Author: Pink Brains
Compiled by: AididiaoJP, Foresight News
Decentralized AI exists because centralized AI faces structural bottlenecks that cannot be solved by capital and code alone:
- Computing resources are scarce and expensive
- Control is overly concentrated
- Model outputs are not verifiable
- Training data acquisition is becoming increasingly difficult
Computing resources are scarce and expensive
GPU infrastructure is projected to grow from $10 billion in 2025 to $77 billion by 2035. Data center GPUs have been sold out for months. The decentralized computing market is expected to grow from $9 billion in 2024 to $22 billion by 2035 (Research and Markets data). This figure only holds true if you believe the shortage is structural rather than cyclical; we believe it is structural.
Control is overly concentrated
ChatGPT, Gemini, Grok, and Claude are all owned and operated by a handful of private companies. Current AI policy assumes that only a few entities capable of concentrating massive computing resources can train powerful systems. Once this assumption is broken, the landscape of who can build frontier intelligence will be completely altered.
Outputs are not verifiable
When a model makes a decision, users cannot verify whether the correct model was run, whether computations were executed correctly, or whether sensitive data was leaked. This might be tolerable for chatbots, but it's completely unacceptable when AI handles loans, healthcare, or autonomous agents operating real-time wallets.
Training data acquisition is becoming increasingly difficult due to privacy concerns and regulation
A centralized crawler located in a single AWS region will soon be rate-limited, geo-blocked, or fed poisoned caches. As a16z stated in its 2026 outlook, privacy is becoming "the most important moat in crypto."
AI needs blockchain to make intelligence open, verifiable, and economically accessible.
Decentralized AI Tech Stack Map
- Application & Services 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 connecting tissue—frameworks for building and identifying agents, agent marketplaces, to coordination layers.
- Infrastructure Layer: AI's underlying resources—privacy & verification, computation, inference, training, data, and storage.
Application & Services Layer
Agentic Finance converts natural language prompts into on-chain actions.
@gizatechxyz's ARMA agent has already processed over $4.6 billion in agent volume across select 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 intents like "earn $1000/month 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 across 500+ markets via the Model Context Protocol.
@minara integrates Hyperliquid and recently joined Lighter. It runs a full "analyze → decide → execute" trading loop via the DMind model and 50+ integrations.
@Cod3xOrg: A network of lightweight AI agents that translate intent into on-chain trades that are built and executed.
@Zyfai_: A self-custodial DeFAI agent that automates and optimizes yield farming, continuously rebalancing capital across protocols to chase risk-adjusted APY without manual 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 Kalshi's crypto market and Mode AI Quant.
Agentic Payments: Machine Pays Machine
Just as the internet became the communication layer for the digital economy, blockchain and stablecoins are becoming the settlement layer for agent 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 started using it in February 2026; AWS launched its native AgentCore Payments.
Buyer and seller activity is increasing, with most transactions tied to real pay-per-use: API calls, AI inference services, agent commerce, and similar workloads. 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 puzzle becomes coordination: how agents discover each other, prove identity, and transact without human involvement.
The trust gap here is the bottleneck. The estimated size of agent commerce is projected to reach $1.5 to $5 trillion by 2030, but adoption is limited by one point—most users are willing to let AI do research, but few are willing to let AI actually buy things.
Today's systems still rely on API keys, with almost no system treating agents as entities with identity.
@GoKiteAI is building a dedicated L1 with identity and payments as native primitives. ERC-8004 is an Ethereum standard providing portable on-chain identity and reputation for agents, which can follow them cross-chain.
In terms of 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 crown jewel of this layer is Bittensor. It is 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, corresponding to a 21 million hard cap.
- The dTAO upgrade gives each subnet its own Alpha token and AMM pool—the market decides emissions.
- The Taoflow upgrade (launched November 2025) allocates emissions purely based on net stake flow. A subnet can drop to zero if it unstakes more than it stakes. 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 incentives needed to support community-owned AI ecosystems.
@NEARProtocol: An invisible coordination layer combining settlement, identity, privacy, TEE, MPC, and PII protection for autonomous agents.
@base—the main base for the "agent economy." 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 computation, routing queries to specialized actors to provide the best results.
@gensynai: Verifiable ML execution, coordinating distributed hardware for training and inference while ensuring work is trustworthy, 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 computation, inference, training, data, and privacy primitives that everything else depends on. This is the most capital-intensive layer of the decentralized AI stack.
Decentralized Computation
@akashnet runs a reverse auction market where providers bid to win your workloads. Q1 2026 saw a 27% growth in new leases, reaching 43,500+, 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 428% YoY growth in usage.
@ionet has aggregated over 130,000 GPUs from 130+ countries on Solana.
@AethirCloud is one of the few with real revenue: self-reporting ~$166 million ARR (Q3 2025), having delivered over 1.5 billion compute hours.
Distributed & Verifiable Inference
Inference accounts for over 70% of AI operating costs. Goldman Sachs expects agent AI to drive a 24x increase 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 via private and uncensored models, with its moat being 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, backed by GPU miners, at costs up to 85% cheaper than AWS. Platform revenue is converted into token demand via an auto-staking mechanism.
@dphnAI—a decentralized AI inference network. Notably, Dolphin developed the uncensored models used by Venice AI and directs 100% of network revenue to token buybacks.
Decentralized Training
Training is the hardest problem and the most impactful—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) does decentralized LLM pre-training and "training-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 a data center's ultra-fast internal network.
Decentralized Data Availability & Storage
Both are becoming bottlenecks as AI workloads scale. Frontier models consume massive amounts of fresh data, and storage demand has surged to the point where major hard drive 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 ~$30 for centralized alternatives.
@grass pays 2.5 million nodes from 190 countries for their idle bandwidth, allowing AI labs to scrape the live web.
@WalrusProtocol, built by @Mysten_Labs, is a fast-rising challenger for decentralized storage and data availability—using 2D erasure coding to efficiently store large "blobs" 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, tradeable assets.
@reppo and @oroagents build high-quality, trustworthy datasets for AI training via incentivized competitions.
Privacy & Verification Layer
The average AI user cannot verify if their data was processed privately, if computations were executed correctly, or even if the claimed model was used.
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 revealing them).
@Arcium: A decentralized confidential computing 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 a proprietary scheme HFHE (Hypergraph FHE), designed for parallel encrypted computation and throughput.
@eigencloud: The heavyweight for verification, built on EigenLayer's restaked security. EigenAI (Verifiable LLM Inference is an OpenAI-compatible API for open-source models where prompts and responses are provably unaltered) 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 TEEs on Phala Cloud, deploys open-source models onto hardware, providing an OpenAI-compatible API where each inference comes with cryptographic proof.
Where Decentralized AI is Heading 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.
Computation 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, computation, 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, applications—evidenced by computational revenue, a growing agent economy, and large-scale distributed training.
But the field is still early. Revenue often lags behind token incentives, adoption remains uneven, and while overall AI investment is surging, decentralized AI still represents only a fraction of venture funding. Token-driven networks can be a powerful advantage, but only if value capture is designed correctly.
Nevertheless, the emergence of projects like Bittensor, NEAR, Virtuals, Base, and Venice indicates that decentralized AI is evolving from a speculative narrative into a new paradigm for coordinating computation, data, capital, and intelligence.
