per-use workloads: API calls, AI inference services, agent commerce, and similar tasks. The initial hype cycle has cooled, but underlying traction is starting to catch up.
Meanwhile, Stripe and Tempo's Machine Payments Protocol is emerging as a second track, recording over 411.9k transactions and 9.6k buyers since launch.
Together, these networks signal a broader shift towards machine-to-machine commerce, where software agents can trade autonomously at machine speed.
Middleware Layer
As the number of agents increases, the core challenge becomes coordination: how agents discover each other, prove identity, and transact without human involvement.
The trust gap here is a bottleneck. The estimated size of agent commerce could reach $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 with identity and payments as native primitives. ERC-8004 is an Ethereum standard providing agents with portable on-chain identity and reputation that can follow them cross-chain.
In marketplaces, @virtuals_io is the operating system for the agent economy on Base. By 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 producing the most useful work. Three mechanisms make it economically serious:
- The December 2025 halving reduced daily TAO issuance from 7200 to 3600, aligning with a 21 million hard cap.
- The dTAO upgrade gives each subnet its own Alpha token and AMM pool—letting the market decide emissions.
- The Taoflow upgrade (launched November 2025) allocates emissions purely based on net stake flow. A subnet could drop to zero if it sees more unstaking than staking. It's Darwinian by design.
The network has surpassed 128 active subnets, with the top 3 compute subnets reportedly achieving a combined $20 million ARR within three months of monetization. Darwinism is the product.
Other projects focus on creating dedicated AI blockchains, or providing the tools, frameworks, and incentive mechanisms needed to support community-owned AI ecosystems.
@NEARProtocol: An invisible coordination layer combining settlement, identity, privacy, TEEs, MPC, and PII protection for autonomous agents.
@base—the main base for the "agent economy." The Base MCP allows AI tools like Claude, ChatGPT, and Cursor to execute on-chain actions via prompts on platforms like Uniswap, Morpho, Avantis—swapping, transferring, DeFi interactions.
@SentientAGI: Its GRID ecosystem connects agents, models, data, and compute, routing queries to specialized participants to deliver optimal results.
@gensynai: Verifiable ML execution, coordinating distributed hardware for training and inference while ensuring trustworthy work, with $AI coordinating the network.
@SaharaAI connects data, models, agents, and rewards within a single AI-native ecosystem.
Infrastructure Layer
Infrastructure is the skeleton of AI—the raw computing, inference, training, data, and privacy primitives that everything above depends on. This is the most capital-intensive layer of the decentralized AI stack.
Decentralized Computing
@akashnet runs a reverse auction marketplace where providers bid to win your workload. New leases grew 27% YoY in Q1 2026 to over 43.5k, marking the third consecutive quarter of growth. Its AkashML inference service processed nearly 120 billion tokens in April, priced 60–85% cheaper than mainstream clouds.
@rendernetwork reported a 428% YoY increase in usage growth.
@ionet has aggregated over 130,000 GPUs from more than 130 countries on Solana.
@AethirCloud is one of the few truly generating revenue: self-reporting ~$166 million ARR (Q3 2025) and delivering over 1.5 billion compute hours.
Distributed & Verifiable Inference
Inference accounts for over 70% of AI operational costs, and Goldman Sachs predicts agent AI will drive a 24x growth in token consumption by 2030—to 120 trillion tokens per month.
The decentralized answer is to make inference cheap, private, and verifiable.
@AskVenice already serves over 2 million users with more than 50 billion tokens daily through private and uncensored models. Its moat is the models.
@OpenGradient has processed over 2 million verifiable inferences, generating 500k+ zkML proofs.
@chutes_ai: Developers can deploy and scale AI models via a simple API, powered by GPU miners, with costs up to 85% cheaper than AWS. Platform revenue is converted into token demand through an auto-staking mechanism.
@dphnAI—a decentralized AI inference network. Notably, Dolphin developed the uncensored models used by Venice AI and uses 100% of network revenue for token buybacks.
Decentralized Training
Training is the hardest problem and the highest-impact one—it determines whether frontier models must be built inside three or four corporate labs.
@PrimeIntellect's INTELLECT-1 (10B parameters) was the first globally distributed training run; INTELLECT-2 (32B parameters) was the first distributed RL run.
@tplr_ai successfully trained Covenant-72B on 70+ distributed nodes, processing ~1.1 trillion tokens, reducing communication costs by 146x.
@NousResearch: Its Psyche network enables fault-tolerant distributed training, and Hermes 4.3 became the first Hermes model trained on decentralized infrastructure rather than a centralized cluster.
@MacrocosmosAI's IOTA subnet (SN9) conducts decentralized LLM pre-training and "train-at-home," while its Data Universe subnet (SN13) handles the data layer. The DiLoCo series of low-communication algorithms allows GPUs scattered globally to collaborate without the ultra-fast internal networks of data centers.
Decentralized Data Availability & Storage
Both are becoming bottlenecks as AI workloads scale. Frontier models consume vast amounts of fresh data, and storage demand has surged to the point where major HDD suppliers report capacity sold out years in advance.
The economics are attractive. Decentralized storage can be 60-80% cheaper than traditional cloud providers. Networks like @Filecoin offer storage for under $1 per TB per month, compared to around $30 for centralized alternatives.
@grass pays 2.5 million nodes across 190 countries for their idle bandwidth, allowing AI labs to scrape the live web.
@WalrusProtocol is a fast-rising challenger built by @Mysten_Labs for decentralized storage and data availability—using 2D erasure coding to store large "blobs" efficiently and increasingly 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 restaked ETH, its thesis is to enable AI agents to run with cryptographic guarantees, making actions provable, auditable, and enforceable.
@vana—an EVM L1 where Data DAOs and Data Liquidity Pools turn personal data into tokenizable, tradable assets.
@reppo and @oroagents build high-quality, trustworthy datasets for AI training through incentivized competitions.
Privacy & Verification Layer
The average AI user cannot verify if a model processed their data privately, executed computations correctly, or even used the claimed model.
In 2026, privacy and verification are becoming prerequisites for AI, not add-ons.
@nillion—the "blind computer," using MPC and its own Nil Message Compute to perform computations on encrypted data without decrypting it. Use cases include private AI inference, encrypted databases, and private RAG (enabling AI to query proprietary knowledge bases without leakage).
@Arcium: A decentralized confidential compute network on Solana. Use cases include Umbra (shielded transfers/private yield) and confidential AI training on sensitive datasets.
@OasisProtocol: A privacy-first L1 using ROFL (Runtime Offchain Logic), a TEE-based framework for running verifiable, privacy-preserving off-chain computations—for AI agents, model training, or oracles.
@octra: A privacy-first L1 natively supporting FHE, using its proprietary scheme HFHE (Hypergraph FHE), designed for parallel encrypted computation and throughput.
@eigencloud: A heavy hitter in verification, built on the restaked security of EigenLayer. EigenAI (verifiable LLM inference is an OpenAI-compatible API for open-source models where prompts and responses are provably untampered) and EigenCompute (verifiable off-chain execution for agent logic).
@PhalaNetwork. Cloud GPUs are powerful but not private; Phala makes workloads provable, even shielded from Phala itself. Its core product, GPU TEE on Phala Cloud, deploys open-source models to hardware, providing an OpenAI-compatible API where each inference has cryptographic proof.
Where Decentralized AI is Headed in 2026-2027
AI demand is growing faster than infrastructure can keep up, and AI agents are becoming the dominant growth engine—the on-chain track is ready.
Computing is transforming into an asset class, and on-chain markets are becoming its financial layer. Institutional players are moving from experimentation to infrastructure investment.
Tokenomics is becoming a structural advantage for decentralized AI in coordinating capital, compute, and data. Opportunities are expanding from AI to robotics, autonomous machines, and physical AI.
Conclusion
Decentralized AI is growing across the major stacks—infrastructure, middleware, and applications—evidenced by compute revenue, a growing agent economy, and large-scale distributed training.
But the field remains early. Revenue often lags token incentives, adoption is still uneven, and while overall AI investment is surging, decentralized AI remains a small fraction of venture capital. Token-driven networks can be a powerful advantage, but only if value capture is designed correctly.
Even so, the emergence of projects like Bittensor, NEAR, Virtuals, Base, and Venice indicates that decentralized AI is evolving from a speculative narrative to a new model for coordinating compute, data, capital, and intelligence.
